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Ji X, Sun W, Lv C, Huang J, Yu R, Dong W, Zhang H. Survival trends and conditional survival in patients with pulmonary metastases from differentiated thyroid carcinoma. Endocrine 2025; 87:1120-1130. [PMID: 39589684 DOI: 10.1007/s12020-024-04109-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 11/09/2024] [Indexed: 11/27/2024]
Abstract
INTRODUCTION Patients with pulmonary metastases from differentiated thyroid carcinoma (DTC) have a significantly poorer prognosis compared to DTC patients without metastases. This study aimed to establish a nomogram combined with dynamic survival analysis to predict the survival probability of patients with pulmonary metastases from differentiated thyroid carcinoma and compensate for the underestimation of survival in patients with very poor prognosis. PATIENTS AND METHODS Patient data were collected from the Surveillance, Epidemiological, and End Result (SEER) data resource from 2010 to 2019. Multivariate analysis was carried out by the Cox proportional hazards regression to construct a nomogram. Receiver operating characteristic (ROC) curves along with calibration were employed to assess the effectiveness of the model.The life table was used to estimate the conditional cancer-specific survival (CSS). RESULTS In the training set, the AUCs for the CSS nomogram were 0.728, 0.741, and 0.779, with a c-index of 0.682, indicating good predictive performance at 3, 5, and 10 years. In the validation set, the AUCs for the CSS nomogram were 0.706, 0.726, and 0.769, with a c-index of 0.696, while the AUCs for the 8th TNM staging system were 0.521, 0.555, and 0.601, with a c-index of 0.579. The overall 5-year conditional survival rate for patients increased slightly from 63.44 to 70.52%. The survival gap was greatest between patients aged <55 years and those aged ≥55 years. CONCLUSION We established a nomogram combined with dynamic survival analysis, which serve as promising options for prognosis estimation, to enhance personalized evaluation of survival risks and provide the basis for the development of more clinical treatment approaches.
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Affiliation(s)
- Xiaoyu Ji
- Department of Thyroid Surgery, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, Liaoning, 110001, China
| | - Wei Sun
- Department of Thyroid Surgery, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, Liaoning, 110001, China
| | - Chengzhou Lv
- Department of Thyroid Surgery, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, Liaoning, 110001, China
| | - Jiapeng Huang
- Department of Thyroid Surgery, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, Liaoning, 110001, China
| | - Ruonan Yu
- Department of Thyroid Surgery, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, Liaoning, 110001, China
| | - Wenwu Dong
- Department of Thyroid Surgery, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, Liaoning, 110001, China
| | - Hao Zhang
- Department of Thyroid Surgery, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, Liaoning, 110001, China.
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Huang F, Wei R, Mei S, Xiao T, Zhao W, Zheng Z, Liu Q. Clinical calculator based on clinicopathological characteristics predicts local recurrence and overall survival following radical resection of stage II-III colorectal cancer. Front Oncol 2025; 15:1494255. [PMID: 39975593 PMCID: PMC11835698 DOI: 10.3389/fonc.2025.1494255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2024] [Accepted: 01/13/2025] [Indexed: 02/21/2025] Open
Abstract
PURPOSE This study aimed to analyze the risk factors and survival prognosis of local recurrence in stage II-III colorectal cancer (CRC) and develop a clinical risk calculator and nomograms to predict local recurrence and survival in treated patients. METHODS Patients who underwent radical surgery between January 2009 and December 2019 at the China National Cancer Center were included. Multivariate nomograms and a clinical risk calculator based on Cox regression were developed. Discrimination was measured with an area under curve (AUC) and variability in individual predictions was assessed with calibration curves. We stratified patients into different risk groups according to the established model to predict their prognosis and guide clinical practice. RESULTS The clinical risk calculator incorporated six variables: tumor thrombus, perineural invasion, tumor grade, pathology T-stage, pathology N-stage, and whether more than 12 lymph nodes were harvested. Our clinical risk calculator provided good discrimination, with AUC values of local recurrence-free survival (LRFS) (0.764) and overall survival (OS) (0.815) in the training cohort and LRFS (0.740) and OS (0.730) in the test cohort. Calibration plots illustrated excellent agreement between the clinical risk calculator predictions and actual observations for 3- and 5-year LRFS and OS. Recurrence risk-stratified analysis showed that low-risk patients were more likely to undergo salvage radical surgery when recurrent disease existed. CONCLUSION The clinical calculator can better account for tumor and patient heterogeneity, providing a more individualized outcome prognostication. The model is expected to aid in treatment planning, such as resectability evaluation, and it can be used in postoperative surveillance (https://oldcoloncancer.shinyapps.io/dynnomapp/).
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Affiliation(s)
- Fei Huang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ran Wei
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shiwen Mei
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tixian Xiao
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wei Zhao
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhaoxu Zheng
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qian Liu
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Wu T, Fang L, Ruan Y, Shi M, Su D, Ma Y, Ma M, Wang B, Liao Y, Han S, Lu X, Zhang C, Liu C, Zhang Y. Tumor aggression-defense index-a novel indicator to predicts recurrence and survival in stage II-III colorectal cancer. J Transl Med 2025; 23:107. [PMID: 39844178 PMCID: PMC11755833 DOI: 10.1186/s12967-025-06141-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2024] [Accepted: 01/13/2025] [Indexed: 01/24/2025] Open
Abstract
BACKGROUND Although the TNM staging system plays a critical role in guiding adjuvant chemotherapy for colorectal cancer (CRC), its precision for risk stratification in stage II and III CRC patients with proficient DNA mismatch repair (pMMR) remains limited. Therefore, precise predictive models and research on postoperative treatments are crucial for enhancing patient survival and improving quality of life. METHODS This retrospective study analyzed 1051 pMMR CRC patients who underwent radical resection and were randomly assigned to training (n = 736) and validation (n = 315) groups. Immunohistochemistry and hematoxylin and eosin staining were utilized to evaluate regulatory-Immunoscore (RIS), tertiary lymphoid structures (TLS), and tumor budding (TB). The Tumor Aggression-Defense Index (TADI) was derived through a multi-factor COX regression model. Subgroup analysis demonstrated potential of TADI in guiding personalized adjuvant therapy for stage II and III CRC. RESULTS Univariate and multivariate Cox analysis indicated that TADI was an independent prognostic indicator. Among stage II CRC, chemotherapy was significantly correlated with improved recurrence times in individuals with intermediate (95% CI 0.19-0.59, P < 0.001) and high (95% CI 0.36-0.95, P = 0.031) TADI. In stage III CRC receiving adjuvant chemotherapy, a duration of 3 months or longer was notably associated with a prolonged time to recurrence in those with high TADI (95% CI 0.40-0.98, P = 0.041) compared to durations of less than 3 months. CONCLUSION The TADI serves as an effective parameter for predicting the survival outcomes of stage I-III pMMR CRC patients and guiding precision treatment strategies.
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Affiliation(s)
- Tong Wu
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China
- Clinical Research Center for Colorectal Cancer in Heilongjiang, Harbin, China
- Key Laboratory of Tumor Immunology in Heilongjiang, Harbin, China
| | - Lin Fang
- Phase I Clinical Research Center, The Affiliated Hospital of Qingdao University in Shandong, Qingdao, China
| | - Yuli Ruan
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China
- Clinical Research Center for Colorectal Cancer in Heilongjiang, Harbin, China
| | - Mengde Shi
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China
- Clinical Research Center for Colorectal Cancer in Heilongjiang, Harbin, China
| | - Dan Su
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China
- Key Laboratory of Tumor Immunology in Heilongjiang, Harbin, China
| | - Yue Ma
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China
- Key Laboratory of Tumor Immunology in Heilongjiang, Harbin, China
| | - Ming Ma
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China
- Key Laboratory of Tumor Immunology in Heilongjiang, Harbin, China
| | - Bojun Wang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China
- Clinical Research Center for Colorectal Cancer in Heilongjiang, Harbin, China
| | - Yuanyu Liao
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China
- Key Laboratory of Tumor Immunology in Heilongjiang, Harbin, China
| | - Shuling Han
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China
- Clinical Research Center for Colorectal Cancer in Heilongjiang, Harbin, China
| | - Xiaolin Lu
- Department of Orthopedic Surgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Chunhui Zhang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China.
| | - Chao Liu
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China.
- Clinical Research Center for Colorectal Cancer in Heilongjiang, Harbin, China.
- Key Laboratory of Tumor Immunology in Heilongjiang, Harbin, China.
| | - Yanqiao Zhang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150001, People's Republic of China.
- Clinical Research Center for Colorectal Cancer in Heilongjiang, Harbin, China.
- Key Laboratory of Tumor Immunology in Heilongjiang, Harbin, China.
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Wang L, Xian X, Liu M, Li J, Shu Q, Guo S, Xu K, Cao S, Zhang W, Zhao W, Ye M. Predicting the decline of physical function among the older adults in China: A cohort study based on China longitudinal health and longevity survey (CLHLS). Geriatr Nurs 2025; 61:378-389. [PMID: 39612589 DOI: 10.1016/j.gerinurse.2024.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 10/29/2024] [Accepted: 11/04/2024] [Indexed: 12/01/2024]
Abstract
BACKGROUND As the arrival of healthy aging, maintaining physical function (PF) in older adults is crucial for their health, so it is necessary to detect the decline of PF among them and take intervention measures. METHODS We construct eight machine learning models to predict declines of PF in this study. The performance of the models was tested by Area Under Curve (AUC), sensitivity, specificity, accuracy, precision-recall (PR) curve and calibration degree. Decision Curve Analysis (DCA) curve was used to evaluate their discrimination ability and clinical practicability. RESULTS There were 2,017 participants in this study. We found that logistic regression models performed the best, with AUC, sensitivity, specificity and accuracy of 0.803, 0.698, 0.761 and 0.744 respectively, and its DCA curve, calibration degree and PR curve also performed well. CONCLUSION Logistic regression can be used as optimal model to identify the risk of PF decline among older adults in China.
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Affiliation(s)
- Liang Wang
- School of Public Health, Chongqing Medical University, Chongqing, PR China
| | - Xiaobing Xian
- Chongqing Geriatrics Hospital, Chongqing, PR China; The Thirteenth People's Hospital of Chongqing, Chongqing, PR China
| | - Meiling Liu
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, PR China
| | - Jie Li
- Academy of Mathematical Sciences, Chongqing Normal University, Chongqing, PR China
| | - Qi Shu
- The First Clinical College, Chongqing Medical University, Chongqing, PR China
| | - Siyi Guo
- The First Clinical College, Chongqing Medical University, Chongqing, PR China
| | - Ke Xu
- School of Public Health, Chongqing Medical University, Chongqing, PR China
| | - Shiwei Cao
- The Second Clinical College, Chongqing Medical University, Chongqing, PR China
| | - Wenjia Zhang
- School of Public Health, Chongqing Medical University, Chongqing, PR China
| | - Wenyan Zhao
- Stomatological Hospital of Chongqing Medical University, Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, PR China
| | - Mengliang Ye
- School of Public Health, Chongqing Medical University, Chongqing, PR China.
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Sun Y, Huang L, Shen X, Yang Z, Xu B, Bao C, Shi Y. Development and validation of a dynamic nomogram for individualized prediction of survival in patients with colon cancer. Sci Rep 2024; 14:28033. [PMID: 39543274 PMCID: PMC11564546 DOI: 10.1038/s41598-024-78783-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 11/04/2024] [Indexed: 11/17/2024] Open
Abstract
Current tools for predicting survival outcomes in colon cancer patients predominantly rely on clinical and pathologic characteristics. However, accumulating evidence demonstrates a significant correlation between nutritional status and patient outcomes. This study aimed to establish a new dynamic nomogram for individualized prediction of postoperative overall survival (OS). The clinicopathological and nutritional data of colon cancer patients undergoing radical resection were retrospectively collected and randomly divided into the primary and validation cohorts. Risk factors on OS rates were investigated by Cox analyses and, the nomogram was constructed using significant predictors. Among 1,024 patients, 341 deaths were observed after median follow-up of 54 months. Five independent prognostic factors, including nutritional status assessments, were incorporated into the nomogram. The C-index regarding 1-, 3-, and 5-year OS were 0.830, 0.859, and 0.757 in the primary cohort and 0.843, 0.870, and 0.773 in the validation cohort, respectively. Calibration curves for the probability of OS exhibited an optimal agreement. Decision curve analyses revealed the greater application value of the nomogram than the TNM staging system. Based on the nomogram, patients could be stratified into three scenarios with significant prognostic classification (P < 0.0001). In conclusion, we developed and validated an easy-to-use dynamic nomogram for predicting postoperative OS in colon cancer patients.
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Affiliation(s)
- Yuting Sun
- Department of Infectious Diseases, Jiangnan University MedicalCenter, 68 Zhongshan Road, Wuxi, 214000, Jiangsu, China
| | - Longchang Huang
- Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, 214000, Jiangsu, China
| | - Xiaoming Shen
- Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, 214000, Jiangsu, China
| | - Zenghui Yang
- Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, 214000, Jiangsu, China
| | - Binghua Xu
- Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, 214000, Jiangsu, China
| | - Chuanqing Bao
- Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, 214000, Jiangsu, China.
| | - Yifan Shi
- Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, 1000 Hefeng Road, Wuxi, 214000, Jiangsu, China.
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Alcala S, Serralta San Martin G, Muñoz-Fernández de Legaria M, Moreno-Rubio J, Salinas S, López-Gil JC, Rojo López JA, Martínez Alegre J, Cortes Bandy DA, Zambrana F, Jiménez-Gordo AM, Casado E, López-Gómez M, Sainz B. Autofluorescent Cancer Stem Cells: Potential Biomarker to Predict Recurrence in Resected Colorectal Tumors. CANCER RESEARCH COMMUNICATIONS 2024; 4:2575-2588. [PMID: 39225547 PMCID: PMC11445700 DOI: 10.1158/2767-9764.crc-24-0188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/27/2024] [Accepted: 08/29/2024] [Indexed: 09/04/2024]
Abstract
Cancer stem cells (CSC) in colorectal cancer drive intratumoral heterogeneity and distant metastases. Previous research from our group showed that CSCs can be easily detected by autofluorescence (AF). The aim of the present study was to evaluate the potential role of AF CSCs as a prognostic biomarker for colorectal cancer relapse. Seventy-five freshly resected tumors were analyzed by flow cytometry. AF was categorized as high (H-AF) or low, and the results were correlated with histologic features [grade of differentiation, presence of metastases in lymph nodes (LN), perivascular and lymphovascular invasion] and clinical variables (time to relapse and overall survival). Nineteen of the 75 (25.3%) patients experienced relapse (local or distant); of these 19 patients, 13 showed positive LNs and 6 had H-AF. Of note, four of them died before 5 years. Although patients with H-AF CSC percentages in the global population experienced 1.5 times increased relapse [HR, 1.47; 95% confidence interval (0.60-3.63)], patients with H-AF CSC percentages and LN metastases had the highest risk of relapse [HR, 7.92; P < 0.004; 95% confidence interval (1.97-31.82)]. These data support AF as an accurate and feasible marker to identify CSCs in resected colorectal cancer. A strong statistical association between H-AF CSCs and the risk of relapse was observed, particularly in patients with positive LNs, suggesting that H-AF patients might benefit from adjuvant chemotherapy regimens and intensive surveillance due to their high propensity to experience disease recurrence. Significance: AF has been proven to be an accurate biomarker for CSC identification; however, to date, their role as a prognostic factor after resection of colorectal cancer tumors has not been investigated. Our results show that determining the presence of AF CSCs after tumor resection has prognostic value and represents a potentially important tool for the management of patients with colorectal cancer.
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Affiliation(s)
- Sonia Alcala
- Department of Biochemistry, School of Medicine, Autónoma University of Madrid and Department of Cancer, Instituto de Investigaciones Biomédicas (IIBm) Sols-Morreale (CSIC-UAM), Madrid, Spain.
- Biomarkers and Personalized Approach to Cancer (BIOPAC) Group, Area 3 Cancer, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.
| | - Gonzalo Serralta San Martin
- Department of Internal Medicine, Infanta Sofía University Hospital, FIIB HUIS HHEN, Madrid, Spain.
- Universidad Europea de Madrid, Madrid, Spain.
| | | | - Juan Moreno-Rubio
- Department of Medical Oncology, Infanta Sofía University Hospital, FIIB HUIS HHEN, Madrid, Spain.
- Precision Nutrition and Cancer Program, Clinical Oncology Group, IMDEA Food Institute, CEI UAM-CSIC, Madrid, Spain.
| | - Silvia Salinas
- Department of Pathology, Infanta Sofía University Hospital, FIIB HUIS HHEN, Madrid, Spain.
| | - Juan Carlos López-Gil
- Department of Biochemistry, School of Medicine, Autónoma University of Madrid and Department of Cancer, Instituto de Investigaciones Biomédicas (IIBm) Sols-Morreale (CSIC-UAM), Madrid, Spain.
- Biomarkers and Personalized Approach to Cancer (BIOPAC) Group, Area 3 Cancer, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.
| | - José Alberto Rojo López
- Department of General Surgery, Infanta Sofía University Hospital, FIIB HUIS HHEN, Madrid, Spain.
| | - Javier Martínez Alegre
- Universidad Europea de Madrid, Madrid, Spain.
- Department of General Surgery, Infanta Sofía University Hospital, FIIB HUIS HHEN, Madrid, Spain.
| | | | - Francisco Zambrana
- Universidad Europea de Madrid, Madrid, Spain.
- Department of Medical Oncology, Infanta Sofía University Hospital, FIIB HUIS HHEN, Madrid, Spain.
- Precision Nutrition and Cancer Program, Clinical Oncology Group, IMDEA Food Institute, CEI UAM-CSIC, Madrid, Spain.
| | - Ana-María Jiménez-Gordo
- Universidad Europea de Madrid, Madrid, Spain.
- Department of Medical Oncology, Infanta Sofía University Hospital, FIIB HUIS HHEN, Madrid, Spain.
- Precision Nutrition and Cancer Program, Clinical Oncology Group, IMDEA Food Institute, CEI UAM-CSIC, Madrid, Spain.
| | - Enrique Casado
- Universidad Europea de Madrid, Madrid, Spain.
- Department of Medical Oncology, Infanta Sofía University Hospital, FIIB HUIS HHEN, Madrid, Spain.
- Precision Nutrition and Cancer Program, Clinical Oncology Group, IMDEA Food Institute, CEI UAM-CSIC, Madrid, Spain.
| | - Miriam López-Gómez
- Universidad Europea de Madrid, Madrid, Spain.
- Department of Medical Oncology, Infanta Sofía University Hospital, FIIB HUIS HHEN, Madrid, Spain.
| | - Bruno Sainz
- Department of Biochemistry, School of Medicine, Autónoma University of Madrid and Department of Cancer, Instituto de Investigaciones Biomédicas (IIBm) Sols-Morreale (CSIC-UAM), Madrid, Spain.
- Biomarkers and Personalized Approach to Cancer (BIOPAC) Group, Area 3 Cancer, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS), Madrid, Spain.
- Centro de Investigación Biomédica en Red, Área Cáncer, CIBERONC, ISCIII, Madrid, Spain.
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Atienza ÁV, Iriarte OA, Sarrias OR, Lizundia TZ, Beristain OS, Casajús AE, Gigli LÁ, Sastre FR, García IM, Rodríguez JR. Neoadjuvant Statistical Algorithm to Predict Individual Risk of Relapse in Patients with Resected Liver Metastases from Colorectal Cancer. Biomedicines 2024; 12:1859. [PMID: 39200323 PMCID: PMC11351994 DOI: 10.3390/biomedicines12081859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2024] [Revised: 08/10/2024] [Accepted: 08/12/2024] [Indexed: 09/02/2024] Open
Abstract
(1) Background: Liver metastases (LM) are the leading cause of death in colorectal cancer (CRC) patients. Despite advancements, relapse rates remain high and current prognostic nomograms lack accuracy. Our objective is to develop an interpretable neoadjuvant algorithm based on mathematical models to accurately predict individual risk, ensuring mathematical transparency and auditability. (2) Methods: We retrospectively evaluated 86 CRC patients with LM treated with neoadjuvant systemic therapy followed by complete surgical resection. A comprehensive analysis of 155 individual patient variables was performed. Logistic regression (LR) was utilized to develop the predictive model for relapse risk through significance testing and ANOVA analysis. Due to data limitations, gradient boosting machine (GBM) and synthetic data were also used. (3) Results: The model was based on data from 74 patients (12 were excluded). After a median follow-up of 58 months, 5-year relapse-free survival (RFS) rate was 33% and 5-year overall survival (OS) rate was 60.7%. Fifteen key variables were used to train the GBM model, which showed promising accuracy (0.82), sensitivity (0.59), and specificity (0.96) in predicting relapse. Similar results were obtained when external validation was performed as well. (4) Conclusions: This model offers an alternative for predicting individual relapse risk, aiding in personalized adjuvant therapy and follow-up strategies.
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Affiliation(s)
- Ángel Vizcay Atienza
- Department of Medical Oncology, Clínica Universidad de Navarra, 31008 Pamplona, Spain; (Á.V.A.); (T.Z.L.)
| | | | - Oskitz Ruiz Sarrias
- Department of Mathematics and Statistic, NNBi, 31110 Noain, Spain; (O.A.I.); (O.R.S.); (O.S.B.)
| | - Teresa Zumárraga Lizundia
- Department of Medical Oncology, Clínica Universidad de Navarra, 31008 Pamplona, Spain; (Á.V.A.); (T.Z.L.)
| | - Onintza Sayar Beristain
- Department of Mathematics and Statistic, NNBi, 31110 Noain, Spain; (O.A.I.); (O.R.S.); (O.S.B.)
| | - Ana Ezponda Casajús
- Department of Radiology, Clínica Universidad de Navarra, 31008 Pamplona, Spain;
| | - Laura Álvarez Gigli
- Department of Pathology, Clínica Universidad de Navarra, 31008 Pamplona, Spain;
| | | | - Ignacio Matos García
- Department of Medical Oncology, Clínica Universidad de Navarra, 28027 Madrid, Spain;
| | - Javier Rodríguez Rodríguez
- Department of Medical Oncology, Clínica Universidad de Navarra, 31008 Pamplona, Spain; (Á.V.A.); (T.Z.L.)
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Bektas AB, Hakki L, Khan A, Widmar M, Wei IH, Pappou E, Smith JJ, Nash GM, Paty PB, Garcia-Aguilar J, Cercek A, Stadler Z, Segal NH, Shia J, Gonen M, Weiser MR. Clinical Calculator for Predicting Freedom From Recurrence After Resection of Stage I-III Colon Cancer in Patients With Microsatellite Instability. JCO Clin Cancer Inform 2024; 8:e2300233. [PMID: 39121392 PMCID: PMC11323037 DOI: 10.1200/cci.23.00233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 06/03/2024] [Accepted: 06/21/2024] [Indexed: 08/11/2024] Open
Abstract
PURPOSE Outcome for patients with nonmetastatic, microsatellite instability (MSI) colon cancer is favorable: however, high-risk cohorts exist. This study was aimed at developing and validating a nomogram model to predict freedom from recurrence (FFR) for patients with resected MSI colon cancer. PATIENTS AND METHODS Data from patients who underwent curative resection of stage I, II, or III MSI colon cancer in 2014-2021 (model training cohort, 384 patients, 33 events; median follow-up, 38.8 months) were retrospectively collected from institutional databases. Variables associated with recurrence in multivariable analysis were selected for inclusion in the clinical calculator. The calculator's predictive accuracy was measured with the concordance index and validated using data from patients who underwent treatment for MSI colon cancer in 2007-2013 (validation cohort, 164 patients, eight events; median follow-up, 84.8 months). RESULTS T category and number of positive lymph nodes were significantly associated with recurrence in multivariable analysis and were selected for inclusion in the clinical calculator. The calculator's concordance index for FFR in the model training cohort was 0.812 (95% CI, 0.742 to 0.873), compared with 0.759 (95% CI, 0.683 to 0.840) for the staging schema of the eighth edition of the American Joint Committee on Cancer Staging Manual. The concordance index for the validation cohort was 0.744 (95% CI, 0.666 to 0.822), confirming robust predictive accuracy. CONCLUSION Although in general patients with nonmetastatic MSI colon cancer had favorable outcome, patients with advanced T category and multiple metastatic lymph nodes had higher risk of recurrence. The clinical calculator identified patients with MSI colon cancer at high risk for recurrence, and this could inform surveillance strategies. In addition, the model could be used in trial design to identify patients suitable for novel adjuvant therapy.
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Affiliation(s)
- Ayyuce Begum Bektas
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York
| | - Lynn Hakki
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Asama Khan
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Maria Widmar
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Iris H. Wei
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Emmanouil Pappou
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - J. Joshua Smith
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Garrett M. Nash
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Philip B. Paty
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | | | - Andrea Cercek
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York
| | - Zsofia Stadler
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York
| | - Neil H. Segal
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York
| | - Jinru Shia
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York
| | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York
| | - Martin R. Weiser
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
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9
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Kobayashi T, Ishida M, Miki H, Yamamoto N, Harino T, Yagyu T, Hori S, Hatta M, Hashimoto Y, Kotsuka M, Yamasaki M, Inoue K, Hirose Y, Sekimoto M. Prognostic scoring system based on indicators reflecting the tumor glandular differentiation and microenvironment for patients with colorectal cancer. Sci Rep 2024; 14:14188. [PMID: 38902294 PMCID: PMC11189912 DOI: 10.1038/s41598-024-65015-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 06/16/2024] [Indexed: 06/22/2024] Open
Abstract
Prognostic stratification is an urgent concern for patients with colorectal cancer (CRC). The desmoplastic reaction (DR) is speculated to mirror the tumor microenvironment. DR types are considered independent prognostic indicators in CRC, but have not been incorporated in previous prognostic nomograms. We aimed to assess the prognostic significance of a novel approach incorporating histopathological indicators reflecting tumor glandular differentiation and microenvironment. We evaluated 329 consecutive patients with CRC who underwent surgical resection at Kansai Medical University. Histological glandular differentiation was scored as 2 (0 point), 3 (1 point), or 4 (2 points). Tumor buddings (TBs) were classified as TB1 (0 point), TB2 (1 point), or TB3 (2 points). pT1 or 2 was considered as 0 point, pT3 or 4 + DR non-immature type as 1 point, and pT3 or 4 + DR immature type as 2 points. Lymph node metastasis was classified as pN0 (0 point), pN1 (1 point), or pN2 (2 points). The preoperative carcinoembryonic antigen levels were categorized as < 5.0 ng/mL (0 point) and ≧5.0 (1 point). Considering these factors, the following D&M (tumor differentiation and microenvironment) scoring system was applied: I (0-2 points), II (3-4 points), III (5-6 points), and IV (7-9 points). Kaplan-Meier curves showed significant differences in disease-specific survival and recurrence-free survival among the assigned scores, highlighting their enhanced utility compared with the American Joint Committee on Cancer 8th edition staging system. The D&M scoring system was valuable as the initial prognostic nomogram, including DR.
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Affiliation(s)
- Toshinori Kobayashi
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan.
| | - Mitsuaki Ishida
- Department of Pathology, Osaka Medical and Pharmaceutical University, 2-7, Daigaku-Machi, Takatsuki City, Osaka, 569-8686, Japan
| | - Hisanori Miki
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan
| | - Nobuyuki Yamamoto
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan
| | - Takashi Harino
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan
| | - Takuki Yagyu
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan
| | - Soshi Hori
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan
| | - Masahiko Hatta
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan
| | - Yuki Hashimoto
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan
| | - Masaya Kotsuka
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan
| | - Makoto Yamasaki
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan
| | - Kentaro Inoue
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan
| | - Yoshinobu Hirose
- Department of Pathology, Osaka Medical and Pharmaceutical University, 2-7, Daigaku-Machi, Takatsuki City, Osaka, 569-8686, Japan
| | - Mitsugu Sekimoto
- Department of Surgery, Kansai Medical University, 2-5-1, Shinmachi, Hirakata City, Osaka, 573-1010, Japan
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10
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Fujii K, Inagaki A, Masaki A, Sugiura M, Suzuki T, Ishida T, Kusumoto S, Iida S, Inagaki H. Nomogram for predicting survival of patients with diffuse large B-cell lymphoma. Ann Hematol 2024; 103:2041-2050. [PMID: 38411628 DOI: 10.1007/s00277-024-05669-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 02/14/2024] [Indexed: 02/28/2024]
Abstract
The international prognostic index (IPI) system has been widely used to predict prognosis in diffuse large B-cell lymphoma (DLBCL). However, this system categorizes DLBCL patients into four risk groups, and cannot optimize individualized prognosis. In addition, other clinicopathological factors, such as molecular aberrations, are not incorporated into the system. To partly overcome these weak points, we developed nomograms to predict individual patient survival. We also incorporated MYD88L265P and CD79BY196 mutations into the nomograms since these mutations are associated with a worse prognosis and their signaling pathways have been highlighted as a therapeutic target. We analyzed 302 DLBCL cases for which multivariate analysis by Cox proportional hazard regression was performed. Nomograms for progression-free survival (PFS) and overall survival (OS) were constructed and assessed by a concordance index (C-index). The nomograms were also evaluated using an open external dataset (n = 187). The MYD88L265P and/or CD79BY196 (MYD88/CD79B) mutation was detected in 62/302 patients. The nomograms incorporating IPI factors exhibited a C-index of 0.738 for PFS and a C-index of 0.765 for OS. The nomograms incorporating IPI factors and the MYD88/CD79B mutation showed a C-index of 0.745 for PFS and a C-index of 0.769 for OS. The nomograms we created were evaluated using an external dataset and were well validated. The present nomograms incorporating IPI factors and the MYD88/CD79B mutation have sufficient discrimination ability, and may effectively predict prognosis in DLBCL patients. The prognostic models we have presented here may help clinicians personalize prognostic assessments and clinical decisions.
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Affiliation(s)
- Keiichiro Fujii
- Department of Pathology and Molecular Diagnostics, Graduate School of Medical Sciences, Nagoya City University, 1-Kawasumi, Mizuho-Ku, Nagoya, 467-8601, Japan
| | - Atsushi Inagaki
- Department of Hematology and Oncology, Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan
- Nagoya City University West Medical Center, Nagoya, Japan
| | - Ayako Masaki
- Department of Pathology and Molecular Diagnostics, Graduate School of Medical Sciences, Nagoya City University, 1-Kawasumi, Mizuho-Ku, Nagoya, 467-8601, Japan
| | - Mariko Sugiura
- Department of Pathology and Molecular Diagnostics, Graduate School of Medical Sciences, Nagoya City University, 1-Kawasumi, Mizuho-Ku, Nagoya, 467-8601, Japan
| | - Tomotaka Suzuki
- Department of Hematology and Oncology, Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan
| | - Takashi Ishida
- Department of Immunology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shigeru Kusumoto
- Department of Hematology and Oncology, Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan
| | - Shinsuke Iida
- Department of Hematology and Oncology, Graduate School of Medical Sciences, Nagoya City University, Nagoya, Japan
| | - Hiroshi Inagaki
- Department of Pathology and Molecular Diagnostics, Graduate School of Medical Sciences, Nagoya City University, 1-Kawasumi, Mizuho-Ku, Nagoya, 467-8601, Japan.
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11
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Dai L, Yan W, Ren X, Liu D, Chang L, Lin S, Wu H, Kang H, Ma X. Construction and Validation of a Nomogram Predicting the Overall Survival Benefit of Unilateral Breast Cancer Patients Undergoing Contralateral Prophylactic Mastectomy. Clin Breast Cancer 2024; 24:351-362. [PMID: 38521702 DOI: 10.1016/j.clbc.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/02/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Currently, research on the prognostic factors of unilateral breast cancer (UBC) patients receiving contralateral prophylactic mastectomy (CPM) is limited. This study aimed to construct a new nomogram to predict these patients' overall survival (OS). METHODS In this retrospective study, 88,477 patients who underwent CPM or unilateral mastectomy (UM) were selected from the Surveillance, Epidemiology, and End Results database. Kaplan-Meier curves and Cox regression analyses were used to determine the difference in the impact of the 2 surgical methods on the prognosis. Multivariate Cox analysis was used to determine the best prognostic variable and construct a nomogram. The concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the discrimination capability and clinical effectiveness of the nomogram. RESULTS The prognosis of patients receiving CPM and UM was significantly different. The DCA curves indicated that the nomogram could provide more excellent clinical net benefits for these patients. The NRI and IDI of the nomogram demonstrated that its performance was better than that of the classical tumor-node-metastasis (TNM) staging system. CONCLUSION This study developed and validated a practical nomogram to predict the OS of UBC patients undergoing CPM, which provided a beneficial tool for clinical decision-making management.
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Affiliation(s)
- Luyao Dai
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Wenyu Yan
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xueting Ren
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Dandan Liu
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Lidan Chang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Shuai Lin
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Hao Wu
- School of Basic Medical Sciences, Xi'an Key Laboratory of Immune Related Diseases, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
| | - Huafeng Kang
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
| | - Xiaobin Ma
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
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Thean LF, Wong M, Lo M, Tan I, Wong E, Gao F, Tan E, Tang CL, Cheah PY. Functional annotation with expression validation identifies novel metastasis-relevant genes from post-GWAS risk loci in sporadic colorectal carcinomas. J Med Genet 2024; 61:276-283. [PMID: 37890997 DOI: 10.1136/jmg-2023-109517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 09/25/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND Colorectal cancer (CRC) is the third highest incidence cancer and is the leading cause of cancer mortality worldwide. Metastasis to distal organ is the major cause of cancer mortality. However, the underlying genetic factors are unclear. This study aimed to identify metastasis-relevant genes and pathways for better management of metastasis-prone patients. METHODS A case-case genome-wide association study comprising 2677 sporadic Chinese CRC cases (1282 metastasis-positive vs 1395 metastasis-negative) was performed using the Human SNP6 microarray platform and analysed with the correlation/trend test based on the additive model. SNP variants with association testing -log10 p value ≥5 were imported into Functional Mapping and Annotation (FUMA) for functional annotation. RESULTS Glycolysis was uncovered as the top hallmark gene set. Transcripts from two of the five genes profiled, hematopoietic substrate 1 associated protein X 1 (HAX1) and hyaluronan-mediatedmotility receptor (HMMR), were significantly upregulated in the metastasis-positive tumours. In contrast to disease-risk variants, HAX1 appeared to act synergistically with HMMR in significantly impacting metastasis-free survival. Examining the subtype datasets with FUMA and Ingenuity Pathway Analysis (IPA) identified distinct pathways demonstrating sexual dimorphism in CRC metastasis. CONCLUSIONS Combining genome-wide association testing with in silico functional annotation and wet-bench validation identified metastasis-relevant genes that could serve as features to develop subtype-specific metastasis-risk signatures for tailored management of patients with stage I-III CRC.
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Affiliation(s)
- Lai Fun Thean
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
| | - Michelle Wong
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
| | - Michelle Lo
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
| | - Iain Tan
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Evelyn Wong
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Fei Gao
- National Heart Research Institute Singapore, National Heart Centre Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Emile Tan
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
| | - Choong Leong Tang
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
| | - Peh Yean Cheah
- Department of Colorectal Surgery, Singapore General Hospital, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
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13
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Khan A, Thompson H, Hsu M, Widmar M, Wei IH, Pappou E, Smith JJ, Nash GM, Paty PB, Garcia-Aguilar J, Shia J, Gonen M, Weiser MR. Validation of a Clinical Calculator Predicting Freedom From Colon Cancer Recurrence After Surgery on the Basis of Molecular and Clinical Variables. Dis Colon Rectum 2024; 67:240-245. [PMID: 37815326 PMCID: PMC10843082 DOI: 10.1097/dcr.0000000000002896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
BACKGROUND The Memorial Sloan Kettering clinical calculator for estimating the likelihood of freedom from colon cancer recurrence on the basis of clinical and molecular variables was developed at a time when testing for microsatellite instability was performed selectively, based on patient age, family history, and histologic features. Microsatellite stability was assumed if no testing was done. OBJECTIVE This study aimed to validate the calculator in a cohort of patients who had all been tested for microsatellite instability. DESIGN Retrospective cohort analysis. SETTINGS Comprehensive cancer center. PATIENTS This study included consecutive patients who underwent curative resection for stage I, II, or III colon cancer between 2017 and 2019. INTERVENTION Universal testing of mircrosatellite phenotype in all cases. MAIN OUTCOME MEASURES The calculator's predictive accuracy was assessed using the concordance index and a calibration plot of predicted versus actual freedom from recurrence at 3 years after surgery. For a secondary sensitivity analysis, the presence of a tumor deposit(s) (disease category N1c) was considered equivalent to one positive lymph node (category N1a). RESULTS With a median follow-up of 32 months among survivors, the concordance index for the 745 patients in the cohort was 0.748 (95% CI, 0.693-0.801), and a plot of predicted versus observed recurrences approached the 45° diagonal, indicating good discrimination and calibration. In the secondary sensitivity analysis for tumor deposits, the concordance index was 0.755 (95% CI, 0.700-0.806). LIMITATIONS This study was limited by its retrospective, single-institution design. CONCLUSIONS These results, based on inclusion of actual rather than imputed microsatellite stability status and presence of tumor deposits, confirm the predictive accuracy and reliability of the calculator. See Video Abstract . VALIDACIN DE UNA CALCULADORA CLNICA QUE PREDICE LA AUSENCIA DE RECURRENCIA POSTQUIRURGICA DEL CNCER DE COLON SOBRE LA BASE DE VARIABLES MOLECULARES Y CLNICAS ANTECEDENTES:La calculadora clínica del Memorial Sloan Kettering para la estimación de la probabilidad de ausencia de recurrencia del cáncer de colon sobre la base de variables clínicas y moleculares, se desarrolló en un momento en que las pruebas para la inestabilidad de microsatélites se realizaban de forma selectiva, basadas en la edad del paciente, los antecedentes familiares y las características histológicas. Se asumía la estabilidad micro satelital si no se realizaba ninguna prueba.OBJETIVO:El objetivo de este estudio fue validar la calculadora en una cohorte de pacientes a los que se les había realizado la prueba de inestabilidad de microsatélites.DISEÑO:Análisis de cohorte retrospectivo.AJUSTE:Centro integral de cáncer.PACIENTES:Pacientes consecutivos con cáncer de colon que fueron sometidos a resección curativa por cáncer de colon en estadios I, II o III entre los años 2017 y 2019.PRINCIPALES MEDIDAS DE RESULTADO:La precisión predictiva de la calculadora fue evaluada mediante el índice de concordancia y un gráfico de calibración de la ausencia de recurrencia predecida versus la real a los 3 años tras la cirugía. A los efectos de un análisis secundario de sensibilidad, la presencia de depósito(s) tumoral(es) (categoría de enfermedad N1c) se consideró equivalente a un ganglio linfático positivo (categoría N1a).RESULTADOS:Con una mediana de seguimiento de 32 meses entre los supervivientes, el índice de concordancia para los 745 pacientes de la cohorte fue de 0,748 (intervalo de confianza del 95 %, 0,693 a 0,801), y una gráfica de recurrencias previstas versus observadas se acercó a la diagonal de 45°, indicando una buena discriminación y calibración. En el análisis secundario de sensibilidad para depósitos tumorales, el índice de concordancia fue de 0,755 (intervalo de confianza del 95 %, 0,700 a 0,806).LIMITACIONES:Diseño retrospectivo, institución única.CONCLUSIONES:Estos resultados, basados en la inclusión real del estado de estabilidad de microsatélites en lugar de imputado y la presencia de depósitos tumorales, confirman la precisión predictiva y la confiabilidad de la calculadora. (Traducción-Dr Osvaldo Gauto ).
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Affiliation(s)
- Asama Khan
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Hannah Thompson
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Meier Hsu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York
| | - Maria Widmar
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Iris H. Wei
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Emmanouil Pappou
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - J. Joshua Smith
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Garrett M. Nash
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | - Philip B. Paty
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
| | | | - Jinru Shia
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York
| | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York
| | - Martin R. Weiser
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York
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Wu X, Li W, Tu H. Big data and artificial intelligence in cancer research. Trends Cancer 2024; 10:147-160. [PMID: 37977902 DOI: 10.1016/j.trecan.2023.10.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 11/19/2023]
Abstract
The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of cancer.
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Affiliation(s)
- Xifeng Wu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Wenyuan Li
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Huakang Tu
- Department of Big Data in Health Science, School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
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15
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Song X, Wang P, Feng R, Chetry M, Li E, Wu X, Liu Z, Liao S, Lin J. Prognostic model of ER-positive, HER2-negative breast cancer predicted by clinically relevant indicators. Clin Transl Oncol 2024; 26:389-397. [PMID: 37713046 DOI: 10.1007/s12094-023-03316-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/12/2023] [Indexed: 09/16/2023]
Abstract
PURPOSE To study the clinicopathological variables connected with disease-free survival (DFS) as well as overall survival (OS) in patients who are ER-positive or HER2-negative and to propose nomograms for predicting individual risk. METHODS In this investigation, we examined 585 (development cohort) and 291 (external validation) ER-positive, HER2-negative breast cancer patients from January 2010 to January 2014. From January 2010 to December 2014, we retrospectively reviewed and analyzed 291 (external validation) and 585 (development cohort) HER2-negative, ER-positive breast cancer patients. Cox regression analysis, both multivariate and univariate, confirmed the independence indicators for OS and DFS. RESULTS Using cox regression analysis, both multivariate and univariate, the following variables were combined to predict the DFS of development cohort: pathological stage (HR = 1.391; 95% CI = 1.043-1.855; P value = 0.025), luminal parting (HR = 1.836; 95% CI = 1.142-2.952; P value = .012), and clinical stage (HR = 1.879; 95% CI = 1.102-3.203; P value = 0.021). Endocrine therapy (HR = 3.655; 95% CI = 1.084-12.324; P value = 0.037) and clinical stage (HR = 6.792; 95% CI = 1.672-28.345; P value = 0.009) were chosen as predictors of OS. Furthermore, we generated RS-OS and RS-DFS. According to the findings of Kaplan-Meier curves, patients who are classified as having a low risk have considerably longer DFS and OS durations than patients who are classified as having a high risk. CONCLUSION To generate nomograms that predicted DFS and OS, independent predictors of DFS in ER-positive/HER2-negative breast cancer patients were chosen. The nomograms successfully stratified patients into prognostic categories and worked well in both internal validation and external validation.
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Affiliation(s)
- Xinming Song
- Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Jinping District, Shantou, 515041, Guangdong, China
| | - Pintian Wang
- Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Jinping District, Shantou, 515041, Guangdong, China
| | - Ruiling Feng
- Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Jinping District, Shantou, 515041, Guangdong, China
| | - Mandika Chetry
- Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Jinping District, Shantou, 515041, Guangdong, China
| | - E Li
- Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, Longhu People's Hospital, Shantou, 515041, China
| | - Xiaohua Wu
- Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, Longhu People's Hospital, Shantou, 515041, China
| | - Zewa Liu
- Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Jinping District, Shantou, 515041, Guangdong, China
| | - Shasha Liao
- Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, Longhu People's Hospital, Shantou, 515041, China
| | - Jing Lin
- Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Jinping District, Shantou, 515041, Guangdong, China.
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Zhang H, Wang H, Yao Y, Liu L, Feng F, Li H, Sun C. Evaluation of Risk Factors, and Development and Validation of Prognostic Prediction Models for Distant Metastasis in Patients With Rectal Cancer: A Study Based on the SEER Database and a Chinese Population. Cancer Control 2024; 31:10732748241303650. [PMID: 39579008 PMCID: PMC11585045 DOI: 10.1177/10732748241303650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2024] [Revised: 11/07/2024] [Accepted: 11/11/2024] [Indexed: 11/24/2024] Open
Abstract
PURPOSE This study aimed to construct a prediction model regarding risk factors and prognostic factors for distant metastasis of T1-T3 stage rectal cancer. For this purpose, a population-based retrospective cohort study was conducted. METHODS Data on 7872 patients diagnosed with rectal cancer between 2004 and 2020 were obtained from the Surveillance, Epidemiology, and End Results database, of whom 746 had distant metastases at diagnosis. Independent risk factors for distant metastasis of rectal cancer were determined using univariate and multivariate logistic regression analyses. Cox proportional hazards regression analyses clarified the independent prognostic factors for distant metastases of rectal cancer. A 7:3 randomization process was used to place all patients into the training and internal validation groups. Furthermore, we retrospectively collected clinical data from 226 patients who had both rectal cancer and distant metastases between 2012 and 2024 at the Weifang Hospital of Traditional Chinese Medicine. We used the calibration curve, DCA curve, C-index, and area under the curve (AUC) to assess the discriminatory and pre-precision qualities of the models. RESULTS The multivariate logistic regression analysis identified race, tumor grade, T stage, N stage, radiotherapy, chemotherapy, surgery, tumor size, and histological subtype as risk factors for distant metastases in rectal cancer, with AUC values for both training and validation sets exceeding 0.8. Using Cox regression analysis, we determined that the age, sex, tumor size, surgery, chemotherapy, and radiotherapy were independent predictors of distant metastasis of rectal cancer. In the prognostic model, the C-index of the training cohort was 0.687 (95% CI: 0.6615-0.7125), that of the internal validation cohort was 0.692 (95% CI: 0.6508-0.7332), and that of the external validation cohort was 0.704 (0.6785-0.7295). CONCLUSION Our nomogram can predict risk factors and analyze the 1-, 2-, and 3 year prognosis of distant metastases in patients with rectal cancer, providing valuable guidance for future clinical work.
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Affiliation(s)
- Huiru Zhang
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Haojun Wang
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yan Yao
- Oncology Laboratory, Weifang Traditional Chinese Hospital, Weifang, China
| | - Lijuan Liu
- Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, China
| | - Fubin Feng
- Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, China
| | - Huayao Li
- College of Traditional Chinese Medicine, Shandong Second Medical University, Weifang, China
| | - Changgang Sun
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
- Department of Oncology, Weifang Traditional Chinese Hospital, Weifang, China
- College of Traditional Chinese Medicine, Shandong Second Medical University, Weifang, China
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Rodriguez PJ, Heagerty PJ, Clark S, Khor S, Chen Y, Haupt E, Hahn EE, Shankaran V, Bansal A. Using Machine Learning to Leverage Biomarker Change and Predict Colorectal Cancer Recurrence. JCO Clin Cancer Inform 2023; 7:e2300066. [PMID: 37963310 PMCID: PMC10681492 DOI: 10.1200/cci.23.00066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/12/2023] [Accepted: 07/12/2023] [Indexed: 11/16/2023] Open
Abstract
PURPOSE The risk of colorectal cancer (CRC) recurrence after primary treatment varies across individuals and over time. Using patients' most up-to-date information, including carcinoembryonic antigen (CEA) biomarker profiles, to predict risk could improve personalized decision making. METHODS We used electronic health record data from an integrated health system on a cohort of patients diagnosed with American Joint Committee on Cancer stage I-III CRC between 2008 and 2013 (N = 3,970) and monitored until recurrence or end of follow-up. We addressed missingness in recurrence outcomes and longitudinal CEA measures, and engineered CEA features using current and past biomarker values for inclusion in a risk prediction model. We used a discrete time Superlearner model to evaluate various algorithms for predicting recurrence. We evaluated the time-varying discrimination and calibration of the algorithms and assessed the role of individual predictors. RESULTS Recurrence was documented in 448 (11.3%) patients. XGBoost with depth = 1 (XGB-D1) predicted recurrence substantially better than all other algorithms at all time points, with AUC ranging from 0.87 (95% CI, 0.86 to 0.88) at 6 months to 0.94 (95% CI, 0.92 to 0.96) at 54 months. The only variable used by XGB-D1 was 6-month change in log CEA. Predicted 1-year risk of recurrence was nearly zero for patients whose log CEA did not increase in the last 6 months, between 12.2% and 34.1% for patients whose log CEA increased between 0.10 and 0.40, and 43.6% for those with a log CEA increase >0.40. Compared with XGB, penalized regression approaches (lasso, ridge, and elastic net) performed poorly, with AUCs ranging from 0.58 to 0.69. CONCLUSION A flexible, machine learning approach that incorporated longitudinal CEA information yielded a simple and high-performing model for predicting recurrence on the basis of 6-month change in log CEA.
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Affiliation(s)
- Patricia J. Rodriguez
- The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington, Seattle, WA
| | | | - Samantha Clark
- The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington, Seattle, WA
| | - Sara Khor
- The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington, Seattle, WA
| | - Yilin Chen
- The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington, Seattle, WA
| | - Eric Haupt
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA
| | - Erin E. Hahn
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA
| | | | - Aasthaa Bansal
- The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington, Seattle, WA
- Fred Hutchinson Cancer Center, Seattle, WA
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Sevilimedu V, Yu L, Samawi H. Misclassification simulation extrapolation method for a Weibull accelerated failure time model. Stat Methods Med Res 2023; 32:1478-1493. [PMID: 37122155 PMCID: PMC10939450 DOI: 10.1177/09622802231168248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
The problem of misclassification in covariates is ubiquitous in survival data and often leads to biased estimates. The misclassification simulation extrapolation method is a popular method to correct this bias. However, its impact on Weibull accelerated failure time models has not been studied. In this paper, we study the bias caused by misclassification in one or more binary covariates in Weibull accelerated failure time models and explore the use of the misclassification simulation extrapolation in correcting for this bias, along with its asymptotic properties. Simulation studies are carried out to investigate the numerical properties of the resulting estimator for finite samples. The proposed method is then applied to colon cancer data obtained from the cancer registry at Memorial Sloan Kettering Cancer Center.
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Affiliation(s)
- Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lili Yu
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, JPH college of Public Health, Georgia Southern University, Statesboro, GA, USA
| | - Hani Samawi
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, JPH college of Public Health, Georgia Southern University, Statesboro, GA, USA
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Li J, Huang L, Liao C, Liu G, Tian Y, Chen S. Two machine learning-based nomogram to predict risk and prognostic factors for liver metastasis from pancreatic neuroendocrine tumors: a multicenter study. BMC Cancer 2023; 23:529. [PMID: 37296397 DOI: 10.1186/s12885-023-10893-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 04/27/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Pancreatic neuroendocrine tumors (PNETs) are one of the most common endocrine tumors, and liver metastasis (LMs) are the most common location of metastasis from PNETS; However, there is no valid nomogram to predict the diagnosis and prognosis of liver metastasis (LMs) from PNETs. Therefore, we aimed to develop a valid predictive model to aid physicians in making better clinical decisions. METHODS We screened patients in the Surveillance, Epidemiology, and End Results (SEER) database from 2010-2016. Feature selection was performed by machine learning algorithms and then models were constructed. Two nomograms were constructed based on the feature selection algorithm to predict the prognosis and risk of LMs from PNETs. We then used the area under the curve (AUC), receiver operating characteristic (ROC) curve, calibration plot and consistency index (C-index) to evaluate the discrimination and accuracy of the nomograms. Kaplan-Meier (K-M) survival curves and decision curve analysis (DCA) were also used further to validate the clinical efficacy of the nomograms. In the external validation set, the same validation is performed. RESULTS Of the 1998 patients screened from the SEER database with a pathological diagnosis of PNET, 343 (17.2%) had LMs at the time of diagnosis. The independent risk factors for the occurrence of LMs in PNET patients included histological grade, N stage, surgery, chemotherapy, tumor size and bone metastasis. According to Cox regression analysis, we found that histological subtype, histological grade, surgery, age, and brain metastasis were independent prognostic factors for PNET patients with LMs. Based on these factors, the two nomograms demonstrated good performance in model evaluation. CONCLUSION We developed two clinically significant predictive models to aid physicians in personalized clinical decision-makings.
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Affiliation(s)
- Jianbo Li
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Long Huang
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Chengyu Liao
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou, 350001, China
| | - Guozhong Liu
- Department of Hepatopancreatobiliary Surgery, First Affiliated Hospital of Fujian Medical University, Fujian, 350005, China
| | - Yifeng Tian
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China.
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou, 350001, China.
| | - Shi Chen
- Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, 350001, China.
- Department of Hepatobiliary Pancreatic Surgery, Fujian Provincial Hospital, Fuzhou, 350001, China.
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Qin L, Heng Y, Deng S, Gu J, Mao F, Xue Y, Jiang Z, Wang J, Cheng D, Wu K, Cao Y, Cai K. Perineural invasion affects prognosis of patients undergoing colorectal cancer surgery: a propensity score matching analysis. BMC Cancer 2023; 23:452. [PMID: 37202778 PMCID: PMC10197328 DOI: 10.1186/s12885-023-10936-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 05/09/2023] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND Tumour perineural invasion (PNI) is a predictor of poor prognosis, but its effect on the prognosis of patients with colorectal cancer (CRC) has not yet been elucidated. METHODS This retrospective study used propensity score matching (PSM). The clinical case data of 1470 patients with surgically treated stage I-IV CRC at Wuhan Union Hospital were collected. PSM was used to analyse and compare the clinicopathological characteristics, perioperative outcomes, and long-term prognostic outcomes of the PNI(+) and PNI(-) groups. The factors influencing prognosis were screened using Cox univariate and multivariate analyses. RESULTS After PSM, 548 patients were included in the study (n = 274 in each group). Multifactorial analysis showed that neurological invasion was an independent prognostic factor affecting patients' OS and DFS (hazard ratio [HR], 1.881; 95% confidence interval [CI], 1.35-2.62; P = 0.0001; HR, 1.809; 95% CI, 1.353-2.419; P < 0.001). Compared to PNI(+) patients without chemotherapy, those who received chemotherapy had a significant improvement in OS (P < 0.01). The AUROC curve of OS in the PNI(+) subgroup (0.802) was higher than that after PSM (0.743), while that of DFS in the PNI(+) subgroup (0.746) was higher than that after PSM (0.706). The independent predictors of PNI(+) could better predict the prognosis and survival of patients with PNI(+). CONCLUSIONS PNI significantly affects the long-term survival and prognosis of patients with CRC undergoing surgery and is an independent risk factor for OS and DFS in patients with CRC undergoing surgery. Postoperative chemotherapy significantly improved the OS of PNI(+) patients.
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Affiliation(s)
- Le Qin
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
- Department of General Surgery, The First Affiliated Hospital of Shihezi University, Shihezi, 832008, Xinjiang, P.R. China
| | - Yixin Heng
- Department of General Surgery, The First Affiliated Hospital of Shihezi University, Shihezi, 832008, Xinjiang, P.R. China
| | - Shenghe Deng
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Junnan Gu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Fuwei Mao
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Yifan Xue
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Zhenxing Jiang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Jun Wang
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Denglong Cheng
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Ke Wu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Yinghao Cao
- Department of Digestive Surgical Oncology, Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Kailin Cai
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China.
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21
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Zhang XY, Xie S, Wang DC, Shan XF, Cai ZG. Prognosis and Nomogram Prediction for Patients with Oral Squamous Cell Carcinoma: A Cohort Study. Diagnostics (Basel) 2023; 13:1768. [PMID: 37238252 PMCID: PMC10217586 DOI: 10.3390/diagnostics13101768] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/14/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023] Open
Abstract
The TNM staging system is often used to predict the prognosis of patients with oral squamous cell carcinoma (OSCC). However, we have found that patients under the same TNM staging may exhibit tremendous differences in survival rates. Therefore, we aimed to investigate the prognosis of postoperative OSCC patients, establish a nomogram survival prediction model, and verify its effectiveness. Operative logs were reviewed for patients who underwent surgical treatment for OSCC at the Peking University School and Hospital of Stomatology. Patient demographic and surgical records were obtained, and they were followed up for overall survival (OS). A total of 432 patients with oral squamous cell carcinoma were included in the study, with a median follow-up time of 47 months. Based on the results of the Cox regression analysis, we constructed and verified the nomogram prediction model, which includes gender, BMI, OPMDs, pain score, SCC grade, and N stage. The C-index value of the 3-year and 5-year prediction models was 0.782 and 0.770, respectively, proving that the model has a certain level of prediction stability. The new nomogram prediction model has potential clinical significance for predicting the postoperative survival of OSCC patients.
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Affiliation(s)
| | | | | | - Xiao-Feng Shan
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China
| | - Zhi-Gang Cai
- Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China
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22
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Lang L, Wang T, Xie L, Yang C, Skudder-Hill L, Jiang J, Gao G, Feng J. An independently validated nomogram for individualised estimation of short-term mortality risk among patients with severe traumatic brain injury: a modelling analysis of the CENTER-TBI China Registry Study. EClinicalMedicine 2023; 59:101975. [PMID: 37180469 PMCID: PMC10173159 DOI: 10.1016/j.eclinm.2023.101975] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/02/2023] [Accepted: 04/05/2023] [Indexed: 05/16/2023] Open
Abstract
Background Severe traumatic brain injury (sTBI) is extremely disabling and associated with high mortality. Early detection of patients at risk of short-term (≤14 days after injury) death and provision of timely treatment is critical. This study aimed to establish and independently validate a nomogram to estimate individualised short-term mortality for sTBI based on large-scale data from China. Methods The data were from the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) China registry (between Dec 22, 2014, and Aug 1, 2017; registered at ClinicalTrials.gov, NCT02210221). This analysis included information of eligible patients with diagnosed sTBI from 52 centres (2631 cases). 1808 cases from 36 centres were enrolled in the training group (used to construct the nomogram) and 823 cases from 16 centres were enrolled in the validation group. Multivariate logistic regression was used to identify independent predictors of short-term mortality and establish the nomogram. The discrimination of the nomogram was evaluated using area under the receiver operating characteristic curves (AUC) and concordance indexes (C-index), the calibration was evaluated using calibration curves and Hosmer-Lemeshow tests (H-L tests). Decision curve analysis (DCA) was used to evaluate the net benefit of the model for patients. Findings In the training group, multivariate logistic regression demonstrated that age (odds ratio [OR] 1.013, 95% confidence interval [CI] 1.003-1.022), Glasgow Coma Scale score (OR 33.997, 95% CI 14.657-78.856), Injury Severity Score (OR 1.020, 95% CI 1.009-1.032), abnormal pupil status (OR 1.738, 95% CI 1.178-2.565), midline shift (OR 2.266, 95% CI 1.378-3.727), and pre-hospital intubation (OR 2.059, 95% CI 1.472-2.879) were independent predictors for short-term death in patients with sTBI. A nomogram was built using the logistic regression prediction model. The AUC and C-index were 0.859 (95% CI 0.837-0.880). The calibration curve of the nomogram was close to the ideal reference line, and the H-L test p value was 0.504. DCA curve demonstrated significantly better net benefit with the model. Application of the nomogram in external validation group still showed good discrimination (AUC and C-index were 0.856, 95% CI 0.827-0.886), calibration, and clinical usefulness. Interpretation A nomogram was developed for predicting the occurrence of short-term (≤14 days after injury) death in patients with sTBI. This can provide clinicians with an effective and accurate tool for the early prediction and timely management of sTBI, as well as support clinical decision-making around the withdrawal of life-sustaining therapy. This nomogram is based on Chinese large-scale data and is especially relevant to low- and middle-income countries. Funding Shanghai Academic Research Leader (21XD1422400), Shanghai Medical and Health Development Foundation (20224Z0012).
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Affiliation(s)
- Lijian Lang
- Brain Injury Centre, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, 200127, China
- Shanghai Institute of Head Trauma, 160 Pujian Road, Shanghai, 200127, China
| | - Tianwei Wang
- Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, 253 Gongye Dadao, Haizhu District, Guangzhou, 510282, China
| | - Li Xie
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, 227 Chongqing Road, Shanghai, China
| | - Chun Yang
- Shanghai Institute of Head Trauma, 160 Pujian Road, Shanghai, 200127, China
| | - Loren Skudder-Hill
- Department of Neurosurgery, Yuquan Hospital Affiliated to Tsinghua University School of Clinical Medicine, 5 Shijingshan Road, Shijingshan, Beijing, 100049, China
| | - Jiyao Jiang
- Brain Injury Centre, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, 200127, China
- Shanghai Institute of Head Trauma, 160 Pujian Road, Shanghai, 200127, China
| | - Guoyi Gao
- Shanghai Institute of Head Trauma, 160 Pujian Road, Shanghai, 200127, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
- Corresponding author. Shanghai Institute of Head Trauma, 160 Pujian Road, Shanghai, 200127, China.
| | - Junfeng Feng
- Brain Injury Centre, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, 200127, China
- Shanghai Institute of Head Trauma, 160 Pujian Road, Shanghai, 200127, China
- Corresponding author. Brain Injury Centre, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 160 Pujian Road, Shanghai, China.
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Bray JP, Munday JS. Development of a Nomogram to Predict the Outcome for Patients with Soft Tissue Sarcoma. Vet Sci 2023; 10:vetsci10040266. [PMID: 37104421 PMCID: PMC10146366 DOI: 10.3390/vetsci10040266] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023] Open
Abstract
Soft tissue sarcomas (STSs) are common cutaneous or subcutaneous neoplasms in dogs. Most STSs are initially treated by surgical excision, and local recurrence may develop in almost 20% of patients. Currently, it is difficult to predict which STS will recur after excision, but this ability would greatly assist patient management. In recent years, the nomogram has emerged as a tool to allow oncologists to predict an outcome from a combination of risk factors. The aim of this study was to develop a nomogram for canine STSs and determine if the nomogram could predict patient outcomes better than individual tumour characteristics. The current study provides the first evidence in veterinary oncology to support a role for the nomogram to assist with predicting the outcome for patients after surgery for STSs. The nomogram developed in this study accurately predicted tumour-free survival in 25 patients but failed to predict recurrence in 1 patient. Overall, the sensitivity, specificity, positive predictive, and negative predictive values for the nomogram were 96%, 45%, 45%, and 96%, respectively (area under the curve: AUC = 0.84). This study suggests a nomogram could play an important role in helping to identify patients who could benefit from revision surgery or adjuvant therapy for an STS.
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Predictive Value of Inflammatory and Nutritional Indexes in the Pathology of Bladder Cancer Patients Treated with Radical Cystectomy. Curr Oncol 2023; 30:2582-2597. [PMID: 36975410 PMCID: PMC10047817 DOI: 10.3390/curroncol30030197] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/15/2023] [Accepted: 02/17/2023] [Indexed: 02/24/2023] Open
Abstract
In recent years, the focus of numerous studies has been the predictive value of inflammatory and nutritional parameters in oncology patients. The aim of our study was to examine the relationship between the inflammatory and nutritional parameters and the histopathological characteristics of patients with bladder cancer. A retrospective study included 491 patients who underwent radical cystectomy for bladder cancer between 2017 and 2021. We calculated the preoperative values of the neutrophil-to-lymphocyte ratio (NLR), the derived neutrophil-to-lymphocyte ratio (dNLR), the systemic immune-inflammation index (SII), the systemic inflammatory response index (SIRI), the platelet-to-lymphocyte ratio (PLR), the lymphocyte-to-monocyte ratio (LMR), the prognostic nutritional index (PNI), and the geriatric nutritional risk index (GNRI). Statistically significant positive correlations were observed between NLR, dNLR, SII, SIRI, and PLR and the pathological stage of the tumor. We observed statistically significant inverse correlations for LMR, PNI, and GNRI with the tumor stage. SIRI was identified as an independent predictor of the presence of LVI. dNLR was identified as an independent predictor of positive surgical margins. GNRI was identified as an independent predictor of the presence of metastases in the lymph nodes. We noticed the predictive value of SIRI, dNLR, and GNRI in the pathology of bladder cancer patients.
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Li C, Zhao K, Zhang D, Pang X, Pu H, Lei M, Fan B, Lv J, You D, Li Z, Zhang T. Prediction models of colorectal cancer prognosis incorporating perioperative longitudinal serum tumor markers: a retrospective longitudinal cohort study. BMC Med 2023; 21:63. [PMID: 36803500 PMCID: PMC9942392 DOI: 10.1186/s12916-023-02773-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 02/08/2023] [Indexed: 02/22/2023] Open
Abstract
BACKGROUND Current prognostic prediction models of colorectal cancer (CRC) include only the preoperative measurement of tumor markers, with their available repeated postoperative measurements underutilized. CRC prognostic prediction models were constructed in this study to clarify whether and to what extent the inclusion of perioperative longitudinal measurements of CEA, CA19-9, and CA125 can improve the model performance, and perform a dynamic prediction. METHODS The training and validating cohort included 1453 and 444 CRC patients who underwent curative resection, with preoperative measurement and two or more measurements within 12 months after surgery, respectively. Prediction models to predict CRC overall survival were constructed with demographic and clinicopathological variables, by incorporating preoperative CEA, CA19-9, and CA125, as well as their perioperative longitudinal measurements. RESULTS In internal validation, the model with preoperative CEA, CA19-9, and CA125 outperformed the model including CEA only, with the better area under the receiver operating characteristic curves (AUCs: 0.774 vs 0.716), brier scores (BSs: 0.057 vs 0.058), and net reclassification improvement (NRI = 33.5%, 95% CI: 12.3 ~ 54.8%) at 36 months after surgery. Furthermore, the prediction models, by incorporating longitudinal measurements of CEA, CA19-9, and CA125 within 12 months after surgery, had improved prediction accuracy, with higher AUC (0.849) and lower BS (0.049). Compared with preoperative models, the model incorporating longitudinal measurements of the three markers had significant NRI (40.8%, 95% CI: 19.6 to 62.1%) at 36 months after surgery. External validation showed similar results to internal validation. The proposed longitudinal prediction model can provide a personalized dynamic prediction for a new patient, with estimated survival probability updated when a new measurement is collected during 12 months after surgery. CONCLUSIONS Prediction models including longitudinal measurements of CEA, CA19-9, and CA125 have improved accuracy in predicting the prognosis of CRC patients. We recommend repeated measurements of CEA, CA19-9, and CA125 in the surveillance of CRC prognosis.
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Affiliation(s)
- Chunxia Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wenhuaxi Road, PO Box 100, Jinan, 250012, Shandong, China
| | - Ke Zhao
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Dafu Zhang
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, No.519 Kunzhou Road, Xishan District, Kunming, 650118, Yunnan, China
| | - Xiaolin Pang
- Department of Radiotherapy, the Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510655, China
| | - Hongjiang Pu
- Department of Colorectal Surgery, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China
| | - Ming Lei
- Department of Clinical Laboratory Medicine, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China
| | - Bingbing Fan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wenhuaxi Road, PO Box 100, Jinan, 250012, Shandong, China
| | - Jiali Lv
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wenhuaxi Road, PO Box 100, Jinan, 250012, Shandong, China
| | - Dingyun You
- School of Biomedical Engineering Research, Kunming Medical University, No.1168 Chunrongxi Road, Chenggong District, Kunming, 650500, Yunnan, China.
| | - Zhenhui Li
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, No.519 Kunzhou Road, Xishan District, Kunming, 650118, Yunnan, China.
| | - Tao Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, 44 Wenhuaxi Road, PO Box 100, Jinan, 250012, Shandong, China.
- Institute for Medical Dataology, Shandong University, Jinan, 250002, China.
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Bray J, Eward W, Breen M. Defining the relevance of surgical margins. Part two: Strategies to improve prediction of recurrence risk. Vet Comp Oncol 2023; 21:145-158. [PMID: 36745110 DOI: 10.1111/vco.12881] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 12/03/2022] [Accepted: 02/03/2023] [Indexed: 02/07/2023]
Abstract
Due to the complex nature of tumour biology and the integration between host tissues and molecular processes of the tumour cells, a continued reliance on the status of the microscopic cellular margin should not remain our only determinant of the success of a curative-intent surgery for patients with cancer. Based on current evidence, relying on a purely cellular focus to provide a binary indication of treatment success can provide an incomplete interpretation of potential outcome. A more holistic analysis of the cancer margin may be required. If we are to move ahead from our current situation - and allow treatment plans to be more intelligently tailored to meet the requirements of each individual tumour - we need to improve our utilisation of techniques that either improve recognition of residual tumour cells within the surgical field or enable a more comprehensive interrogation of tumour biology that identifies a risk of recurrence. In the second article in this series on defining the relevance of surgical margins, the authors discuss possible alternative strategies for margin assessment and evaluation in the canine and feline cancer patient. These strategies include considering adoption of the residual tumour classification scheme; intra-operative imaging systems including fluorescence-guided surgery, optical coherence tomography and Raman spectroscopy; molecular analysis and whole transcriptome analysis of tissues; and the development of a biologic index (nomogram). These techniques may allow evaluation of individual tumour biology and the status of the resection margin in ways that are different to our current techniques. Ultimately, these techniques seek to better define the risk of tumour recurrence following surgery and provide the surgeon and patient with more confidence in margin assessment.
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Affiliation(s)
| | - Will Eward
- Orthopedic Surgical Oncologist, Duke Cancer Center, Durham, North Carolina, USA
| | - Matthew Breen
- Oscar J. Fletcher Distinguished Professor of Comparative Oncology Genetics, College of Veterinary Medicine, North Carolina State University, Raleigh, North Carolina, USA
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The T-CEA score: a useful prognostic indicator based on postoperative CEA and pathological T4 levels for patients with stage II-III colorectal cancer. Surg Today 2023:10.1007/s00595-023-02644-6. [PMID: 36707435 DOI: 10.1007/s00595-023-02644-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/21/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE To investigate a prognostic score for stage II-III colorectal cancer (CRC) based on post-CEA and pT4 levels. METHODS Two cohorts of stage II-III CRC patients who underwent curative surgery between 2011 and 2017 were included. The prognostic score (T-CEA score) was calculated as follows: T-CEA-0, post-CEA ≤ 5 ng/mL and pT1-3; T-CEA-1, post-CEA > 5 ng/mL or pT4; T-CEA-2, post-CEA > 5 ng/mL and pT4. RESULTS The T-CEA scores of the 587 patients were as follows: T-CEA-0 (n = 436; 74%), T-CEA-1 (n = 129; 22%), and T-CEA-2 (n = 10; 2%). The 5-year recurrence-free survival (RFS) rates of the T-CEA-0, 1, and 2 groups were 80.3%, 54.8%, and 0%, respectively (P < 0.01), and the 5-year overall survival (OS) rates were 90.9%, 74.2%, and 0%, respectively (T-CEA-0 vs T-CEA-1: P < 0.01, T-CEA-1 vs T-CEA-2: P = 0.04). Multivariate analysis revealed that an elevated T-CEA score of 1 or 2 was a significant risk factor for poor RFS (HR: 2.89, P < 0.01) and OS (HR: 2.85, P < 0.01). CONCLUSION The T-CEA score is a reliable and convenient prognostic score for stage II-III CRC.
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Qu A, Wang Q, Chang Q, Liu J, Yang Y, Zhang X, Zhang Y, Zhang X, Wang H, Zhang Y. Prognostic and predictive value of a lncRNA signature in patients with stage II colon cancer. Sci Rep 2023; 13:1350. [PMID: 36693876 PMCID: PMC9873786 DOI: 10.1038/s41598-022-25852-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 12/06/2022] [Indexed: 01/26/2023] Open
Abstract
The current staging method is inadequate to identify high-risk recurrence patients with stage II colon cancer (CC). Using a systematic and comprehensive-biomarker discovery and validation method, we aimed to construct a lncRNA-based signature to improve the prognostic prediction of stage II CC. We identified 1,377 differently expressed lncRNAs by analyzing 16 paired stage II CC tumor tissue and adjacent normal mucosal tissue from the TCGA dataset. Subsequently, using a univariable and step multivariable Cox regression model, we trained an 11-lncRNA signature in the training cohort (n = 141), which could divide patients into high-risk and low-risk groups (AUC at 3 years = 0.801, 95% CI: 0.724-0.877; AUC at 5 years = 0.801, 95% CI: 0.718-0.885). Significantly, patients in the high-risk group had poorer recurrence-free survival (RFS) compared with the low-risk group (log-rank test, P < 0.001 in the training cohort). This lncRNA-based signature was further confirmed in the validation cohort (P < 0.001). Multivariate Cox regression and stratified survival analyses showed that the prognostic value of this signature was independent of other clinicopathological risk factors (CEA, T stage, and chemotherapy). Time-dependent receiver operating characteristic (ROC) analysis demonstrated that this signature had better prognostic ability than any other clinical risk factors or single lncRNAs (all P < 0.05). A nomogram was constructed for clinical use, which integrated both the lncRNA-based signature and clinical risk factors (CEA and T stage) and performed well in the calibration plots. Altogether, our lncRNA-based signature was an independent prognostic factor and possessed a stronger predictive power compared with the currently used clinicopathological risk factors when predicting the recurrence of patients with stage II CC. Collectively, this lncRNA-based signature might facilitate individualized treatment decisions and postoperative counseling, ultimately contributing to improved survival.
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Affiliation(s)
- Ailin Qu
- Department of Clinical Laboratory, Qilu Hospital, Shandong University, Wenhua Xi Road, Jinan, 250012, Shandong Province, People's Republic of China
| | - Qian Wang
- Department of Gastroenterology, Central Hospital, Shandong First Medical University, Jinan, 250011, Shandong Province, People's Republic of China
| | - Qing Chang
- Department of Clinical Laboratory, Qilu Hospital, Shandong University, Wenhua Xi Road, Jinan, 250012, Shandong Province, People's Republic of China
| | - Jingkang Liu
- Department of Gynecology, Qilu Hospital, Shandong University, Jinan, 250012, Shandong Province, People's Republic of China
| | - Yongmei Yang
- Department of Clinical Laboratory, Qilu Hospital, Shandong University, Wenhua Xi Road, Jinan, 250012, Shandong Province, People's Republic of China
| | - Xin Zhang
- Department of Clinical Laboratory, Qilu Hospital, Shandong University, Wenhua Xi Road, Jinan, 250012, Shandong Province, People's Republic of China
| | - Yanli Zhang
- Department of Clinical Laboratory, Shandong Provincial Third Hospital, Jinan, 250031, Shandong Province, People's Republic of China
| | - Xiaoshi Zhang
- Department of Clinical Laboratory, Qilu Hospital, Shandong University, Wenhua Xi Road, Jinan, 250012, Shandong Province, People's Republic of China
| | - Hongchun Wang
- Department of Clinical Laboratory, Qilu Hospital, Shandong University, Wenhua Xi Road, Jinan, 250012, Shandong Province, People's Republic of China.
| | - Yi Zhang
- Department of Clinical Laboratory, Qilu Hospital, Shandong University, Wenhua Xi Road, Jinan, 250012, Shandong Province, People's Republic of China.
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Tralongo P, Cappuccio F, Gori S, Donato V, Beretta G, Elia A, Romano F, Iacono M, Tralongo AC, Bordonaro S, Di Mari A, Giuliano SR, Buccafusca G, Careri MC, Santoro A. Clinicians' and Patients' Perceptions and Use of the Word "Cured" in Cancer Care: An Italian Survey. Curr Oncol 2023; 30:1332-1353. [PMID: 36826064 PMCID: PMC9955737 DOI: 10.3390/curroncol30020103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/13/2023] [Accepted: 01/14/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The words "hope" and "cure" were used in a greater number of articles and sentences in narrative and editorial papers than in primary research. Despite concomitant improvements in cancer outcomes, the related reluctance to use these terms in more scientifically oriented original reports may reflect a bias worthy of future exploration. This study aims to survey a group of physicians and cancer patients regarding their perception and use of the word cure. MATERIALS AND METHOD An anonymous online and print survey was conducted to explore Italian clinicians' (the sample includes medical oncologists, radiotherapists, and oncological surgeons) and cancer patients' approach to the perception and use of the word "cure" in cancer care. The participants received an email informing them of the study's purpose and were invited to participate in the survey via a linked form. A portion, two-thirds, of questionnaires were also administered to patients in the traditional paper form. RESULTS The survey was completed by 224 clinicians (54 oncologists, 78 radiotherapists, and 92 cancer surgeons) and 249 patients. The results indicate a favourable attitude for patients in favour of a new language ("cured" vs. "complete remission") of the disease experience. CONCLUSIONS The use of the word cured is substantially accepted and equally shared by doctors and patients. Its use can facilitate the elimination of metaphoric implications and toxic cancer-related connotations registered in all cultures that discourage patients from viewing cancer as a disease with varied outcomes, including cure.
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Affiliation(s)
- Paolo Tralongo
- Medical Oncology Unit, Medical Oncology Department, Umberto I Hospital, RAO, 96011 Siracusa, Italy
- Correspondence:
| | - Francesco Cappuccio
- Medical Oncology Unit, Medical Oncology Department, Umberto I Hospital, RAO, 96011 Siracusa, Italy
| | - Stefania Gori
- Medical Oncology Unit, IRCCS Sacro Cuore Don Calabria, Negrar di Valpolicella, 37024 Verona, Italy
| | - Vittorio Donato
- Radiotherapy Unit, San Camillo Forlanini Hospital, 00152 Rome, Italy
| | - Giordano Beretta
- Medical Oncology Unit, Santo Spirito Hospital, 65124 Pescara, Italy
| | - Ausilia Elia
- Medical Oncology Unit, Medical Oncology Department, Umberto I Hospital, RAO, 96011 Siracusa, Italy
| | - Fabrizio Romano
- Medical Oncology Unit, Medical Oncology Department, Umberto I Hospital, RAO, 96011 Siracusa, Italy
| | - Margherita Iacono
- Medical Oncology Unit, Medical Oncology Department, Umberto I Hospital, RAO, 96011 Siracusa, Italy
| | | | - Sebastiano Bordonaro
- Medical Oncology Unit, Medical Oncology Department, Umberto I Hospital, RAO, 96011 Siracusa, Italy
| | - Annamaria Di Mari
- Medical Oncology Unit, Medical Oncology Department, Umberto I Hospital, RAO, 96011 Siracusa, Italy
| | | | - Gabriella Buccafusca
- Medical Oncology Unit, Medical Oncology Department, Umberto I Hospital, RAO, 96011 Siracusa, Italy
| | - Maria Carmela Careri
- Medical Oncology Unit, Medical Oncology Department, Umberto I Hospital, RAO, 96011 Siracusa, Italy
| | - Armando Santoro
- Department of Biomedical Sciences, Humanitas University, IRCCS Humanitas Research Hospital and Humanitas Cancer Center, 20089 Milan, Italy
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Ruan GT, Song MM, Zhang KP, Xie HL, Zhang Q, Zhang X, Tang M, Zhang XW, Ge YZ, Yang M, Zhu LC, Shi HP. A novel nutrition-related nomogram for the survival prediction of colorectal cancer-results from a multicenter study. Nutr Metab (Lond) 2023; 20:2. [PMID: 36600242 DOI: 10.1186/s12986-022-00719-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 12/18/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Precisely predicting the short- and long-term survival of patients with cancer is important. The tumor-node-metastasis (TNM) stage can accurately predict the long-term, but not short-term, survival of cancer. Nutritional status can affect the individual status and short-term outcomes of patients with cancer. Our hypothesis was that incorporating TNM stage and nutrition-related factors into one nomogram improves the survival prediction for patients with colorectal cancer (CRC). METHOD This multicenter prospective primary cohort included 1373 patients with CRC, and the internal validation cohort enrolled 409 patients with CRC. Least absolute shrinkage and selection operator regression analyses were used to select prognostic indicators and develop a nomogram. The concordance (C)-index, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to assess the prognostic discriminative ability of the nomogram, TNM stage, Patient-Generated Subjective Global Assessment (PGSGA), and TNM stage + PGSGA models. The overall survival (OS) curve of risk group stratification was calculated based on the nomogram risk score. RESULTS TNM stage, radical resection, reduced food intake, activities and function declined, and albumin were selected to develop the nomogram. The C-index and calibration plots of the nomogram showed good discrimination and consistency for CRC. Additionally, the ROC curves and DCA of the nomogram showed better survival prediction abilities in CRC than the other models. The stratification curves of the different risk groups of the different TNM categories were significantly different. CONCLUSION The novel nomogram showed good short- and long-term outcomes of OS in patients with CRC. This model provides a personalized and convenient prognostic prediction tool for clinical applications.
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Affiliation(s)
- Guo-Tian Ruan
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, China.,Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, China
| | - Meng-Meng Song
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, China.,Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, China
| | - Kang-Ping Zhang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, China.,Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, China
| | - Hai-Lun Xie
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, China.,Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, China
| | - Qi Zhang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, China.,Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, China
| | - Xi Zhang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, China.,Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, China
| | - Meng Tang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, China.,Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, China
| | - Xiao-Wei Zhang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, China.,Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, China
| | - Yi-Zhong Ge
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, China.,Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, China
| | - Ming Yang
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, China.,Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, China
| | - Li-Chen Zhu
- Department of Immunology, School of Preclinical Medicine, Guangxi Medical University, Nanning, China
| | - Han-Ping Shi
- Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, China. .,Key Laboratory of Cancer FSMP for State Market Regulation, Beijing, 100038, China.
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Viñal D, Martinez-Recio S, Martinez-Perez D, Ruiz-Gutierrez I, Jimenez-Bou D, Peña-Lopez J, Alameda-Guijarro M, Martin-Montalvo G, Rueda-Lara A, Gutierrez-Sainz L, Palacios ME, Custodio AB, Ghanem I, Feliu J, Rodríguez-Salas N. Clinical Score to Predict Recurrence in Patients with Stage II and Stage III Colon Cancer. Cancers (Basel) 2022; 14:cancers14235891. [PMID: 36497373 PMCID: PMC9735724 DOI: 10.3390/cancers14235891] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 11/11/2022] [Accepted: 11/21/2022] [Indexed: 12/03/2022] Open
Abstract
Background: The prognosis of patients with stage II and stage III colon cancer is heterogeneous. Clinical and pathological characteristics, such as tumor budding, may help to further refine the recurrence risk. Methods: We included all the patients with localized colon cancer at Hospital Universitario La Paz from October 2016 to October 2021. We built a prognostic score for recurrence in the training cohort based on multivariate cox regression analysis and categorized the patients into two risk groups. Results: A total of 440 patients were included in the training cohort. After a median follow-up of 45 months, 81 (18%) patients had a first tumor recurrence. T4, N2, and high tumor budding remained with a p value <0.05 at the last step of the multivariate cox regression model for time to recurrence (TTR). We assigned 2 points to T4 and 1 point to N2 and high tumor budding. Forty-five percent of the patients were assigned to the low-risk group (score = 0). Compared to the high-risk group (score 1−4), patients in the low-risk group had a significantly longer TTR (hazard ratio for disease recurrence of 0.14 (95%CI: 0.00 to 0.90; p < 0.045)). The results were confirmed in the validation cohort. Conclusions: In our study, we built a simple score to predict tumor recurrence based on T4, N2, and high tumor budding. Patients in the low-risk group, that comprised 44% of the cohort, had an excellent prognosis.
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Affiliation(s)
- David Viñal
- Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain
| | | | | | - Iciar Ruiz-Gutierrez
- Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain
| | - Diego Jimenez-Bou
- Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain
| | - Jesús Peña-Lopez
- Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain
| | | | - Gema Martin-Montalvo
- Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain
| | - Antonio Rueda-Lara
- Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain
| | | | | | - Ana Belén Custodio
- Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain
| | - Ismael Ghanem
- Department of Medical Oncology, Hospital Universitario La Paz, 28046 Madrid, Spain
| | - Jaime Feliu
- Department of Medical Oncology, Hospital Universitario La Paz, IdiPAZ, Catedra UAM-AMGEN, CIBERONC, 28046 Madrid, Spain
- Correspondence:
| | - Nuria Rodríguez-Salas
- Department of Medical Oncology, Hospital Universitario La Paz, IdiPAZ, CIBERONC, 28046 Madrid, Spain
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Calderillo Ruiz G, Lopez Basave H, Vazquez Renteria RS, Castillo Morales A, Guijosa A, Castillo Morales C, Herrera M, Diaz C, Vazquez Cortes E, Ruiz-Garcia E, Munoz Montano WR. The Prognostic Significance of HALP Index for Colon Cancer Patients in a Hispanic-Based Population. JOURNAL OF ONCOLOGY 2022; 2022:4324635. [PMID: 36467502 PMCID: PMC9711950 DOI: 10.1155/2022/4324635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 10/11/2022] [Accepted: 10/17/2022] [Indexed: 10/04/2023]
Abstract
Background Survival and recurrence rates following locoregional colon cancer surgical resection are highly variable. Currently used tools to assess patient risk are still imperfect. In the present work, we evaluate, for the first time, the prognostic value of the recently developed HALP (hemoglobin, albumin, lymphocyte, and platelet) index in Hispanic colon cancer patients. Patients and Methods. We conducted a retrospective cohort study in Mexican patients with a nonmetastatic colon cancer diagnosis who underwent surgical resection. We determined the preoperative HALP score optimal cut-off value by using the X-tile software. We plotted survival curves using the Kaplan-Meier method and performed a multivariate Cox regression analysis to explore the association of preoperative HALP score with two primary endpoints: overall survival (OS) and disease-free survival (DFS). Results We included 640 patients (49.8% female). The optimal HALP cut-off value was 15.0. A low HALP index was statistically significantly associated with a higher TNM stage. Low HALP score was statistically significantly associated with shorter median OS in the Kaplan-Meier analysis (73.5 vs. 84.8 months) and in the multivariate Cox regression analysis (HR = 1.942, 95% CI = 1.647-2.875). There was no significant association between the HALP score and DFS. Conclusions Our findings show that the HALP index is an independent factor associated with survival in Hispanic patients, despite recurrence. It seems to reflect both the anatomical extent of the disease and traditionally unaccounted nutritional and inflammatory factors that are significant for prognosis.
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Affiliation(s)
| | - Horacio Lopez Basave
- Gastrointestinal Oncology Unit, Instituto Nacional de Cancerologia, Mexico, Mexico
| | | | | | - Alberto Guijosa
- School of Medicine, Universidad Panamericana, Mexico, Mexico
| | | | - Marytere Herrera
- Gastrointestinal Oncology Unit, Instituto Nacional de Cancerologia, Mexico, Mexico
| | - Consuelo Diaz
- Gastrointestinal Oncology Unit, Instituto Nacional de Cancerologia, Mexico, Mexico
| | | | - Erika Ruiz-Garcia
- Gastrointestinal Oncology Unit, Instituto Nacional de Cancerologia, Mexico, Mexico
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Tang M, Gao L, He B, Yang Y. Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China. Front Oncol 2022; 12:1044344. [PMID: 36465374 PMCID: PMC9714626 DOI: 10.3389/fonc.2022.1044344] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 10/31/2022] [Indexed: 06/30/2024] Open
Abstract
Background To construct prognostic model of colorectal cancer (CRC) recurrence and metastasis (R&M) with traditional Chinese medicine (TCM) factors based on different machine learning (ML) methods. Aiming to offset the defects in the existing model lacking TCM factors. Methods Patients with stage I-III CRC after radical resection were included as the model data set. The training set and the internal verification set were randomly divided at a ratio of 7: 3 by the "set aside method". The average performance index and 95% confidence interval of the model were calculated by repeating 100 tests. Eight factors were used as predictors of Western medicine. Two types of models were constructed by taking "whether to accept TCM intervention" and "different TCM syndrome types" as TCM predictors. The model was constructed by four ML methods: logistic regression, random forest, Extreme Gradient Boosting (XGBoost) and support vector machine (SVM). The predicted target was whether R&M would occur within 3 years and 5 years after radical surgery. The area under curve (AUC) value and decision curve analysis (DCA) curve were used to evaluate accuracy and utility of the model. Results The model data set consisted of 558 patients, of which 317 received TCM intervention after radical resection. The model based on the four ML methods with the TCM factor of "whether to accept TCM intervention" showed good ability in predicting R&M within 3 years and 5 years (AUC value > 0.75), and XGBoost was the best method. The DCA indicated that when the R&M probability in patients was at a certain threshold, the models provided additional clinical benefits. When predicting the R&M probability within 3 years and 5 years in the model with TCM factors of "different TCM syndrome types", the four methods all showed certain predictive ability (AUC value > 0.70). With the exception of the model constructed by SVM, the other methods provided additional clinical benefits within a certain probability threshold. Conclusion The prognostic model based on ML methods shows good accuracy and clinical utility. It can quantify the influence degree of TCM factors on R&M, and provide certain values for clinical decision-making.
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Affiliation(s)
- Mo Tang
- Oncology Department, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Lihao Gao
- Smart City Business Unit, Baidu Inc., Beijing, China
| | - Bin He
- Oncology Department, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Yufei Yang
- Oncology Department, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
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Veenstra CM, Ellis KR, Abrahamse P, Ward KC, Morris AM, Hawley ST. A dyadic survey study of partner engagement in and patient receipt of guideline-recommended colorectal cancer surveillance. BMC Cancer 2022; 22:1060. [PMID: 36229796 PMCID: PMC9559022 DOI: 10.1186/s12885-022-10131-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 09/21/2022] [Indexed: 11/25/2022] Open
Abstract
Background We investigated whether partner (spouse or intimate partner) engagement in colorectal cancer (CRC) surveillance is associated with patient receipt of surveillance. Methods From 2019 to 2020 we surveyed Stage III CRC survivors diagnosed 2014–2018 at an academic cancer center, a community oncology practice and the Georgia SEER registry, and their partners. Partner engagement was measured across 3 domains: Informed about; Involved in; and Aware of patient preferences around surveillance. We evaluated bivariate associations between domains of partner engagement and independent partner variables. Analysis of variance and multivariable logistic regression were used to compare domains of engagement with patient-reported receipt of surveillance. Results 501 patients responded (51% response rate); 428 had partners. 311 partners responded (73% response rate). Partners were engaged across all domains. Engagement varied by sociodemographics. Greater partner involvement was associated with decreased odds of receipt of composite surveillance (OR 0.67, 95% CI 0.48–0.93) and trended towards significance for decreased odds of receipt of endoscopy (OR 0.60, 95% CI 0.34–1.03) and CEA (OR 0.75, 95% CI 0.55–1.04). Greater partner awareness was associated with increased odds of patients’ receipt of endoscopy (OR 2.18, 95% CI 1.15–4.12) and trended towards significance for increased odds of receipt of composite surveillance (OR 1.30, 95% CI 0.91–2.04). Conclusion Partners are engaged (informed, involved, and aware) in CRC surveillance. Future research to develop dyadic interventions that capitalize on the positive aspects of partner engagement may help partners effectively engage in surveillance to improve patient care. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10131-3.
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Affiliation(s)
- Christine M Veenstra
- University of Michigan, 300 North Ingalls, NIB, Room 3A22, 48109, Ann Arbor, MI, USA.
| | - Katrina R Ellis
- University of Michigan, 300 North Ingalls, NIB, Room 3A22, 48109, Ann Arbor, MI, USA
| | - Paul Abrahamse
- University of Michigan, 300 North Ingalls, NIB, Room 3A22, 48109, Ann Arbor, MI, USA
| | | | | | - Sarah T Hawley
- University of Michigan, 300 North Ingalls, NIB, Room 3A22, 48109, Ann Arbor, MI, USA
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Wang X, Lu J, Song Z, Zhou Y, Liu T, Zhang D. From past to future: Bibliometric analysis of global research productivity on nomogram (2000-2021). Front Public Health 2022; 10:997713. [PMID: 36203677 PMCID: PMC9530946 DOI: 10.3389/fpubh.2022.997713] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/02/2022] [Indexed: 01/26/2023] Open
Abstract
Background Nomogram, a visual clinical predictive model, provides a scientific basis for clinical decision making. Herein, we investigated 20 years of nomogram research responses, focusing on current and future trends and analytical challenges. Methods We mined data of scientific literature from the Core Collection of Web of Science, searching for the original articles with title "Nomogram*/Parton Table*/Parton Nomogram*", published within January 1st, 2000 to December 30th, 2021. Data records were validated using HistCite Version and analyzed with a transformable statistical method, the Bibliometrix 3.0 package of R Studio. Results In total, 4,176 original articles written by 19,158 authors were included from 915 sources. Annually, Nomogram publications are continually produced, which have rapidly grown since 2018. China published the most articles; however, its total citations ranked second after the United States. Both total citations and average article citations in the United States rank first globally, and a high degree of cooperation exists between countries. Frontiers in Oncology published the most papers (238); this number has grown rapidly since 2019. Journal of Urology had the highest H-index, with an average increase in publications over the past 20 years. Most research topics were tumor-related, among which tumor risk prediction and prognostic evaluation were the main contents. Research on prognostic assessment is more published and advanced, while risk prediction and diagnosis have good developmental prospects. Furthermore, nomogram of the urinary system has been highly developed. Following advancements in nomogram modeling, it has recently been applied to non-oncological subjects. Conclusion This bibliometric analysis provides a comprehensive overview of the current nomogram status, which could enable better understanding of its development over the years, and provide global researchers a comprehensive analysis and structured information to help identify hot spots and gaps in future research.
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Affiliation(s)
- Xiaoxue Wang
- Department of Health Management, Shengjing Hospital of China Medical University, Shenyang, China
| | - Jingliang Lu
- Lanzhou Information Center, Chinese Academy of Sciences, Lanzhou, China
| | - Zixuan Song
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yangzi Zhou
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Tong Liu
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China,Tong Liu
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China,*Correspondence: Dandan Zhang
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Weiser MR, Chou JF, Kim JK, Widmar M, Wei IH, Pappou EP, Smith JJ, Nash GM, Paty PB, Cercek A, Saltz LB, Romesser PB, Crane CH, Garcia-Aguilar J, Schrag D, Gönen M. A Dynamic Clinical Calculator for Estimating Conditional Recurrence-Free Survival After Total Neoadjuvant Therapy for Rectal Cancer and Either Surgery or Watch-and-Wait Management. JAMA Netw Open 2022; 5:e2233859. [PMID: 36173634 PMCID: PMC9523500 DOI: 10.1001/jamanetworkopen.2022.33859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE The risk of recurrence in patients with locally advanced rectal cancer has historically been determined after surgery, relying on pathologic variables. A growing number of patients are being treated without surgery, and their risk of recurrence needs to be calculated differently. OBJECTIVE To develop a dynamic calculator for estimating the probability of recurrence-free survival (RFS) in patients with rectal cancer who undergo total neoadjuvant therapy (TNT) (induction systemic chemotherapy and chemoradiotherapy) and either surgery or watch-and-wait management. DESIGN, SETTING, AND PARTICIPANTS This cohort study included patients who presented with stage II or III rectal cancer between June 1, 2009, and March 1, 2015, at a comprehensive cancer center. Conditional modeling was incorporated into a previously validated clinical calculator to allow the probability of RFS to be updated based on whether the patient remained in watch-and-wait management or underwent delayed surgery. Data were analyzed from November 2021 to March 2022. EXPOSURE TNT followed by immediate surgery or watch-and-wait management with the possibility of delayed surgery. MAIN OUTCOMES AND MEASURES RFS, concordance index, calibration curves. RESULTS Of the 302 patients in the cohort, 204 (68%) underwent surgery within 3 months from TNT completion (median [range] age, 51 [22-82] years; 78 [38%] women), 54 (18%) underwent surgery more than 3 months from TNT completion (ie, delayed surgery; median [range] age, 62 [31-87] years; 30 [56%] female), and 44 (14%) remained in watch-and-wait management as of April 21, 2021 (median [range] age, 58 [32-89] years; 16 [36%] women). Among patients who initially opted for watch-and-wait management, migration to surgery due to regrowth or patient choice occurred mostly within the first year following completion of TNT, and RFS did not differ significantly whether surgery was performed 3.0 to 5.9 months (73%; 95% CI, 52%-92%) vs 6.0 to 11.9 months (71%; 95% CI, 51%-99%) vs more than 12.0 months (70%; 95% CI, 49%-100%) from TNT completion (P = .70). RFS for patients in the watch-and-wait cohort at 12 months from completion of TNT more closely resembled patients who had undergone surgery and had a pathologic complete response than the watch-and-wait cohort at 3 months from completion of TNT. Accordingly, model performance improved over time, and the concordance index increased from 0.62 (95% CI, 0.53-0.71) at 3 months after TNT to 0.66 (95% CI, 0-0.75) at 12 months. CONCLUSIONS AND RELEVANCE In this cohort study of patients with rectal cancer, the clinical calculator reliably estimated the likelihood of RFS for patients who underwent surgery immediately after TNT, patients who underwent delayed surgery after entering watch-and-wait management, and patients who remained in watch-and-wait management. Delayed surgery following attempted watch-and-wait did not appear to compromise oncologic outcomes. The risk calculator provided conditional survival estimates at any time during surveillance and could help physicians counsel patients with rectal cancer about the consequences of alternative treatment pathways and thereby support informed decisions that incorporate patients' preferences.
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Affiliation(s)
- Martin R. Weiser
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Joanne F. Chou
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jin K. Kim
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Maria Widmar
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Iris H. Wei
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Emmanouil P. Pappou
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - J. Joshua Smith
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Garrett M. Nash
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Philip B. Paty
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Andrea Cercek
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Leonard B. Saltz
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Paul B. Romesser
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Christopher H. Crane
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Julio Garcia-Aguilar
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Deborah Schrag
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mithat Gönen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
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Zhou Y, Lin C, Zhu L, Zhang R, Cheng L, Chang Y. Nomograms and scoring system for forecasting overall and cancer-specific survival of patients with prostate cancer. Cancer Med 2022; 12:2600-2613. [PMID: 35993499 PMCID: PMC9939188 DOI: 10.1002/cam4.5137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 07/11/2022] [Accepted: 08/02/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Estimated life expectancy is one of the most important factors in determining treatment options for prostate cancer (PCa) patients. However, clinicians have few effective prognostic tools to individually assess survival in patients with PCa. METHODS We screened 283,252 patients diagnosed with PCa from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015, and randomly divided them into the training and validation groups. We used univariate and multivariate Cox analyses to identify independent prognostic factors and further established nomograms to predict 1-, 3-, 5-, and 10-year overall survival (OS) and cancer-specific survival (CSS) for PCa patients. The prediction performance of nomograms was tested and externally validated by Concordance index (C-index) and receiver operating characteristic (ROC) curve. Calibration curve and decision curve analysis (DCA) were used for internal validation. We further developed PCa prognostic scoring system based on the impact of available variables on survival. RESULTS The variables age, race, marital status, TNM stage, surgery method, radiotherapy, chemotherapy, PSA value, and Gleason score identified as independent prognostic factors were included in the survival nomograms. The results of training (C-index: OS = 0.776, CSS = 0.889; AUC value: OS = 0.772-0.802, CSS = 0.892-0.936) and external validation (C-index: OS = 0.759, CSS = 0.875) indicated our nomograms had good performance in predicting 1-, 3-, 5-, and 10-year OS and CSS prediction. Internal validation using the calibration curves and DCA curves demonstrated the effectiveness of the prediction models. The prognostic scoring system was more effective than the AJCC staging system in predicting the survival of PCa patients, especially for OS. CONCLUSION The prognostic nomograms and prognostic scoring system have favorable performance in predicting OS and CSS of PCa patients. These individualized survival prediction tools may contribute to clinical decisions.
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Affiliation(s)
- Yuan Zhou
- Department of Urology SurgeryThe People's Hospital of Xuancheng CityXuanchengChina,Wannan Medical CollegeWuhuChina
| | - Changming Lin
- Department of Urology SurgeryThe Fourth Affiliated Hospital of AnHui Medical UniversityHefeiChina
| | - Lian Zhu
- Department of Urology SurgeryThe People's Hospital of Xuancheng CityXuanchengChina,Wannan Medical CollegeWuhuChina
| | - Rentao Zhang
- Department of Urology SurgeryThe People's Hospital of Xuancheng CityXuanchengChina,Wannan Medical CollegeWuhuChina
| | - Lei Cheng
- Department of Pulmonary MedicineShanghai Chest Hospital, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yuanyuan Chang
- Department of Pulmonary MedicineShanghai Chest Hospital, Shanghai Jiao Tong UniversityShanghaiChina
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Awidi M, Bagga A. Artificial intelligence and machine learning in colorectal cancer. Artif Intell Gastrointest Endosc 2022; 3:31-43. [DOI: 10.37126/aige.v3.i3.31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/24/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
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Lin A, Qi C, Li M, Guan R, Imyanitov EN, Mitiushkina NV, Cheng Q, Liu Z, Wang X, Lyu Q, Zhang J, Luo P. Deep Learning Analysis of the Adipose Tissue and the Prediction of Prognosis in Colorectal Cancer. Front Nutr 2022; 9:869263. [PMID: 35634419 PMCID: PMC9131178 DOI: 10.3389/fnut.2022.869263] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/11/2022] [Indexed: 11/18/2022] Open
Abstract
Research has shown that the lipid microenvironment surrounding colorectal cancer (CRC) is closely associated with the occurrence, development, and metastasis of CRC. According to pathological images from the National Center for Tumor diseases (NCT), the University Medical Center Mannheim (UMM) database and the ImageNet data set, a model called VGG19 was pre-trained. A deep convolutional neural network (CNN), VGG19CRC, was trained by the migration learning method. According to the VGG19CRC model, adipose tissue scores were calculated for TCGA-CRC hematoxylin and eosin (H&E) images and images from patients at Zhujiang Hospital of Southern Medical University and First People's Hospital of Chenzhou. Kaplan-Meier (KM) analysis was used to compare the overall survival (OS) of patients. The XCell and MCP-Counter algorithms were used to evaluate the immune cell scores of the patients. Gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) were used to analyze upregulated and downregulated pathways. In TCGA-CRC, patients with high-adipocytes (high-ADI) CRC had significantly shorter OS times than those with low-ADI CRC. In a validation queue from Zhujiang Hospital of Southern Medical University (Local-CRC1), patients with high-ADI had worse OS than CRC patients with low-ADI. In another validation queue from First People's Hospital of Chenzhou (Local-CRC2), patients with low-ADI CRC had significantly longer OS than patients with high-ADI CRC. We developed a deep convolution network to segment various tissues from pathological H&E images of CRC and automatically quantify ADI. This allowed us to further analyze and predict the survival of CRC patients according to information from their segmented pathological tissue images, such as tissue components and the tumor microenvironment.
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Affiliation(s)
- Anqi Lin
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Chang Qi
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Mujiao Li
- College of Biomedical Engineering, Southern Medical University, Guangzhou, China
- Department of Information, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Rui Guan
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Evgeny N. Imyanitov
- Department of Tumor Growth Biology, N.N. Petrov Institute of Oncology, St. Petersburg, Russia
| | - Natalia V. Mitiushkina
- Department of Tumor Growth Biology, N.N. Petrov Institute of Oncology, St. Petersburg, Russia
| | - Quan Cheng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Zaoqu Liu
- Department of Interventional Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaojun Wang
- First People's Hospital of Chenzhou City, Chenzhou, China
| | - Qingwen Lyu
- Department of Information, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- *Correspondence: Qingwen Lyu
| | - Jian Zhang
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Jian Zhang
| | - Peng Luo
- Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Peng Luo
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Mazaki J, Katsumata K, Tago T, Kasahara K, Enomoto M, Ishizaki T, Nagakawa Y, Tsuchida A. Novel and Simple Nomograms Using Inflammation and Nutritional Biomarkers for Stage II–III Colon Cancer, Taking “Time after Curative Surgery” into Consideration. Nutr Cancer 2022; 74:2875-2886. [DOI: 10.1080/01635581.2022.2042570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Junichi Mazaki
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Kenji Katsumata
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Tomoya Tago
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Kenta Kasahara
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Masanobu Enomoto
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Tetsuo Ishizaki
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Yuichi Nagakawa
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
| | - Akihiko Tsuchida
- Department of Gastrointestinal and Pediatric Surgery, Tokyo Medical University, Tokyo, Japan
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Identification of patients with ductal carcinoma in situ at high risk of postoperative upstaging: A comprehensive review and an external (un)validation of predictive models developed. Eur J Obstet Gynecol Reprod Biol 2022; 271:7-14. [DOI: 10.1016/j.ejogrb.2022.01.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 12/30/2021] [Accepted: 01/27/2022] [Indexed: 12/17/2022]
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Qiu H, Ding S, Liu J, Wang L, Wang X. Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer. Curr Oncol 2022; 29:1773-1795. [PMID: 35323346 PMCID: PMC8947571 DOI: 10.3390/curroncol29030146] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/28/2022] [Accepted: 03/03/2022] [Indexed: 12/29/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.
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Affiliation(s)
- Hang Qiu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
- Correspondence: (H.Q.); (X.W.)
| | - Shuhan Ding
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA;
| | - Jianbo Liu
- West China School of Medicine, Sichuan University, Chengdu 610041, China;
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Liya Wang
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Xiaodong Wang
- West China School of Medicine, Sichuan University, Chengdu 610041, China;
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Correspondence: (H.Q.); (X.W.)
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Cheng E, Ou FS, Ma C, Spiegelman D, Zhang S, Zhou X, Bainter TM, Saltz LB, Niedzwiecki D, Mayer RJ, Whittom R, Hantel A, Benson A, Atienza D, Messino M, Kindler H, Giovannucci EL, Van Blarigan EL, Brown JC, Ng K, Gross CP, Meyerhardt JA, Fuchs CS. Diet- and Lifestyle-Based Prediction Models to Estimate Cancer Recurrence and Death in Patients With Stage III Colon Cancer (CALGB 89803/Alliance). J Clin Oncol 2022; 40:740-751. [PMID: 34995084 PMCID: PMC8887946 DOI: 10.1200/jco.21.01784] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 10/08/2021] [Accepted: 12/06/2021] [Indexed: 12/12/2022] Open
Abstract
PURPOSE Current tools in predicting survival outcomes for patients with colon cancer predominantly rely on clinical and pathologic characteristics, but increasing evidence suggests that diet and lifestyle habits are associated with patient outcomes and should be considered to enhance model accuracy. METHODS Using an adjuvant chemotherapy trial for stage III colon cancer (CALGB 89803), we developed prediction models of disease-free survival (DFS) and overall survival by additionally incorporating self-reported nine diet and lifestyle factors. Both models were assessed by multivariable Cox proportional hazards regression and externally validated using another trial for stage III colon cancer (CALGB/SWOG 80702), and visual nomograms of prediction models were constructed accordingly. We also proposed three hypothetical scenarios for patients with (1) good-risk, (2) average-risk, and (3) poor-risk clinical and pathologic features, and estimated their predictive survival by considering clinical and pathologic features with or without adding self-reported diet and lifestyle factors. RESULTS Among 1,024 patients (median age 60.0 years, 43.8% female), we observed 394 DFS events and 311 deaths after median follow-up of 7.3 years. Adding self-reported diet and lifestyle factors to clinical and pathologic characteristics meaningfully improved performance of prediction models (c-index from 0.64 [95% CI, 0.62 to 0.67] to 0.69 [95% CI, 0.67 to 0.72] for DFS, and from 0.67 [95% CI, 0.64 to 0.70] to 0.71 [95% CI, 0.69 to 0.75] for overall survival). External validation also indicated good performance of discrimination and calibration. Adding most self-reported favorable diet and lifestyle exposures to multivariate modeling improved 5-year DFS of all patients and by 6.3% for good-risk, 21.4% for average-risk, and 42.6% for poor-risk clinical and pathologic features. CONCLUSION Diet and lifestyle factors further inform current recurrence and survival prediction models for patients with stage III colon cancer.
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Affiliation(s)
- En Cheng
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT
- Division of Research, Kaiser Permanente Northern California, Oakland, CA
| | - Fang-Shu Ou
- Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, MN
| | - Chao Ma
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Donna Spiegelman
- Department of Biostatistics, Yale School of Public Health, New Haven, CT
- Center on Methods for Implementation and Prevention Science, Yale School of Public Health, New Haven, CT
| | - Sui Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Xin Zhou
- Department of Biostatistics, Yale School of Public Health, New Haven, CT
- Center on Methods for Implementation and Prevention Science, Yale School of Public Health, New Haven, CT
| | - Tiffany M. Bainter
- Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, MN
| | | | - Donna Niedzwiecki
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC
| | - Robert J. Mayer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Renaud Whittom
- Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | | | - Al Benson
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL
| | | | | | | | - Edward L. Giovannucci
- Department of Epidemiology, and Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Erin L. Van Blarigan
- Department of Epidemiology and Biostatistics, and Urology, University of California, San Francisco, CA
| | - Justin C. Brown
- Cancer Metabolism Program, Pennington Biomedical Research Center, Baton Rouge, LA
| | - Kimmie Ng
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Cary P. Gross
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT
- Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Cancer Outcomes, Public Policy, and Effectiveness Research Center, Yale Cancer Center, New Haven, CT
| | | | - Charles S. Fuchs
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT
- Division of Hematology and Medical Oncology, Department of Internal Medicine, Yale School of Medicine, New Haven, CT
- Yale Cancer Center, Smilow Cancer Hospital, New Haven, CT
- Hematology and Oncology Product Development, Genentech & Roche, South San Francisco, CA
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A Prediction Model for Tumor Recurrence in Stage II–III Colorectal Cancer Patients: From a Machine Learning Model to Genomic Profiling. Biomedicines 2022; 10:biomedicines10020340. [PMID: 35203549 PMCID: PMC8961774 DOI: 10.3390/biomedicines10020340] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/27/2022] [Accepted: 01/28/2022] [Indexed: 01/27/2023] Open
Abstract
Background: Colorectal cancer (CRC) is one of the most prevalent malignant diseases worldwide. Risk prediction for tumor recurrence is important for making effective treatment decisions and for the survival outcomes of patients with CRC after surgery. Herein, we aimed to explore a prediction algorithm and the risk factors for postoperative tumor recurrence using a machine learning (ML) approach with standardized pathology reports for patients with stage II and III CRC. Methods: Pertinent clinicopathological features were compiled from medical records and standardized pathology reports of patients with stage II and III CRC. Four ML models based on logistic regression (LR), random forest (RF), classification and regression decision trees (CARTs), and support vector machine (SVM) were applied for the development of the prediction algorithm. The area under the curve (AUC) of the ML models was determined in order to compare the prediction accuracy. Genomic studies were performed using a panel-targeted next-generation sequencing approach. Results: A total of 1073 patients who received curative intent surgery at the National Cheng Kung University Hospital between January 2004 and January 2019 were included. Based on conventional statistical methods, chemotherapy (p = 0.003), endophytic tumor configuration (p = 0.008), TNM stage III disease (p < 0.001), pT4 (p < 0.001), pN2 (p < 0.001), increased numbers of lymph node metastases (p < 0.001), higher lymph node ratios (LNR) (p < 0.001), lymphovascular invasion (p < 0.001), perineural invasion (p < 0.001), tumor budding (p = 0.004), and neoadjuvant chemoradiotherapy (p = 0.025) were found to be correlated with the tumor recurrence of patients with stage II–III CRC. While comparing the performance of different ML models for predicting cancer recurrence, the AUCs for LR, RF, CART, and SVM were found to be 0.678, 0.639, 0.593, and 0.581, respectively. The LR model had a better accuracy value of 0.87 and a specificity value of 1 in the testing set. Two prognostic factors, age and LNR, were selected by multivariable analysis and the four ML models. In terms of age, older patients received fewer cycles of chemotherapy and radiotherapy (p < 0.001). Right-sided colon tumors (p = 0.002), larger tumor sizes (p = 0.008) and tumor volumes (p = 0.049), TNM stage II disease (p < 0.001), and advanced pT3–4 stage diseases (p = 0.04) were found to be correlated with the older age of patients. However, pN2 diseases (p = 0.005), lymph node metastasis number (p = 0.001), LNR (p = 0.004), perineural invasion (p = 0.018), and overall survival rate (p < 0.001) were found to be decreased in older patients. Furthermore, PIK3CA and DNMT3A mutations (p = 0.032 and 0.039, respectively) were more frequently found in older patients with stage II–III CRC compared to their younger counterparts. Conclusions: This study demonstrated that ML models have a comparable predictive power for determining cancer recurrence in patients with stage II–III CRC after surgery. Advanced age and high LNR were significant risk factors for cancer recurrence, as determined by ML algorithms and multivariable analyses. Distinctive genomic profiles may contribute to discrete clinical behaviors and survival outcomes between patients of different age groups. Studies incorporating complete molecular and genomic profiles in cancer prediction models are beneficial for patients with stage II–III CRC.
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Zhang TT, Zeng J, Yang Y, Wang JJ, Kang YJ, Zhang DH, Liu XZ, Chen K, Wang X, Fang Y. A visualized dynamic prediction model for survival of patients with geriatric thyroid cancer: A population-based study. Front Endocrinol (Lausanne) 2022; 13:1038041. [PMID: 36568078 PMCID: PMC9780441 DOI: 10.3389/fendo.2022.1038041] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE Thyroid cancer (TC) is a common malignancy with a poor prognosis with aging. However, no accurate predictive survival model exists for patients with geriatric TC.We aimed to establish prediction models of prognosis in elderly TC. METHODS We retrospectively reviewed the clinicopathology characteristics of patients with geriatric TC in the Surveillance, Epidemiology, and End Results database (SEER) from 2004 to 2018. The risk predictors used to build the nomograms were derived from the Cox proportional risk regression. These nomograms were used to predict 1-, 3-, and 5-year overall survival and cancer-specific survival in elderly patients with TC. The accuracy and discriminability of the new model were evaluated by the consistency index (C-index) and calibration curve. The clinical applicability value of the model was assessed using the decision curve analysis. RESULTS We used the SEER database to include 16475 patients with geriatric TC diagnosed from 2004 to 2018. The patients from 2004 to 2015 were randomly sorted out on a scale of 7:3. They were classified into a training group (n = 8623) and a validation group (n = 3669). Patients with TC diagnosed in 2016-2018 were classified into external validation groups (n = 4183). The overall survival nomogram consisted of 10 variables (age, gender, marital status, histologic type, grade, TNM stage, surgery status, and tumor size). A cancer-specific survival nomogram consisted of eight factors (age, tumor size, grade, histologic type, surgery, and TNM stage). The C-index values for the training, validation, and external validation groups were 0.775 (95% confidence interval [CI] 0.785-0.765), 0.776 (95% CI 0.792-0.760), and 0.895(95% CI 0.873-0.917), respectively. The overall survival was consistent with a nomogram based on the calibration curve. Besides, the decision curve analysis showed excellent clinical application value of the nomogram. Additionally, we found that surgery could improve the prognosis of patients with geriatric at high-risk (P < 0.001) but not those at low-risk (P = 0.069). CONCLUSION This was the first study to construct predictive survival nomograms for patients with geriatric TC. The well-established nomograms and the actual results could guide follow-up management strategies.
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Affiliation(s)
- Ting-ting Zhang
- Department of Endocrinology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jing Zeng
- Department of Endocrinology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yan Yang
- Department of Endocrinology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jin-jing Wang
- Department of Endocrinology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yao-jie Kang
- Department of Endocrinology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Dong-he Zhang
- Department of Day Clinic, The Fifth Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Xiao-zhu Liu
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Kang Chen
- Department of Endocrinology, The First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
- *Correspondence: Kang Chen, ; Xuan Wang, ; Yi Fang,
| | - Xuan Wang
- Department of Endocrinology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
- *Correspondence: Kang Chen, ; Xuan Wang, ; Yi Fang,
| | - Yi Fang
- Department of Endocrinology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
- *Correspondence: Kang Chen, ; Xuan Wang, ; Yi Fang,
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Prognosis Based Definition of Resectability in Pancreatic Cancer: A Road Map to New Guidelines. Ann Surg 2022; 275:175-181. [PMID: 32149822 DOI: 10.1097/sla.0000000000003859] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
OBJECTIVE To identify objective preoperative prognostic factors that are able to predict long-term survival of patients affected by PDAC. SUMMARY OF BACKGROUND DATA In the modern era of improved systemic chemotherapy for PDAC, tumor biology, and response to chemotherapy are essential in defining prognosis and an improved approach is needed for classifying resectability beyond purely anatomic features. METHODS We queried the National Cancer Database regarding patients diagnosed with PDAC from 2010 to 2016. Cox proportional hazard models were used to select preoperative baseline factors significantly associated with survival; final models for overall survival (OS) were internally validated and formed the basis of the nomogram. RESULTS A total of 7849 patients with PDAC were included with a median follow-up of 19 months. On multivariable analysis, factors significantly associated with OS included carbohydrate antigen 19-9, neoadjuvant treatment, tumor size, age, facility type, Charlson/Deyo score, primary site, and sex; T4 stage was not independently associated with OS. The cumulative score was used to classify patients into 3 groups: good, intermediate, and poor prognosis, respectively. The strength of our model was validated by a highly significant randomization test, Log-rank test, and simple hazard ratio; the concordance index was 0.59. CONCLUSION This new PDAC nomogram, based solely on preoperative variables, could be a useful tool to patients and counseling physicians in selecting therapy. This model suggests a new concept of resectability that is meant to reflect the biology of the tumor, thus partially overcoming existing definitions, that are mainly based on tumor anatomic features.
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Ren J, Xu L, Zhou S, Ouyang J, You W, Sheng N, Yan L, Peng D, Xie L, Wang Z. Clinicopathological Features Combined With Immune Infiltration Could Well Distinguish Outcomes in Stage II and Stage III Colorectal Cancer: A Retrospective Study. Front Oncol 2021; 11:776997. [PMID: 34926285 PMCID: PMC8678133 DOI: 10.3389/fonc.2021.776997] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 11/15/2021] [Indexed: 01/01/2023] Open
Abstract
Background The Immunoscore predicts prognosis in patients with colorectal cancer (CRC). However, a few studies have incorporated the Immunoscore into the construction of comprehensive prognostic models in CRC, especially stage II CRC. We aimed to construct and validate multidimensional models integrating clinicopathological characteristics and the Immunoscore to predict the prognosis of patients with stage II–III CRC. Methods Patients (n = 254) diagnosed with stage II–III CRC from 2009 to 2016 were used to generate Cox models for predicting disease-free survival (DFS) and overall survival (OS). The variables included basic clinical indicators, blood inflammatory markers, preoperative tumor biomarkers, mismatch repair status, and the Immunoscore (CD3+ and CD8+ T-cell densities). Univariate and multivariate Cox proportional regressions were used to construct the prognostic models for DFS and OS. We validated the predictive accuracy and ability of the prognostic models in our cohort of 254 patients. Results We constructed two predictive prognostic models with C-index values of 0.6941 for DFS and 0.7138 for OS in patients with stage II–III CRC. The Immunoscore was the most informative predictor of DFS (11.92%), followed by pN stage, carcinoembryonic antigen (CEA), and vascular infiltration. For OS, the Immunoscore was the most informative predictor (8.59%), followed by pN stage, age, CA125, and CEA. Based on the prognostic models, nomograms were developed to predict the 3- and 5-year DFS and OS rates. Patients were divided into three risk groups (low, intermediate, and high) according to the risk scores obtained from the nomogram, and significant differences were observed in the recurrence and survival of the different risk groups (p < 0.0001). Calibration curve and time-dependent receiver operating characteristic (ROC) analysis showed good accuracy of our models. Furthermore, the decision curve analysis indicated that our nomograms had better net benefit than pathological TNM (pTNM) stage within a wide threshold probability. Especially, we developed a website based on our prognostic models to predict the risks of recurrence and death of patients with stage II–III CRC. Conclusions Multidimensional models including the clinicopathological characteristics and the Immunoscore were constructed and validated, with good accuracy and convenience, to evaluate the risks of recurrence and death of stage II–III CRC patients.
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Affiliation(s)
- Jiazi Ren
- Department of General Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Linfeng Xu
- Shanghai Center for Bioinformation Technology, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China.,School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Siyu Zhou
- Department of General Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Jian Ouyang
- Shanghai Center for Bioinformation Technology, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Weiqiang You
- Department of General Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Nengquan Sheng
- Department of General Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Li Yan
- Department of General Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Du Peng
- Department of Colorectal Surgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Xie
- Shanghai Center for Bioinformation Technology, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Zhigang Wang
- Department of General Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
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Chang X, Chen J, Zhang W, Yang J, Yu S, Deng W, Ni W, Zhou Z, Chen D, Feng Q, Lv J, Liang J, Hui Z, Wang L, Lin Y, Chen X, Xue Q, Mao Y, Gao Y, Wang D, Feng F, Gao S, He J, Xiao Z. Recurrence risk stratification based on a competing-risks nomogram to identify patients with esophageal cancer who may benefit from postoperative radiotherapy. Ther Adv Med Oncol 2021; 13:17588359211061948. [PMID: 34987617 PMCID: PMC8721393 DOI: 10.1177/17588359211061948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 11/02/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND A reliable model is needed to estimate the risk of postoperative recurrence and the benefits of postoperative radiotherapy (PORT) in patients with thoracic esophageal squamous cell cancer (TESCC). METHODS The study retrospectively reviewed 3652 TESCC patients in stage IB-IVA after radical esophagectomy, with or without PORT. In one institution as the training cohort (n = 1620), independent risk factors associated with locoregional recurrence (LRR), identified by the competing-risks regression, were used to establish a predicting nomogram, which was validated in an external cohort (n = 1048). Area under curve (AUC) values of receiver operating characteristic curves were calculated to evaluate discrimination. Risk stratification was conducted using a decision tree analysis based on the cumulative point score of the LRR nomogram. After balancing the baseline of characteristics between treatment groups by inverse probability of treatment weighting, the effect of PORT was evaluated in each risk group. RESULTS Sex, age, tumor location, tumor grade, and N category were identified as independent risk factors for LRR and added into the nomogram. The AUC values were 0.638 and 0.706 in the training and validation cohorts, respectively. Three risk groups were established. For patients in the intermediate- and high-risk groups, PORT significantly improved the 5-year overall survival by 10.2% and 9.4%, respectively (p < 0.05). Although PORT was significantly associated with reduced LRR in the low-risk group, overall survival was not improved. CONCLUSION The nomogram can effectively estimate the individual risk of LRR, and patients in the intermediate- and high-risk groups are highly recommended to undergo PORT.
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Affiliation(s)
- Xiao Chang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021 China
| | - Junqiang Chen
- Department of Radiation Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Wencheng Zhang
- Department of Radiation Oncology and Key Laboratory of Cancer Prevention Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
| | - Jinsong Yang
- Department of Radiation Oncology, Union Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
| | - Shufei Yu
- Department of Radiation Oncology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China
| | - Wei Deng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, China
| | - Wenjie Ni
- Department of Radiation Oncology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
| | - Zongmei Zhou
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021 China
| | - Dongfu Chen
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021 China
| | - Qinfu Feng
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021 China
| | - Jima Lv
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021 China
| | - Jun Liang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021 China
| | - Zhouguang Hui
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021 China
| | - Lvhua Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021 China
| | - Yu Lin
- Department of Radiation Oncology, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Xiaohui Chen
- Department of Thoracic Surgery, Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, Fuzhou, China
| | - Qi Xue
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yousheng Mao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yushun Gao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dali Wang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feiyue Feng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shugeng Gao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 South Panjiayuan Lane, Beijing, 100021 China
| | - Zefen Xiao
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 South Panjiayuan Lane, Beijing 100021, 100021 China
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Cai Z, Wang Y, Li L, Wang H, Song C, Yin D, Song W, Dou K. Development and Validation of a Nomogram for Predicting the Risk of Adverse Cardiovascular Events in Patients with Coronary Artery Ectasia. J Cardiovasc Dev Dis 2021; 8:jcdd8120186. [PMID: 34940541 PMCID: PMC8708195 DOI: 10.3390/jcdd8120186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 11/30/2021] [Accepted: 12/09/2021] [Indexed: 12/25/2022] Open
Abstract
Coronary artery ectasia (CAE) is a rare finding and is associated with poor clinical outcomes. However, prognostic factors are not well studied and no prognostication tool is available. In a derivation set comprising 729 consecutive CAE patients between January 2009 and June 2014, a nomogram was developed using Cox regression. Total of 399 patients from July 2014 to December 2015 formed the validation set. The primary outcome was 5-year major adverse cardiovascular events (MACE), a component of cardiovascular death and nonfatal myocardial infarction. Besides the clinical factors, we used quantitative coronary angiography (QCA) and defined QCA classification of four types, according to max diameter (< or ≥5 mm) and max length ratio (ratio of lesion length to vessel length, < or ≥1/3) of the dilated lesion. A total of 27 cardiovascular deaths and 41 nonfatal myocardial infarctions occurred at 5-year follow-up. The nomogram effectively predicted 5-year MACE risk using predictors including age, prior PCI, high sensitivity C-reactive protein, N-terminal pro-brain natriuretic peptide, and QCA classification (area under curve [AUC] 0.75, 95% CI 0.68–0.82 in the derivation set; AUC 0.71, 95% CI 0.56–0.86 in the validation set). Patients were classified as high-risk if prognostic scores were ≥155 and the Kaplan–Meier curves were well separated (log-rank p < 0.001 in both sets). Calibration curve and Hosmer–Lemeshow test indicated similarity between predicted and actual 5-year MACE survival (p = 0.90 in the derivation and p = 0.47 in the validation set). This study developed and validated a simple-to-use method for assessing 5-year MACE risk in patients with CAE.
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Affiliation(s)
- Zhongxing Cai
- Cardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China; (Z.C.); (H.W.); (C.S.); (D.Y.)
| | - Yintang Wang
- Department of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China;
| | - Luqi Li
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100020, China;
| | - Haoyu Wang
- Cardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China; (Z.C.); (H.W.); (C.S.); (D.Y.)
| | - Chenxi Song
- Cardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China; (Z.C.); (H.W.); (C.S.); (D.Y.)
| | - Dong Yin
- Cardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China; (Z.C.); (H.W.); (C.S.); (D.Y.)
| | - Weihua Song
- Cardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China; (Z.C.); (H.W.); (C.S.); (D.Y.)
- Correspondence: (W.S.); (K.D.)
| | - Kefei Dou
- Cardiometabolic Medicine Center, Department of Cardiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China; (Z.C.); (H.W.); (C.S.); (D.Y.)
- Correspondence: (W.S.); (K.D.)
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Luo Z, Fu Z, Li T, Zhang Y, Zhang J, Yang Y, Yang Z, Li Q, Qiu Z, Huang C. Development and Validation of the Individualized Prognostic Nomograms in Patients With Right- and Left-Sided Colon Cancer. Front Oncol 2021; 11:709835. [PMID: 34790565 PMCID: PMC8591050 DOI: 10.3389/fonc.2021.709835] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/24/2021] [Indexed: 12/23/2022] Open
Abstract
Background The overall survival (OS) of patients diagnosed with colon cancer (CC) varied greatly, so did the patients with the same tumor stage. We aimed to design a nomogram that is capable of predicting OS in resected left-sided colon cancers (LSCC) and right-sided colon cancers (RSCC), and thus to stratify patients into different risk groups, respectively. Methods Records from a retrospective cohort of 577 patients with complete information were used to construct the nomogram. Univariate and multivariate analyses screened risk factors associated with overall survival. The performance of the nomogram was evaluated with concordance index (c-index), calibration plots, and decision curve analyses for discrimination, accuracy, calibration ability, and clinical net benefits, respectively, which was further compared with the American Joint Committee on Cancer (AJCC) 8th tumor-node-metastasis (TNM) classification. Risk stratification based on nomogram scores was performed with recursive partitioning analysis. Results The LSCC nomogram incorporated carbohydrate antigen 12-5 (CA12-5), age and log odds of positive lymph nodes (LODDS), and RSCC nomogram enrolled tumor stroma percentage (TSP), age and LODDS. Compared with the TNM classification, the LSCC and RSCC nomograms both had a statistically higher C-index (0.837, 95% CI: 0.827-0.846 and 0.780, 95% CI 0.773-0.787, respectively) and more clinical net benefits, respectively. Calibration plots revealed no deviations from reference lines. All results were reproducible in the validation cohort. Conclusions An original predictive nomogram was constructed and validated for OS in patients with CC after surgery, which had facilitated physicians to appraise the individual survival of postoperative patients accurately and to identify high-risk patients who were in need of more aggressive treatment and follow-up strategies.
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Affiliation(s)
- Zai Luo
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhongmao Fu
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Tengfei Li
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yuan Zhang
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jianming Zhang
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yan Yang
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhengfeng Yang
- Institute of Translational Medicine, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qi Li
- Department of Medical Oncology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhengjun Qiu
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Chen Huang
- Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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