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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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Guo Z, Zhang Z, Liu L, Zhao Y, Liu Z, Zhang C, Qi H, Feng J, Yang C, Tai W, Banchini F, Inchingolo R. Machine learning for predicting liver and/or lung metastasis in colorectal cancer: A retrospective study based on the SEER database. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108362. [PMID: 38704899 DOI: 10.1016/j.ejso.2024.108362] [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: 12/03/2023] [Revised: 04/11/2024] [Accepted: 04/20/2024] [Indexed: 05/07/2024]
Abstract
OBJECTIVE This study aims to establish a machine learning (ML) model for predicting the risk of liver and/or lung metastasis in colorectal cancer (CRC). METHODS Using the National Institutes of Health (NIH)'s Surveillance, Epidemiology, and End Results (SEER) database, a total of 51265 patients with pathological diagnosis of colorectal cancer from 2010 to 2015 were extracted for model development. On this basis, We have established 7 machine learning algorithm models. Evaluate the model based on accuracy, and AUC of receiver operating characteristics (ROC) and explain the relationship between clinical pathological features and target variables based on the best model. We validated the model among 196 colorectal cancer patients in Beijing Electric Power Hospital of Capital Medical University of China to evaluate its performance and universality. Finally, we have developed a network-based calculator using the best model to predict the risk of liver and/or lung metastasis in colorectal cancer patients. RESULTS 51265 patients were enrolled in the study, of which 7864 (15.3 %) had distant liver and/or lung metastasis. RF had the best predictive ability, In the internal test set, with an accuracy of 0.895, AUC of 0.956, and AUPR of 0.896. In addition, the RF model was evaluated in the external validation set with an accuracy of 0.913, AUC of 0.912, and AUPR of 0.611. CONCLUSION In this study, we constructed an RF algorithm mode to predict the risk of colorectal liver and/or lung metastasis, to assist doctors in making clinical decisions.
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Affiliation(s)
- Zhentian Guo
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Zongming Zhang
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China.
| | - Limin Liu
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Yue Zhao
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Zhuo Liu
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Chong Zhang
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Hui Qi
- Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China; Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China
| | - Jinqiu Feng
- Key Laboratory of Geriatrics (Hepatobiliary Diseases) of China General Technology Group, Beijing, 100073, China; Department of Immunology, Peking University School of Basic Medical Sciences, Peking University, Beijing, 100191, China
| | - Chunmin Yang
- Department of Gastroenterology, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical University, Beijing, 100073, China
| | - Weiping Tai
- Department of Gastroenterology, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China
| | - Filippo Banchini
- General Surgery Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy
| | - Riccardo Inchingolo
- Interventional Radiology Unit, "F. Miulli" Regional General Hospital, Acquaviva delle Fonti, 70021, Italy
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Kokkinakis S, Ziogas IA, Llaque Salazar JD, Moris DP, Tsoulfas G. Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models. Cancers (Basel) 2024; 16:1645. [PMID: 38730597 PMCID: PMC11083016 DOI: 10.3390/cancers16091645] [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: 04/07/2024] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024] Open
Abstract
Colorectal liver metastasis (CRLM) is a disease entity that warrants special attention due to its high frequency and potential curability. Identification of "high-risk" patients is increasingly popular for risk stratification and personalization of the management pathway. Traditional regression-based methods have been used to derive prediction models for these patients, and lately, focus has shifted to artificial intelligence-based models, with employment of variable supervised and unsupervised techniques. Multiple endpoints, like overall survival (OS), disease-free survival (DFS) and development or recurrence of postoperative complications have all been used as outcomes in these studies. This review provides an extensive overview of available clinical prediction models focusing on the prognosis of CRLM and highlights the different predictor types incorporated in each model. An overview of the modelling strategies and the outcomes chosen is provided. Specific patient and treatment characteristics included in the models are discussed in detail. Model development and validation methods are presented and critically appraised, and model performance is assessed within a proposed framework.
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Affiliation(s)
- Stamatios Kokkinakis
- Department of General Surgery, School of Medicine, University Hospital of Heraklion, University of Crete, 71500 Heraklion, Greece;
| | - Ioannis A. Ziogas
- Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (I.A.Z.); (J.D.L.S.)
| | - Jose D. Llaque Salazar
- Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (I.A.Z.); (J.D.L.S.)
| | - Dimitrios P. Moris
- Department of Surgery, Duke University Medical Center, Durham, NC 27710, USA;
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Centre for Research and Innovation in Solid Organ Transplantation, Aristotle University School of Medicine, 54124 Thessaloniki, Greece
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Zhao F, Sun Y, Zhao J, Ge J, Zheng C, Ning K. Clinical characteristics and prognosis analysis of postoperative patients with stage I-III colon cancer based on SEER database. Clin Transl Oncol 2024; 26:225-230. [PMID: 37393416 DOI: 10.1007/s12094-023-03239-w] [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: 04/28/2023] [Accepted: 05/29/2023] [Indexed: 07/03/2023]
Abstract
PURPOSE To identify the relevant factors affecting the prognosis and survival time of colon cancer and construct a survival prediction model. METHODS Data on postoperative stage I-III colon cancer patients were obtained from the Surveillance, Epidemiology, and End Results database. We used R project to analyze the data. Univariate and multivariate Cox regression analyses were performed for independent factors correlated with overall survival from colon cancer. The C-index was used to screen the factors that had the greatest influence in overall survival after surgery in colon cancer patients. Receiver operating characteristic (ROC) curve was made according to the Risk score and calculated to validate the predictive accuracy of the model. In addition, we used decision curve analysis (DCA) to evaluate the clinical benefits and utility of the nomogram. We created a model survival curve to determine the difference in prognosis between patients in the low-risk group and those in the high-risk group. RESULTS Univariate and multifactor COX analyses showed that the race, Grade, tumor size, N-stage and T-stage were independent risk factors affecting survival time of patients. The analysis of ROC and DCA showed the nomogram prediction model constructed based on the above indicators has good predictive effects. CONCLUSION Overall, the nomogram constructed in this study has good predictive effects. It can provide a reference for future clinicians to evaluate the prognosis of colon cancer patients.
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Affiliation(s)
- Fuqiang Zhao
- Department of Oncology Surgery, The Second Affiliated Hospital of Qiqihaer Medical University, No. 37 Zhonghuaxi Road, Jianhua District, Qiqihar, 161006, Heilongjiang, China.
| | - Ying Sun
- Department of Pharmacy Department, The Second Affiliated Hospital of Qiqihaer Medical University, Qiqihar, China
| | - Jingying Zhao
- Department of Oncology Surgery, The Second Affiliated Hospital of Qiqihaer Medical University, No. 37 Zhonghuaxi Road, Jianhua District, Qiqihar, 161006, Heilongjiang, China
| | - Jie Ge
- Department of Epidemiology and Statistic, Public Health College, Qiqihaer Medical University, Qiqihar, China
| | - Chunlei Zheng
- Department of Oncology Surgery, The Second Affiliated Hospital of Qiqihaer Medical University, No. 37 Zhonghuaxi Road, Jianhua District, Qiqihar, 161006, Heilongjiang, China
| | - Kepeng Ning
- Department of Oncology Surgery, The Second Affiliated Hospital of Qiqihaer Medical University, No. 37 Zhonghuaxi Road, Jianhua District, Qiqihar, 161006, Heilongjiang, China
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Liu CY, Yang YS, Ye K, He HF. Development of nomograms for predicting the survival of intestinal-type gastric adenocarcinoma patients after surgery. Sci Rep 2023; 13:17430. [PMID: 37833383 PMCID: PMC10576064 DOI: 10.1038/s41598-023-44671-w] [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: 12/09/2022] [Accepted: 10/11/2023] [Indexed: 10/15/2023] Open
Abstract
Intestinal-type gastric adenocarcinoma (IGA) is a common phenotype of gastric cancer. Currently, few studies have constructed nomograms that may predict overall (OS) and cancer-specific survival (CSS) probability after surgery. This study is to establish novel nomograms for predicting the survival of IGA patients who received surgery. A total of 1814 IGA patients who received surgery between 2000 and 2018 were selected from Surveillance, Epidemiology, and End Results database and randomly assigned to the training and validating sets at a ratio of 7:3. Then univariate and multivariate cox regression analyses were performed to screen significant indictors for the construction of nomograms. The calibration curve, the area under the receiver operating characteristic (receiver operating characteristic, ROC) curve (the area under curve, AUC), C-index, net reclassification index (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) curves were applied to assess the performance of the model. The significant outcomes of multivariate analysis revealed that ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, radiotherapy, number of regional nodes examined, number of regional nodes positive) were demonstrated to construct the nomogram for OS and ten variables (age, sex, race, surgery type, summary stage, grade, AJCC TNM stage, chemotherapy, number of regional nodes examined, number of regional nodes positive) for CSS. The calibration and AUC uncovered their favorable predictive performance. Subsequently, C-index, NRI, IDI and DCA curves further validated the predicative superiority of nomograms over 7th AJCC Stage System. The validated nomogram provides more reliable OS and CSS predictions for postoperative IGA patients with good accuracy, which can help surgeons in treatment decision-making and prognosis evaluation.
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Affiliation(s)
- Chu-Yun Liu
- Department of Anaesthesiology, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian, China
| | - Yu-Shen Yang
- Department of Anaesthesiology, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian, China
- Department of General Surgery, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian, China
| | - Kai Ye
- Department of General Surgery, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian, China.
| | - He-Fan He
- Department of Anaesthesiology, The Second Affiliated Hospital of Fujian Medical University, No. 34 North Zhongshan Road, Quanzhou, 362000, Fujian, China.
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Jiang Y, Zhou S, Zhu Z, Chen J, Liang J. Survival nomograms for simultaneous resection of primary and hepatic lesions without neoadjuvant chemotherapy in patients with resectable colorectal liver metastasis. CANCER INNOVATION 2023; 2:240-252. [PMID: 38089745 PMCID: PMC10686155 DOI: 10.1002/cai2.45] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/26/2022] [Accepted: 11/30/2022] [Indexed: 10/15/2024]
Abstract
Background No well-performing nomogram has been developed specifically to predict individual-patient cancer-specific survival (CSS) and overall survival (OS) among patients with resectable colorectal liver metastasis (CRLM) who undergo simultaneous resection of primary and hepatic lesions without neoadjuvant chemotherapy (NAC). We aim to investigate the prognosis of patients with resectable CRLM undergoing simultaneous resection of primary and hepatic lesions without NAC. Methods Data of patients with CRLM in the Surveillance, Epidemiology and End Results Program (cohort, n = 225) were collected as the training set, and data of patients with CRLM treated at the National Cancer Center (cohort, n = 180) were collected as the validation set. The prognostic value of the clinicopathological parameters in the training cohort was assessed using Kaplan‒Meier curves and univariate and multivariate Cox proportional hazards models, and OS and CSS nomograms integrated with the prognostic variables were constructed. Calibration analyses, receiver operating characteristic (ROC) curves, and decision curve analyses (DCAs) were then performed to evaluate the performance of the nomograms. Results There was no collinearity among the collected variables. Three factors were associated with OS and CSS: the pretreatment carcinoembryonic antigen (CEA) concentration, pathologic N (pN) stage, and adjuvant chemotherapy (each p < 0.05). OS and CSS nomograms were constructed using these three parameters. The calibration plots revealed favorable agreement between the predicted and observed outcomes. The areas under the ROC curves were approximately 0.7. The DCA plots revealed that both nomograms had satisfactory clinical benefits. The ROC curves and DCAs also confirmed that the nomogram surpassed the tumor, node, and metastasis staging system. Conclusion The herein-described nomograms containing the pretreatment CEA concentration, pN stage, and adjuvant chemotherapy may be effective models for predicting postoperative survival in patients with CRLM.
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Affiliation(s)
- Yu‐Juan Jiang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Si‐Cheng Zhou
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zi‐Xing Zhu
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Jing‐Hua Chen
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Jian‐Wei Liang
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Li T, Liang Y, Wang D, Zhou Z, Shi H, Li M, Liao H, Li T, Lei X. Development and validation of a clinical survival model for young-onset colorectal cancer with synchronous liver-only metastases: a SEER population-based study and external validation. Front Oncol 2023; 13:1161742. [PMID: 37143954 PMCID: PMC10153626 DOI: 10.3389/fonc.2023.1161742] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 04/03/2023] [Indexed: 05/06/2023] Open
Abstract
Background The morbidity and mortality of young-onset colorectal cancer (YO-CRC) patients have been increasing in recent years. Moreover, YO-CRC patients with synchronous liver-only metastases (YO-CRCSLM) have various survival outcomes. Therefore, the purpose of this study was to construct and validate a prognostic nomogram for patients with YO-CRCSLM. Methods The YO-CRCSLM patients were rigorously screened from the Surveillance, Epidemiology, and End Results (SEER) database in January 2010 and December 2018 and then assigned to a training and validation cohort randomly (1488 and 639 patients, respectively). Moreover, the 122 YO-CRCSLM patients who were enrolled in The First Affiliated Hospital of Nanchang University were served as a testing cohort. The variables were selected using the multivariable Cox model based on the training cohort and then developed a nomogram. The validation and testing cohort were used to validate the model's predictive accuracy. The calibration plots were used to determine the Nomogram's discriminative capabilities and precision, and the decision analysis (DCA) was performed to evaluate the Nomogram's net benefit. Finally, the Kaplan-Meier survival analyses were performed for the stratified patients based on total nomogram scores classified by the X-tile software. Results The Nomogram was constructed including ten variables: marital status, primary site, grade, metastatic lymph nodes ratio (LNR), T stage, N stage, carcinoembryonic antigen (CEA), Surgery, and chemotherapy. The Nomogram performed admirably in the validation and testing group according to the calibration curves. The DCA analyses showed good clinical utility values. Low-risk patients (score<234) had significantly better survival outcomes than middle-risk (234-318) and high-risk (>318) patients (P < 0.001). Conclusion A nomogram predicting the survival outcomes for patients with YO-CRCSLM was developed. In addition to facilitating personalized survival prediction, this nomogram may assist in developing clinical treatment strategies for patients with YO-CRCSLM who are undergoing treatment.
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Affiliation(s)
- Tao Li
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Gastrointestinal Surgical Institute, Nanchang University, Nanchang, Jiangxi, China
| | - Yahang Liang
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Gastrointestinal Surgical Institute, Nanchang University, Nanchang, Jiangxi, China
| | - Daqiang Wang
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Gastrointestinal Surgical Institute, Nanchang University, Nanchang, Jiangxi, China
| | - Zhen Zhou
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Gastrointestinal Surgical Institute, Nanchang University, Nanchang, Jiangxi, China
| | - Haoran Shi
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Gastrointestinal Surgical Institute, Nanchang University, Nanchang, Jiangxi, China
| | - Mingming Li
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Gastrointestinal Surgical Institute, Nanchang University, Nanchang, Jiangxi, China
| | - Hualin Liao
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Gastrointestinal Surgical Institute, Nanchang University, Nanchang, Jiangxi, China
| | - Taiyuan Li
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Gastrointestinal Surgical Institute, Nanchang University, Nanchang, Jiangxi, China
| | - Xiong Lei
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Gastrointestinal Surgical Institute, Nanchang University, Nanchang, Jiangxi, China
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Wang X, Qi R, Xu Y, Lu X, Shi Q, Wang Y, Wang D, Wang C. Clinicopathological characteristics and prognosis of colon cancer with lung metastasis without liver metastasis: A large population-based analysis. Medicine (Baltimore) 2022; 101:e31333. [PMID: 36281166 PMCID: PMC9592286 DOI: 10.1097/md.0000000000031333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Distant metastasis explains the high mortality rate of colon cancer, in which lung metastasis without liver metastasis (LuM) is a rare subtype. This study is aimed to identify risk factors of LuM and LLM (lung metastasis with liver metastasis) from colon cancer, and to analyze the prognosis of patients with LuM by creating a nomogram. Patients' information were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariable logistic regression analysis was used to determine the risk factors for LuM and LLM. Prognostic factors for cancer-specific survival (CSS) and overall survival (OS) were identified by multivariate Cox proportional hazards regression and nomogram models were established to predict CSS and OS. Multivariate logistic regression analysis showed that blacks, splenic flexure of colon tumor, tumor size >5 cm, T4, N3, and higher lymph node positive rate were associated with the occurrence of LuM. Meanwhile, age >65 years old, female, splenic flexure of colon, higher lymph node positive rate, and brain metastasis were independent risk factors for CSS. The C-index of the prediction model for CSS was 0.719 (95% CI: 0.691-0.747). In addition, age, primary site, tumor size, differentiation grade, N stage, and bone metastasis were significantly different between LuM and LLM. The nomograms we created were effective in predicting the survival of individuals. Furthermore, patients with LuM and LLM from colon cancer might require different follow-up intervals and examinations.
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Affiliation(s)
- Xiao Wang
- Department of Colorectal Surgery, The First People’s Hospital of Fuyang, Hangzhou, Zhejiang Province, P. R. China
| | - Ruihua Qi
- Department of Colorectal Surgery, The First People’s Hospital of Fuyang, Hangzhou, Zhejiang Province, P. R. China
| | - Ying Xu
- Department of Colorectal Surgery, The First People’s Hospital of Fuyang, Hangzhou, Zhejiang Province, P. R. China
| | - Xingang Lu
- Department of Colorectal Surgery, The First People’s Hospital of Fuyang, Hangzhou, Zhejiang Province, P. R. China
| | - Qing Shi
- Department of Colorectal Surgery, The First People’s Hospital of Fuyang, Hangzhou, Zhejiang Province, P. R. China
| | - Ya Wang
- Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Department of Hospital Infection-Control, Cancer Hospital of the University of Chinese Academy of Sciences, Department of Hospital Infection-Control, Zhejiang Cancer Hospital, Hangzhou, Zhejiang Province, P. R. China
| | - Da Wang
- Department of Colorectal Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, P. R. China
- *Correspondence: Chunliang Wang, Department of Colorectal Surgery, The First People’s Hospital of Fuyang, Hangzhou, Zhejiang Province 311499, P. R. China (e-mail: )
| | - Chunliang Wang
- Department of Colorectal Surgery, The First People’s Hospital of Fuyang, Hangzhou, Zhejiang Province, P. R. China
- *Correspondence: Chunliang Wang, Department of Colorectal Surgery, The First People’s Hospital of Fuyang, Hangzhou, Zhejiang Province 311499, P. R. China (e-mail: )
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Jin X, Wu Y, Feng Y, Lin Z, Zhang N, Yu B, Mao A, Zhang T, Zhu W, Wang L. A population-based predictive model identifying optimal candidates for primary and metastasis resection in patients with colorectal cancer with liver metastatic. Front Oncol 2022; 12:899659. [PMID: 36276059 PMCID: PMC9585382 DOI: 10.3389/fonc.2022.899659] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 09/13/2022] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND The survival benefit of primary and metastatic resection for patients with colorectal cancer with liver metastasis (CRLM) has been observed, but methods for discriminating which individuals would benefit from surgery have been poorly defined. Herein, a predictive model was developed to stratify patients into sub-population based on their response to surgery. METHODS We assessed the survival benefits for adults diagnosed with colorectal liver metastasis by comparing patients with curative surgery vs. those without surgery. CRLM patients enrolled in the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 were identified for model construction. Other data including CRLM patients from our center were obtained for external validation. Calibration plots, the area under the curve (AUC), and decision curve analysis (DCA) were used to evaluate the performance of the nomogram compared with the tumor-node-metastasis (TNM) classification. The Kaplan-Meier analysis was performed to examine whether this model would distinguish patients who could benefit from surgery. RESULTS A total of 1,220 eligible patients were identified, and 881 (72.2%) underwent colorectal and liver resection. Cancer-specific survival (CSS) for the surgery group was significantly better than that for the no-surgery group (41 vs. 14 months, p < 0.001). Five factors were found associated with CSS and adopted to build the nomograms, i.e., age, T stage, N stage, neoadjuvant chemotherapy, and primary tumor position. The AUC of the CRLM nomogram showed a better performance in identifying patients who could obtain benefits in the surgical treatment, compared with TNM classification (training set, 0.826 [95% CI, 0.786-0.866] vs. 0.649 [95% CI, 0.598-0.701]; internal validation set, 0.820 [95% CI, 0.741-0.899] vs. 0.635 [95% CI, 0.539-0.731]; external validation set, 0.763 [95% CI, 0.691-0.836] vs. 0.626 [95% CI, 0.542-0.710]). The calibration curves revealed excellent agreement between the predicted and actual survival outcomes. The DCA showed that the nomogram exhibited more clinical benefits than the TNM staging system. The beneficial and surgery group survived longer significantly than the non-beneficial and surgery group (HR = 0.21, 95% CI, 0.17-0.27, p < 0.001), but no difference was observed between the non-beneficial and surgery and non-surgery groups (HR = 0.89, 95% CI, 0.71-1.13, p = 0.344). CONCLUSIONS An accurate and easy-to-use CRLM nomogram has been developed and can be applied to identify optimal candidates for the resection of primary and metastatic lesions among CRLM patients.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Weiping Zhu
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lu Wang
- Department of Hepatic Surgery, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
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10
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Deng S, Jiang Z, Cao Y, Gu J, Mao F, Xue Y, Qin L, Liu K, Wang J, Wu K, Cai K. Development and validation of a prognostic scoring system for patients with colorectal cancer hepato-pulmonary metastasis: a retrospective study. BMC Cancer 2022; 22:643. [PMID: 35690752 PMCID: PMC9188712 DOI: 10.1186/s12885-022-09738-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/03/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Hepato-pulmonary metastasis of colorectal cancer (CRC) is a rare disease with poor prognosis. This study aims to establish a highly efficient nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer hepato-pulmonary metastasis (CRCHPM). METHODS We retrospectively analyzed the data of patients with CRCHPM from SEER database and Wuhan Union Hospital Cancer Center (WUHCC). A total of 1250 CRCHPM patients were randomly assigned to the training, internal validation, and external validation cohorts from 2010 to 2016.Univariate and multivariate cox analysis were performed to identify independent clinicopathological predictors of OS and CSS, and a nomogram was constructed to predict OS and CSS in CRCHPM patients. RESULTS A nomogram of OS was constructed based on seven independent predictors of age, degree of differentiation, T stage, chemotherapy, number of lsampled lymph nodes, number of positive lymph nodes, and tumor size. Nomogram showed favorable sensitivity in predicting OS at 1, 3 and 5 years, with area under the receiver operating characteristic curve (AUROC) values of 0.802, 0.759 and 0.752 in the training cohort;0.814, 0.769 and 0.716 in the internal validation cohort;0.778, 0.756 and 0.753 in the external validation cohort, respectively. A nomogram of CSS was constructed based on three independent predictors of T stage, chemotherapy, and tumor size. The AUROC values of 1, 3 and 5 years were 0.709,0.588,0.686 in the training cohort; 0.751, 0.648,0.666 in the internal validation cohort;0.781,0.588,0.645 in the external validation cohort, respectively. Calibration curves, Concordance index (C-index), and decision curve analysis (DCA) results revealed that using our model to predict OS and CSS is more efficient than other single clinicopathological characteristics. CONCLUSION A nomogram of OS and CSS based on clinicopathological characteristics can be conveniently used to predict the prognosis of CRCHPM patients.
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Affiliation(s)
- Shenghe Deng
- 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
| | - Yinghao Cao
- 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
| | - Le Qin
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Ke Liu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, Hubei, China
| | - Jiliang Wang
- 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
| | - 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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11
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Ren D, Wang WL, Wang G, Chen WW, Li XK, Li GD, Bai SX, Dong HM, Chen WH. Development and Internal Validation of a Nomogram-Based Model to Predict Three-Year and Five-Year Overall Survival in Patients with Stage II/III Colon Cancer. Cancer Manag Res 2022; 14:225-236. [PMID: 35058717 PMCID: PMC8765714 DOI: 10.2147/cmar.s335665] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 10/20/2021] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE The aim of this study was to develop a nomogram-based model to predict the three-year and five-year overall survival (OS) of patients with stage II/III colon cancer following radical resection. METHODS A total of 1156 patients with stage II/III colon cancer who underwent radical resection at the Affiliated Hospital of Guizhou Medical University between December 2012 and December 2018 were enrolled. Lasso regression was used to screen out 12 variables: age, prealbumin, albumin, degree of differentiation, total tumor-node-metastasis (TNM) stage, T stage, N stage, prognostic nutritional index (PNI), platelet/lymphocyte count, carcinoembryonic antigen, carbohydrate antigen 19-9 (CA19-9), and postoperative adjuvant chemotherapy. The data set was then randomly split into a modeling set and a validation set, and the bootstrap method was used to verify the internal validity of the final model. A nomogram was then used to present the model, and the risk groups were categorized according to the total score in the nomogram. RESULTS This study established and developed a simple, easy-to-use predictive model that included age, degree of differentiation, N stage, CA19-9, PNI, and postoperative chemotherapy as variables. In the multivariate Cox regression analysis, only postoperative chemotherapy was identified as an independent risk factor for death in patients with colon cancer. The receiver operating characteristic curve showed that the model demonstrated good resolution, with an area under the curve of 0.803. Decision curve analysis indicated that the model had a good positive net gain, and the bootstrap method was used to verify its stability. In the OS rate, the C-index was 0.78. According to the total score of the nomogram, the risk group was layered by drawing the Kaplan-Meier (K-M) curve. In the three-year OS K-M curve, the survival rates of the low-risk group, the medium-risk group, and the high-risk group were 96%, 93%, and 82%, respectively. In the five-year OS K-M curve, the survival rates of the low-risk group, the medium-risk group, and the high-risk group were 94%, 90%, and 73%, respectively. CONCLUSION The nomogram-based prediction model developed in this study is stable and has good resolution, reliability, and net gain. It will therefore be useful for clinicians performing risk stratification and postoperative monitoring and in the development of personalized treatment options for patients with stage II/III colon cancer.
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Affiliation(s)
- Dan Ren
- Oncology Department, Guizhou Medical University, Guiyang, Guizhou, 550000, People’s Republic of China
| | - Wen-Ling Wang
- Department of Abdominal Oncology, Affiliated Hospital of Guizhou Medical University, Affiliated Tumor Hospital of Guizhou Medical University, Guiyang, Guizhou, 550000, People’s Republic of China
| | - Gang Wang
- Department of Abdominal Oncology, Affiliated Hospital of Guizhou Medical University, Affiliated Tumor Hospital of Guizhou Medical University, Guiyang, Guizhou, 550000, People’s Republic of China
| | - Wei-Wei Chen
- Department of Abdominal Oncology, Affiliated Hospital of Guizhou Medical University, Affiliated Tumor Hospital of Guizhou Medical University, Guiyang, Guizhou, 550000, People’s Republic of China
| | - Xiao-Kai Li
- Department of Abdominal Oncology, Affiliated Hospital of Guizhou Medical University, Affiliated Tumor Hospital of Guizhou Medical University, Guiyang, Guizhou, 550000, People’s Republic of China
| | - Guo-Dong Li
- Department of Abdominal Oncology, Affiliated Hospital of Guizhou Medical University, Affiliated Tumor Hospital of Guizhou Medical University, Guiyang, Guizhou, 550000, People’s Republic of China
| | - Sai-Xi Bai
- Department of Abdominal Oncology, Affiliated Hospital of Guizhou Medical University, Affiliated Tumor Hospital of Guizhou Medical University, Guiyang, Guizhou, 550000, People’s Republic of China
| | - Hong-Min Dong
- Department of Abdominal Oncology, Affiliated Hospital of Guizhou Medical University, Affiliated Tumor Hospital of Guizhou Medical University, Guiyang, Guizhou, 550000, People’s Republic of China
| | - Wang-Hua Chen
- Department of Abdominal Oncology, Affiliated Hospital of Guizhou Medical University, Affiliated Tumor Hospital of Guizhou Medical University, Guiyang, Guizhou, 550000, People’s Republic of China
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12
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Kuai L, Zhang Y, Luo Y, Li W, Li XD, Zhang HP, Liu TY, Yin SY, Li B. Prognostic Nomogram for Liver Metastatic Colon Cancer Based on Histological Type, Tumor Differentiation, and Tumor Deposit: A TRIPOD Compliant Large-Scale Survival Study. Front Oncol 2021; 11:604882. [PMID: 34712601 PMCID: PMC8546254 DOI: 10.3389/fonc.2021.604882] [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: 09/10/2020] [Accepted: 09/20/2021] [Indexed: 12/11/2022] Open
Abstract
Objective A proportional hazard model was applied to develop a large-scale prognostic model and nomogram incorporating clinicopathological characteristics, histological type, tumor differentiation grade, and tumor deposit count to provide clinicians and patients diagnosed with colon cancer liver metastases (CLM) a more comprehensive and practical outcome measure. Methods Using the Transparent Reporting of multivariable prediction models for individual Prognosis or Diagnosis (TRIPOD) guidelines, this study identified 14,697 patients diagnosed with CLM from 1975 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) 21 registry database. Patients were divided into a modeling group (n=9800), an internal validation group (n=4897) using computerized randomization. An independent external validation cohort (n=60) was obtained. Univariable and multivariate Cox analyses were performed to identify prognostic predictors for overall survival (OS). Subsequently, the nomogram was constructed, and the verification was undertaken by receiver operating curves (AUC) and calibration curves. Results Histological type, tumor differentiation grade, and tumor deposit count were independent prognostic predictors for CLM. The nomogram consisted of age, sex, primary site, T category, N category, metastasis of bone, brain or lung, surgery, and chemotherapy. The model achieved excellent prediction power on both internal (mean AUC=0.811) and external validation (mean AUC=0.727), respectively, which were significantly higher than the American Joint Committee on Cancer (AJCC) TNM system. Conclusion This study proposes a prognostic nomogram for predicting 1- and 2-year survival based on histopathological and population-based data of CLM patients developed using TRIPOD guidelines. Compared with the TNM stage, our nomogram has better consistency and calibration for predicting the OS of CLM patients.
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Affiliation(s)
- Le Kuai
- Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Ying Zhang
- Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Ying Luo
- Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Wei Li
- Center for Translational Medicine, Huaihe Hospital of Henan University, Kaifeng, China
| | - Xiao-Dong Li
- Department of Urology Surgery, Huaihe Hospital of Henan University, Kaifeng, China.,Institute of Evidence-Based Medicine and Knowledge Translation, Henan University, Kaifeng, China
| | - Hui-Ping Zhang
- Research and Development Center, Shanghai Applied Protein Technology Co., Ltd., Shanghai, China
| | - Tai-Yi Liu
- Research and Development Center, Shanghai Applied Protein Technology Co., Ltd., Shanghai, China
| | - Shuang-Yi Yin
- Center for Translational Medicine, Huaihe Hospital of Henan University, Kaifeng, China
| | - Bin Li
- Department of Dermatology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Dermatology, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China.,Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, China
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13
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Xiong Y, Shi X, Hu Q, Wu X, Long E, Bian Y. A Nomogram for Predicting Survival in Patients With Breast Cancer Liver Metastasis: A Population-Based Study. Front Oncol 2021; 11:600768. [PMID: 34150607 PMCID: PMC8206538 DOI: 10.3389/fonc.2021.600768] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 04/23/2021] [Indexed: 12/29/2022] Open
Abstract
Objective The prognosis of patients with breast cancer liver metastasis (BCLM) was poor. We aimed at constructing a nomogram to predict overall survival (OS) for BCLM patients using the SEER (Surveillance Epidemiology and End Results) database, thus choosing an optimized therapeutic regimen to treat. Methods We identified 1173 patients with BCLM from the SEER database and randomly divided them into training (n=824) and testing (n=349) cohorts. The Cox proportional hazards model was applied to identify independent prognostic factors for BCLM, based on which a nomogram was constructed to predict 1-, 2-, and 3-year OS. Its discrimination and calibration were evaluated by the Concordance index (C-index) and calibration plots, while the accuracy and benefits were assessed by comparing it to AJCC-TNM staging system using the decision curve analysis (DCA). Kaplan-Meier survival analyses were applied to test the clinical utility of the risk stratification system. Results Grade, marital status, surgery, radiation therapy, chemotherapy, CS tumor size, tumor subtypes, bone metastatic, brain metastatic, and lung metastatic were identified to be independent prognostic factors of OS. In comparison with the AJCC-TNM staging system, an improved C-index was obtained (training group: 0.701 vs. 0.557, validation group: 0.634 vs. 0.557). The calibration curves were consistent between nomogram-predicted survival probability and actual survival probability. Additionally, the DCA curves yielded larger net benefits than the AJCC-TNM staging system. Finally, the risk stratification system can significantly distinguish the ones with different survival risk based on the different molecular subtypes. Conclusion We have successfully built an effective nomogram and risk stratification system to predict OS in BCLM patients, which can assist clinicians in choosing the appropriate treatment strategies for individual BCLM patients.
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Affiliation(s)
- Yu Xiong
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xia Shi
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Qi Hu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xingwei Wu
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Enwu Long
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuan Bian
- Personalized Drug Therapy Key Laboratory of Sichuan Province, Department of Pharmacy, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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14
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Liu C, Hu C, Huang J, Xiang K, Li Z, Qu J, Chen Y, Yang B, Qu X, Liu Y, Zhang G, Wen T. A Prognostic Nomogram of Colon Cancer With Liver Metastasis: A Study of the US SEER Database and a Chinese Cohort. Front Oncol 2021; 11:591009. [PMID: 33738248 PMCID: PMC7962604 DOI: 10.3389/fonc.2021.591009] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 01/25/2021] [Indexed: 12/12/2022] Open
Abstract
Background Among colon cancer patients, liver metastasis is a commonly deadly phenomenon, but there are few prognostic models for these patients. Methods The clinicopathologic data of colon cancer with liver metastasis (CCLM) patients were downloaded from the Surveillance, Epidemiology and End Results (SEER) database. All patients were randomly divided into training and internal validation sets based on the ratio of 7:3. A prognostic nomogram was established with Cox analysis in the training set, which was validated by two independent validation sets. Results A total of 5,700 CCLM patients were included. Age, race, tumor size, tumor site, histological type, grade, AJCC N status, carcinoembryonic antigen (CEA), lung metastasis, bone metastasis, surgery, and chemotherapy were independently associated with the overall survival (OS) of CCLM in the training set, which were used to establish a nomogram. The AUCs of 1-, 2- and 3-year were higher than or equal to 0.700 in the training, internal validation, and external validation sets, indicating the favorable effects of our nomogram. Besides, whether in overall or subgroup analysis, the risk score calculated by this nomogram can divide CCLM patients into high-, middle- and low-risk groups, which suggested that the nomogram can significantly determine patients with different prognosis and is suitable for different patients. Conclusion Higher age, the race of black, larger tumor size, higher grade, histological type of mucinous adenocarcinoma and signet ring cell carcinoma, higher N stage, RCC, lung metastasis, bone metastasis, without surgery, without chemotherapy, and elevated CEA were independently associated with poor prognosis of CCLM patients. A nomogram incorporating the above variables could accurately predict the prognosis of CCLM.
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Affiliation(s)
- Chuan Liu
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China.,Liaoning Province Clinical Research Center for Cancer, Shenyang, China
| | - Chuan Hu
- Medical College, Qingdao University, Qingdao, China
| | - Jiale Huang
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China.,Liaoning Province Clinical Research Center for Cancer, Shenyang, China
| | - Kanghui Xiang
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China.,Liaoning Province Clinical Research Center for Cancer, Shenyang, China
| | - Zhi Li
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China.,Liaoning Province Clinical Research Center for Cancer, Shenyang, China
| | - Jinglei Qu
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China.,Liaoning Province Clinical Research Center for Cancer, Shenyang, China
| | - Ying Chen
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China.,Liaoning Province Clinical Research Center for Cancer, Shenyang, China
| | - Bowen Yang
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China.,Liaoning Province Clinical Research Center for Cancer, Shenyang, China
| | - Xiujuan Qu
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China.,Liaoning Province Clinical Research Center for Cancer, Shenyang, China
| | - Yunpeng Liu
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China.,Liaoning Province Clinical Research Center for Cancer, Shenyang, China
| | - Guangwei Zhang
- Smart Hospital Management Department, The First Hospital of China Medical University, Shenyang, China
| | - Ti Wen
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.,Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China.,Liaoning Province Clinical Research Center for Cancer, Shenyang, China
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15
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Wang N, Yang J, Lyu J, Liu Q, He H, Liu J, Li L, Ren X, Li Z. A convenient clinical nomogram for predicting the cancer-specific survival of individual patients with small-intestine adenocarcinoma. BMC Cancer 2020; 20:505. [PMID: 32487033 PMCID: PMC7268250 DOI: 10.1186/s12885-020-06971-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 05/18/2020] [Indexed: 02/07/2023] Open
Abstract
Background The objective of this study was to develop a practical nomogram for predicting the cancer-specific survival (CSS) of patients with small-intestine adenocarcinoma. Methods Patients diagnosed with small-intestine adenocarcinoma between 2010 and 2015 were selected for inclusion in this study from the Surveillance, Epidemiology, and End Results (SEER) database. The selected patients were randomly divided into the training and validation cohorts at a ratio of 7:3. The predictors of CSS were identified by applying both forward and backward stepwise selection methods in a Cox regression model. The performance of the nomogram was measured by the concordance index (C-index), the area under receiver operating characteristic curve (AUC), calibration plots, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Results Multivariate Cox regression indicated that factors including age at diagnosis, sex, marital status, insurance status, histology grade, SEER stage, surgery status, T stage, and N stage were independent covariates associated with CSS. These factors were used to construct a predictive model, which was built and virtualized by a nomogram. The C-index of the constructed nomogram was 0.850. The AUC values indicated that the established nomogram displayed better discrimination performance than did the seventh edition of the American Joint Committee on Cancer TNM staging system in predicting CSS. The IDI and NRI also showed that the nomogram exhibited superior performance in both the training and validation cohorts. Furthermore, the calibrated nomogram predicted survival rates that closely corresponded to actual survival rates, while the DCA demonstrated the considerable clinical usefulness of the nomogram. Conclusion We have constructed a nomogram for predicting the CSS of small-intestine adenocarcinoma patients. This prognostic model may improve the ability of clinicians to predict survival in individual patients and provide them with treatment recommendations.
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Affiliation(s)
- Na Wang
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.,School of Nursing and Health, Henan University, Kaifeng, Henan, China
| | - Jin Yang
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.,School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Qingqing Liu
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Hairong He
- Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Jie Liu
- School of Nursing and Health, Henan University, Kaifeng, Henan, China
| | - Li Li
- School of Nursing and Health, Henan University, Kaifeng, Henan, China
| | - Xuequn Ren
- Center for Evidence-Based Medicine and Clinical Research, Huaihe Hospital of Henan University, Kaifeng, Henan, China. .,Department of General Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China.
| | - Zhendong Li
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China.
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Wu JY, Wang YF, Ma H, Li SS, Miao HL. Nomograms predicting long-term survival in patients with invasive intraductal papillary mucinous neoplasms of the pancreas: A population-based study. World J Gastroenterol 2020; 26:535-549. [PMID: 32089629 PMCID: PMC7015718 DOI: 10.3748/wjg.v26.i5.535] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 01/06/2020] [Accepted: 01/11/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND There are few effective tools to predict survival in patients with invasive intraductal papillary mucinous neoplasms of the pancreas.
AIM To develop comprehensive nomograms to individually estimate the survival outcome of patients with invasive intraductal papillary mucinous neoplasms of the pancreas.
METHODS Data of 1219 patients with invasive intraductal papillary mucinous neoplasms after resection were extracted from the Surveillance, Epidemiology, and End Results database, and randomly divided into the training (n = 853) and the validation (n = 366) cohorts. Based on the Cox regression model, nomograms were constructed to predict overall survival and cancer-specific survival for an individual patient. The performance of the nomograms was measured according to discrimination, calibration, and clinical utility. Moreover, we compared the predictive accuracy of the nomograms with that of the traditional staging system.
RESULTS In the training cohort, age, marital status, histological type, T stage, N stage, M stage, and chemotherapy were selected to construct nomograms. Compared with the American Joint Committee on Cancer 7th staging system, the nomograms were generally more discriminative. The nomograms passed the calibration steps by showing high consistency between actual probability and nomogram prediction. Categorial net classification improvements and integrated discrimination improvements suggested that the predictive accuracy of the nomograms exceeded that of the American Joint Committee on Cancer staging system. With respect to decision curve analyses, the nomograms exhibited more preferable net benefit gains than the staging system across a wide range of threshold probabilities.
CONCLUSION The nomograms show improved predictive accuracy, discrimination capability, and clinical utility, which can be used as reliable tools for risk classification and treatment recommendations.
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Affiliation(s)
- Jia-Yuan Wu
- Department of Clinical Research, the Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
| | - Yu-Feng Wang
- School of Public Health, Guangdong Medical University, Zhanjiang 524023, Guangdong Province, China
| | - Huan Ma
- School of Public Health, Guangdong Medical University, Zhanjiang 524023, Guangdong Province, China
| | - Sha-Sha Li
- School of Public Health, Guangdong Medical University, Zhanjiang 524023, Guangdong Province, China
| | - Hui-Lai Miao
- Department of Clinical Research, the Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
- Department of Hepatobiliary Surgery, the Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong Province, China
- Department of Hepatobiliary Surgery, the Second Affiliated Hospital of Guangdong Medical University, Zhanjiang 524003, Guangdong Province, China
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Wang CY, Yang J, Zi H, Zheng ZL, Li BH, Wang Y, Ge Z, Jian GX, Lyu J, Li XD, Ren XQ. Nomogram for predicting the survival of gastric adenocarcinoma patients who receive surgery and chemotherapy. BMC Cancer 2020; 20:10. [PMID: 31906882 PMCID: PMC6943892 DOI: 10.1186/s12885-019-6495-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 12/23/2019] [Indexed: 12/16/2022] Open
Abstract
Background Surgery is the only way to cure gastric adenocarcinoma (GAC), and chemotherapy is the basic adjuvant management for GAC. A significant prognostic nomogram for predicting the respective disease-specific survival (DSS) rates of GAC patients who receive surgery and chemotherapy has not been established. Objective We were planning to establish a survival nomogram model for GAC patients who receive surgery and chemotherapy. Methods We identified 5764 GAC patients who had received surgery and chemotherapy from the record of Surveillance, Epidemiology, and End Results (SEER) database. About 70% (n = 4034) of the chosen GAC patients were randomly assigned to the training set, and the rest of the included ones (n = 1729) were assigned to the external validation set. A prognostic nomogram was constructed by the training set and the predictive accuracy of it was validated by the validation set. Results Based on the outcome of a multivariate analysis of candidate factors, a nomogram was developed that encompassed age at diagnosis, number of regional lymph nodes examined after surgery, number of positive regional lymph nodes, sex, race, grade, derived AJCC stage, summary stage, and radiotherapy status. The C-index (Harrell’s concordance index) of the nomogram model was some larger than that of the traditional seventh AJCC staging system (0.707 vs 0.661). Calibration plots of the constructed nomogram displayed that the probability of DSS commendably accord with the survival rate. Integrated discrimination improvement (IDI) revealed obvious increase and categorical net reclassification improvement (NRI) showed visible enhancement. IDI for 3-, 5- and 10- year DSS were 0.058, 0.059 and 0.058, respectively (P > 0.05), and NRI for 3-, 5- and 10- year DSS were 0.380 (95% CI = 0.316–0.470), 0.407 (95% CI = 0.350–0.505), and 0.413 (95% CI = 0.336–0.519), respectively. Decision curve analysis (DCA) proved that the constructed nomogram was preferable to the AJCC staging system. Conclusion The constructed nomogram supplies more credible DSS predictions for GAC patients who receive surgery and chemotherapy in the general population. According to validation, the new nomogram will be beneficial in facilitating individualized survival predictions and useful when performing clinical decision-making for GAC patients who receive surgery and chemotherapy.
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Affiliation(s)
- Chao-Yang Wang
- Department of General Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China.,Institute of Evidence-Based Medicine and knowledge translation, Henan University, Kaifeng, Henan, China
| | - Jin Yang
- Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.,School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Hao Zi
- Institute of Evidence-Based Medicine and knowledge translation, Henan University, Kaifeng, Henan, China
| | - Zhong-Li Zheng
- Institute of Evidence-Based Medicine and knowledge translation, Henan University, Kaifeng, Henan, China
| | - Bing-Hui Li
- Department of General Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China.,Institute of Evidence-Based Medicine and knowledge translation, Henan University, Kaifeng, Henan, China
| | - Yang Wang
- Department of General Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China.,Institute of Evidence-Based Medicine and knowledge translation, Henan University, Kaifeng, Henan, China
| | - Zheng Ge
- Department of General Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China.,Institute of Evidence-Based Medicine and knowledge translation, Henan University, Kaifeng, Henan, China
| | - Guang-Xu Jian
- Institute of Evidence-Based Medicine and knowledge translation, Henan University, Kaifeng, Henan, China.,Department of ICU, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Jun Lyu
- Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.,School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, China
| | - Xiao-Dong Li
- Institute of Evidence-Based Medicine and knowledge translation, Henan University, Kaifeng, Henan, China.,Department of Urology, Huaihe Hospital of Henan University, Kaifeng, Henan, China
| | - Xue-Qun Ren
- Department of General Surgery, Huaihe Hospital of Henan University, Kaifeng, Henan, China. .,Institute of Evidence-Based Medicine and knowledge translation, Henan University, Kaifeng, Henan, China.
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Li J, Liu L. Overall survival in patients over 40 years old with surgically resected pancreatic carcinoma: a SEER-based nomogram analysis. BMC Cancer 2019; 19:726. [PMID: 31337369 PMCID: PMC6651947 DOI: 10.1186/s12885-019-5958-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 07/18/2019] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The aim of this study was to identify the determinants of overall survival (OS) within patients over 40 years old with surgically resected pancreatic carcinoma (PC), and to develop a nomogram with the intention of OS predicting. METHODS A total of 6341 patients of 40 years of age or later with surgically resected PC between 2010 and 2015 were enrolled from the Surveillance, Epidemiology, and End Results (SEER) program and randomly assigned into training set (4242 cases) and validation set (2099 cases). A nomogram was constructed for predicting 1-, 2- and 3-years OS based on univairate and multivariate Cox regression. The C-index and calibration plot were adopted to assess the nomogram performance. RESULTS Our analysis showed that age, location of carcinoma in pancreas, tumor grade, TNM stage, size of carcinoma together with lymph node ratio (LNR) were considered to be independent overall survival predictors. A nomogram based on these six factors was developed with C-index being 0.680 (95%CI: 0.667-0.693). All calibration curves of OS fitted well. The OS curves stratified by nomogram-predicted probability score (≥20, 10-19 and < 10) demonstrated statistically significant difference not only within training set but also in validation set. CONCLUSIONS The present nomogram for OS predicting can serve as the efficacious survival-predicting model and assist in accurate decision-making for patients over 40 years old with surgically resected PC.
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Affiliation(s)
- Jian Li
- Clinical Research Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Huangpu District, Shanghai, 200025 China
| | - Leshan Liu
- Clinical Research Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, Huangpu District, Shanghai, 200025 China
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