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Guo L, Ding Y, Wen J, Miao M, Hu K, Ye G. Risk factors and predictive nomogram for non-curative resection in patients with early gastric cancer treated with endoscopic submucosal dissection: a retrospective cohort study. World J Surg Oncol 2025; 23:213. [PMID: 40450359 DOI: 10.1186/s12957-025-03850-x] [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: 02/14/2025] [Accepted: 05/16/2025] [Indexed: 06/03/2025] Open
Abstract
INTRODUCTION The objective of this study was to determine independent clinicopathological factors that can predict submucosal invasion and non-curative resection (NCR) outcomes after endoscopic submucosal dissection (ESD) in patients with early gastric cancer (EGC). METHODS Data were collected from consecutive patients who underwent gastric ESD at the First Affiliated Hospital of Ningbo University between 2016 and 2023. A retrospective analysis was conducted using the chi-squared test and logistic regression analysis. Multiple logistic regression analysis was applied to investigate factors independently predicting both submucosal invasion and NCR. These factors were used to construct predictive nomograms. RESULTS A total of 511 patients (535 EGC lesions) underwent ESD. Of these, 452 were curative (84.7%), and 83 (15.5%) were non-curative. Multivariate analysis revealed that location in the body and fundus or cardia of the stomach, larger tumor size (≥ 30 mm), and histological undifferentiated type were independent risk factors for submucosal invasion and deep submucosal invasion in patients with EGC (all P < 0.05). Multivariate analysis showed that tumor size of 20 ~ 29 mm, tumor size ≥ 30 mm, elevated lesions, depressed lesions, undifferentiated tumors and submucosal invasion were all independent predictors of NCR for EGCs (all P < 0.05). The area under the ROC curve (AUC) of the nomogram model for predicting submucosal invasion and non-curative resection was 0.821 (95% CI, 0.758 ~ 0.884) and 0.937 (95%CI, 0.889 ~ 0.985), respectively. CONCLUSIONS We developed nomograms to predict the risk of submucosal invasion and NCR prior to ESD. These predictive factors in addition to the existing ESD criteria can help provide the best treatment option for patients with EGC.
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Affiliation(s)
- Lihua Guo
- Department of Gastroenterology, The First Affiliated Hospital of Ningbo University, No.247 Renmin Road, Ningbo, 315020, China
| | - Yong Ding
- Department of Gastroenterology, The First Affiliated Hospital of Ningbo University, No.247 Renmin Road, Ningbo, 315020, China
| | - Jinfeng Wen
- Department of Gastroenterology, The First Affiliated Hospital of Ningbo University, No.247 Renmin Road, Ningbo, 315020, China
| | - Min Miao
- Department of Gastroenterology, The First Affiliated Hospital of Ningbo University, No.247 Renmin Road, Ningbo, 315020, China
| | - Kefeng Hu
- Department of Gastroenterology, The First Affiliated Hospital of Ningbo University, No.247 Renmin Road, Ningbo, 315020, China
| | - Guoliang Ye
- Department of Gastroenterology, The First Affiliated Hospital of Ningbo University, No.247 Renmin Road, Ningbo, 315020, China.
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Deng L, Che L, Sun H, En R, Ha B, Liu T, Wang T, Xu Q. Predicting the risk of lymph node metastasis in colon cancer: development and validation of an online dynamic nomogram based on multiple preoperative data. BMC Gastroenterol 2025; 25:350. [PMID: 40340933 PMCID: PMC12063464 DOI: 10.1186/s12876-025-03958-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2024] [Accepted: 04/29/2025] [Indexed: 05/10/2025] Open
Abstract
BACKGROUND Predicting lymph node metastasis (LNM) in colon cancer (CC) is crucial to treatment decision-making and prognosis. This study aimed to develop and validate a nomogram that estimates the risk of LNM in patients with CC using multiple clinical data from patients before surgery. METHODS Clinicopathological data were collected from 412 CC patients who underwent Radical resection of CC. The training cohort consisted of 300 cases, and the external validation cohort consisted of 112 cases. The LASSO and multivariate logistic regression were used to select the predictors and construct the nomogram. The discrimination and calibration of the nomogram were evaluated by the ROC curve and calibration curve, respectively. The clinical application of the nomogram was assessed by decision curve analysis(DCA) and clinical impact curves(CIC). RESULTS Eight independent factors associated with LNM were identified by multivariate logistic analysis: LN status on CT, tumor diameter on CT, differentiation, ulcer, intestinal obstruction, anemia, blood type, and neutrophil percentage. The online dynamic nomogram model constructed by independent factors has good discrimination and consistency. The AUC of 0.834(95% CI: 0.755-0.855) in the training cohort, 0.872(95%CI: 0.807-0.937) in the external validation cohort, and Internal validation showed that the corrected C statistic was 0.810. The calibration curve of both the training set and the external validation set indicated that the predicted outcome of the nomogram was highly consistent with the actual outcome. The DCA and CIC indicate that the model has clinical practical value. CONCLUSION Based on various simple parameters collected preoperatively, the online dynamic nomogram can accurately predict LNM risk in CC patients. The high discriminative ability and significant improvement of NRI and IDI indicate that the model has potential clinical application value.
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Affiliation(s)
- Longlian Deng
- Department of Abdominal Oncology, the Second People's Hospital of Neijiang, Neijiang, 641000, China
- Department of Gastrointestinal Surgery, Inner Mongolia Bayannur Hospital, Bayannur, 015000, China
| | - Lemuge Che
- Baotou Medical College, Baotou, 014000, China
| | - Haibin Sun
- Department of Gastrointestinal Surgery, Inner Mongolia Bayannur Hospital, Bayannur, 015000, China
| | - Riletu En
- Department of Gastrointestinal Surgery, Inner Mongolia Bayannur Hospital, Bayannur, 015000, China
| | - Bowen Ha
- Department of Gastrointestinal Surgery, Inner Mongolia Bayannur Hospital, Bayannur, 015000, China
- Inner Mongolia Medical University, Hohhot, 010110, China
| | - Tao Liu
- Department of Spinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, 050051, China
| | - Tengqi Wang
- Cancer Center, Inner Mongolia Bayannur Hospital, Bayannur, 015000, China.
| | - Qiang Xu
- Department of Abdominal Oncology, the Second People's Hospital of Neijiang, Neijiang, 641000, China.
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He Y, Xie X, Yang B, Jin X, Feng Z. Combining biomarkers to construct a novel predictive model for predicting preoperative lymph node metastasis in early gastric cancer. Front Oncol 2025; 15:1533889. [PMID: 40406257 PMCID: PMC12094995 DOI: 10.3389/fonc.2025.1533889] [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: 11/25/2024] [Accepted: 04/14/2025] [Indexed: 05/26/2025] Open
Abstract
Background Accurately identifying the status of lymph node metastasis (LNM) is crucial for determining the appropriate treatment strategy for early gastric cancer (EGC) patients. Methods Univariate and multivariate logistic regression analyses were used to explore the association between clinicopathological factors and LNM in EGC patients, leading to the development of a nomogram. Differential expression analysis was conducted to identify biomarkers associated with LNM, and their expression was evaluated through immunohistochemistry. The biomarker was integrated into the conventional model to create a new model, which was then assessed for reclassification and discrimination abilities. Results Multivariate logistic regression analysis revealed that tumor size, histological type, and the presence of ulcers are independent risk factors for LNM in EGC patients. The nomogram demonstrated good clinical performance. Incorporating HAVCR1 immunohistochemical expression into the new model further improved its performance, reclassification, and discrimination abilities. Conclusion The novel nomogram predictive model, based on preoperative clinicopathological factors such as tumor size, histological type, presence of ulcers, and HAVCR1 expression, provides valuable guidance for selecting treatment strategies for EGC patients.
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Affiliation(s)
| | | | | | | | - Zhijie Feng
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Hebei Key Laboratory of Gastroenterology, Hebei Institute of Gastroenterology, Hebei Clinical Research Center for Digestive Diseases, Shijiazhuang, Hebei, China
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Zhao B, Gu M, Wang Z, Li J, Wen M, Wu D, Li S, Liu L, Wang X. Risk factors and nomogram development for lymph node metastasis in early-onset early-stage gastric cancer: a retrospective cohort study. Front Oncol 2025; 15:1544758. [PMID: 40371226 PMCID: PMC12074922 DOI: 10.3389/fonc.2025.1544758] [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: 12/13/2024] [Accepted: 04/07/2025] [Indexed: 05/16/2025] Open
Abstract
Background The incidence of early onset gastric cancer(EOGC) is increasing. However, few studies have focused on early onset early stage gastric cancer(EEGC). The aim of this study was to determine the threshold age of patients with EOGC, identify the clinicopathological characteristics associated with lymph node metastasis(LNM) in EEGC, and develop a predictive model for LNM in EEGC. Methods A retrospective cohort study was conducted, including 1765 patients with early-stage gastric cancer. Logistic inflection point and stratified analysis were used to determine the threshold age. 266 patients met the criteria for EEGC and were included for further analysis. The patients were divided into two groups for the purposes of the study: a training dataset and an external validation dataset. The division of patients into these two groups was conducted in accordance with the time of surgery, with the ratio of patients in each group being approximately 7:3.Univariate and multivariate logistic regression analysis were used to identify LNM risk factors. A predictive nomogram was developed and validated using calibration plots and the area under the curve (AUC).The constructed logistic regression model was then validated using the external validation dataset. Results The threshold age for EOGC was determined to be 45 years. Of the 266 patients with EEGC, 20.7% had LNM. Tumor maximum diameter and lymphovascular invasion were identified as independent risk factors for LNM. The nomogram demonstrated high predictive accuracy, with an AUC of 0.809. Conclusions This study demonstrated that tumor maximum diameter and lymphovascular invasion were independent risk factors for LNM in EEGC. The predictive nomogram showed promising accuracy and might assist in identifying patients at higher risk of LNM, potentially informing treatment strategies. Given the relatively high LNM rate, endoscopic submucosal dissection may not be suitable for EEGC patients. Further large-scale multicenter studies are needed to deepen the understanding of this population and to confirm these findings.
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Affiliation(s)
- Binghe Zhao
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
- Medical College, Nankai University, Tianjin, China
| | - Mingyu Gu
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Zijian Wang
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jie Li
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
- Medical College, Nankai University, Tianjin, China
| | - Minghai Wen
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
- Medical College, Nankai University, Tianjin, China
| | - Di Wu
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shuo Li
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
- Medical College, Nankai University, Tianjin, China
| | - Lu Liu
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
- Medical College, Nankai University, Tianjin, China
| | - Xinxin Wang
- Department of General Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, China
- Medical College, Nankai University, Tianjin, China
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Zhu B, Wang Y, Zhang Z, Wang L, Ma Y, Li M. Development and validation of a radiologically-based nomogram for preoperative prediction of difficult laparoscopic cholecystectomy. Front Med (Lausanne) 2025; 12:1561769. [PMID: 40342585 PMCID: PMC12060169 DOI: 10.3389/fmed.2025.1561769] [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: 01/16/2025] [Accepted: 04/07/2025] [Indexed: 05/11/2025] Open
Abstract
Background Preoperative prediction of difficult laparoscopic cholecystectomy (DLC) remains challenging, as intraoperative anatomical complexity significantly increases complication risks. Current studies have not reached consensus on definitive risk factors for DLC. Materials and methods This retrospective study aimed to identify DLC risk factors and develop a predictive model. We analyzed clinical data from 265 patients undergoing laparoscopic cholecystectomy (LC) at the Department of General Surgery, Shijiazhuang People's Hospital, between September 2022 and June 2024. Risk factors were explored through least absolute shrinkage and selection operator (LASSO) regression, multivariate analysis, and receiver operating characteristic (ROC) curves, with a nomogram constructed for prediction. Results Among 265 eligible patients, four independent risk factors were identified: thickness of gallbladder wall (p = 0.0007), cystic duct length (p < 0.0001), cystic duct diameter (p < 0.0001), and gallbladder neck stones (p = 0.0002). The nomogram demonstrated strong predictive performance, with an area under the curve (AUC) of 0.915 in the training cohort and 0.842 in the validation cohort. Calibration curves indicated excellent model fit. Conclusion and discussion The proposed predictive model integrating gallbladder neck stones, thickness of gallbladder wall, cystic duct length, and cystic duct diameter may assist surgeons in preoperative DLC risk stratification. Further validation through multicenter prospective studies is warranted.
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Affiliation(s)
| | | | | | | | | | - Ming Li
- Department of General Surgery, Shijiazhuang People's Hospital, Shijiazhuang, China
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Song K, Wu J, Xu M, Li M, Chen Y, Zhang Y, Chen H, Jiang C. An ensemble learning model to predict lymph node metastasis in early gastric cancer. Sci Rep 2025; 15:11257. [PMID: 40175493 PMCID: PMC11965319 DOI: 10.1038/s41598-025-95794-1] [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] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Accepted: 03/24/2025] [Indexed: 04/04/2025] Open
Abstract
Lymph node metastasis is a critical factor for determining therapeutic strategies and assessing the prognosis of early gastric cancer. This work aimed to establish a more dependable predictive model for identify lymph node metastasis in early gastric cancer. The study utilized both univariate and multivariate logistic regression analyses to identify independent risk factors for lymph node metastasis of early gastric cancer, while employing five distinct algorithms to calculate feature weights. The optimal feature combination for each algorithm model was determined by combining the six highest weight features from all five models along with the independent risk factors. An ensemble learning model was subsequently constructed by integrating these five models. The model's performance was evaluated by the AUC, accuracy, and F1 score. Following this, a threshold was determined based on the F1 score, and the model's performance was assessed using an external validation set. The lymph node metastasis rate of early gastric cancer in our study was 16.4%. The ensemble learning model achieved an AUC value of 0.860 in the test set, with an accuracy of 82.35% and an F1 score of 0.611. Based on the F1 score, the model's threshold was set at 0.18. Additionally, the model demonstrated an AUC of 0.892 in the external validation set, along with an accuracy of 78.30% and an F1 score of 0.60.We constructed an ensemble learning model for predicting lymph node metastasis of early gastric cancer. Gastric surgery should be considered as the preferred treatment when the risk of lymph node metastasis exceeds 18%.
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Affiliation(s)
- Kaiqing Song
- Department of Gastroenterology, The Fourth Affiliated Hospital of Soochow University, Suzhou, China
| | - Jiaming Wu
- Department of Gastroenterology, The Fourth Affiliated Hospital of Soochow University, Suzhou, China
| | - Muchen Xu
- Department of Radiotherapy, The Fourth Affiliated Hospital of Soochow University, Suzhou, China
- Department of Radiotherapy, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Mengying Li
- Department of Gastroenterology, The Fourth Affiliated Hospital of Soochow University, Suzhou, China
| | - Yuqi Chen
- Department of Gastroenterology, The Fourth Affiliated Hospital of Soochow University, Suzhou, China
| | - Yi Zhang
- Department of General Surgery, The Fourth Affiliated Hospital of Soochow University, Suzhou, China
- Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Hong Chen
- Department of General Surgery, The Fourth Affiliated Hospital of Soochow University, Suzhou, China.
| | - Caifeng Jiang
- Department of Gastroenterology, The Fourth Affiliated Hospital of Soochow University, Suzhou, China.
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Cao B, Zhang C, Jiang M, Yang Y, Liu X. Development and validation of risk prediction models for permanent hypocalcemia after total thyroidectomy in patients with papillary thyroid carcinoma. Sci Rep 2025; 15:9348. [PMID: 40102549 PMCID: PMC11920412 DOI: 10.1038/s41598-025-93867-9] [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] [Subscribe] [Scholar Register] [Received: 07/20/2024] [Accepted: 03/10/2025] [Indexed: 03/20/2025] Open
Abstract
Hypocalcemia is a common complication and can be permanent in patients following total thyroidectomy (TT). The aim of this study was to identify factors associated with permanent hypocalcemia and to develop a validated risk prediction model for permanent hypocalcemia to assist surgeons in the appropriate follow-up of high-risk patients regarding supplemental therapy. We included data of 92 patients with papillary thyroid carcinoma (PTC) undergoing TT who were randomly allocated in a 7:3 ratio to a training set (n = 65) and validation set (n = 27). Univariate and multivariate logistic regression analyses revealed significant correlations of permanent hypocalcemia with parathyroid hormone (PTH) at postoperative month 1 (IM PTH), IM calcium (Ca), and IM phosphorus (P). These variables were constructed two models. Model 1 used the three indicators listed above; model 2 also included tumor, node, metastasis staging. The receiver operating characteristic (ROC) curve analysis showed that the areas under the curve (AUC) for models 1 and 2 were high for both the training set (0.905/0.913) and the validation set (0.894/0.800). Calibration curves showed good agreement between the incidence of permanent hypocalcemia estimated using the predictive models and the actual incidence. Model 1 may be more concise and convenient for clinical use.
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Affiliation(s)
- BoHan Cao
- Department of General Surgery, Benxi Central Hospital of China Medical University, No. 29 Shengli Street, Mingshan District, Benxi, 117000, Liaoning Province, China
| | - CanGang Zhang
- Department of General Surgery, Benxi Central Hospital of China Medical University, No. 29 Shengli Street, Mingshan District, Benxi, 117000, Liaoning Province, China
| | - MingMing Jiang
- Department of General Surgery, Benxi Central Hospital of China Medical University, No. 29 Shengli Street, Mingshan District, Benxi, 117000, Liaoning Province, China
| | - Yi Yang
- Department of General Surgery, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, 110004, Liaoning Province, China
| | - XiCai Liu
- Department of General Surgery, Benxi Central Hospital of China Medical University, No. 29 Shengli Street, Mingshan District, Benxi, 117000, Liaoning Province, China.
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Yu W, Xu Z, Li B, Zi M, Ren J, Wang W, Sun Q, Zhang Q, Wang D. Nomogram for pre-procedural prediction of lymph node metastasis in patients with submucosal early gastric cancer. Surg Endosc 2025; 39:1661-1671. [PMID: 39786464 DOI: 10.1007/s00464-024-11517-z] [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: 09/25/2024] [Accepted: 12/30/2024] [Indexed: 01/12/2025]
Abstract
BACKGROUND The treatment of early gastric cancer (EGC) is contingent upon the status of lymph node metastasis (LNM). Accurate preoperative prediction of LNM is critical for reducing unnecessary surgeries. This study seeks to evaluate the risk factors for LNM in submucosal EGC and develop a predictive model to optimize therapeutic decision-making. METHODS A retrospective analysis was performed on clinical data from 389 patients with T1b-stage EGC who underwent radical gastrectomy. Univariate and multivariate analyses were conducted to identify independent risk factors, followed by the development of a nomogram to predict LNM. The model's efficacy was validated through receiver operating characteristic curves, calibration curves, and decision curve analysis. RESULTS Of the 389 patients, 77 had LNM. Logistic regression analysis identified gender, CA199 levels, tumor location, degree of differentiation, presence of ulcers, and lymph node enlargement on CT as independent risk factors for LNM. A nomogram was constructed to assess the risk of LNM, demonstrating strong predictive accuracy with an area under the curve of 0.82 in the training set and 0.74 in the validation set, along with good sensitivity and positive predictive value. CONCLUSIONS This study presents a reliable preoperative nomogram to estimate the likelihood of LNM in submucosal EGC, providing valuable guidance for determining the most effective treatment strategies for patients.
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Affiliation(s)
- Wenhao Yu
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, 98 Nantong West Road, Yangzhou, 225001, Jiangsu, China
- Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, 225001, China
- General Surgery Institute of Yangzhou, Yangzhou University, Yangzhou, 225001, China
- Yangzhou Key Laboratory of Basic and Clinical Transformation of Digestive and Metabolic Diseases, Yangzhou, China
- Northern Jiangsu People's Hospital, Yangzhou, 225001, China
| | - Zijie Xu
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, 98 Nantong West Road, Yangzhou, 225001, Jiangsu, China
- Yangzhou Key Laboratory of Basic and Clinical Transformation of Digestive and Metabolic Diseases, Yangzhou, China
- Northern Jiangsu People's Hospital, Yangzhou, 225001, China
| | - Ben Li
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, 98 Nantong West Road, Yangzhou, 225001, Jiangsu, China
- Yangzhou Key Laboratory of Basic and Clinical Transformation of Digestive and Metabolic Diseases, Yangzhou, China
- Northern Jiangsu People's Hospital, Yangzhou, 225001, China
| | - Mengli Zi
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, 98 Nantong West Road, Yangzhou, 225001, Jiangsu, China
- Yangzhou Key Laboratory of Basic and Clinical Transformation of Digestive and Metabolic Diseases, Yangzhou, China
- Northern Jiangsu People's Hospital, Yangzhou, 225001, China
| | - Jun Ren
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, 98 Nantong West Road, Yangzhou, 225001, Jiangsu, China
- Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, 225001, China
- General Surgery Institute of Yangzhou, Yangzhou University, Yangzhou, 225001, China
- Yangzhou Key Laboratory of Basic and Clinical Transformation of Digestive and Metabolic Diseases, Yangzhou, China
- Northern Jiangsu People's Hospital, Yangzhou, 225001, China
| | - Wei Wang
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, 98 Nantong West Road, Yangzhou, 225001, Jiangsu, China
- Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, 225001, China
- General Surgery Institute of Yangzhou, Yangzhou University, Yangzhou, 225001, China
- Yangzhou Key Laboratory of Basic and Clinical Transformation of Digestive and Metabolic Diseases, Yangzhou, China
- Northern Jiangsu People's Hospital, Yangzhou, 225001, China
| | - Qiannan Sun
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, 98 Nantong West Road, Yangzhou, 225001, Jiangsu, China
- Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, 225001, China
- General Surgery Institute of Yangzhou, Yangzhou University, Yangzhou, 225001, China
- Yangzhou Key Laboratory of Basic and Clinical Transformation of Digestive and Metabolic Diseases, Yangzhou, China
- Northern Jiangsu People's Hospital, Yangzhou, 225001, China
| | - Qi Zhang
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, 98 Nantong West Road, Yangzhou, 225001, Jiangsu, China.
- Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, 225001, China.
- General Surgery Institute of Yangzhou, Yangzhou University, Yangzhou, 225001, China.
- Yangzhou Key Laboratory of Basic and Clinical Transformation of Digestive and Metabolic Diseases, Yangzhou, China.
- Northern Jiangsu People's Hospital, Yangzhou, 225001, China.
| | - Daorong Wang
- Department of Gastrointestinal Surgery, Northern Jiangsu People's Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, 98 Nantong West Road, Yangzhou, 225001, Jiangsu, China.
- Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, 225001, China.
- General Surgery Institute of Yangzhou, Yangzhou University, Yangzhou, 225001, China.
- Yangzhou Key Laboratory of Basic and Clinical Transformation of Digestive and Metabolic Diseases, Yangzhou, China.
- Northern Jiangsu People's Hospital, Yangzhou, 225001, China.
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Yang H, Dou J, Guo R, Sun M, Gao J, Shu HJ, Sun H, Zhao X, Song Y, Hou Y, Wang X, Luo D. Establishing and internally validating a predictive model for coronary heart disease incorporating triglyceride-glucose index, monocyte-to-high-density lipoprotein cholesterol ratio and conventional risk factors in patients experiencing chest pain and referred for invasive coronary angiography. Lipids Health Dis 2025; 24:72. [PMID: 40001112 PMCID: PMC11852830 DOI: 10.1186/s12944-025-02486-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2024] [Accepted: 02/12/2025] [Indexed: 02/27/2025] Open
Abstract
BACKGROUND Coronary heart disease (CHD) represents a severe form of ischemic cardiac condition that necessitates timely and accurate diagnosis. Although coronary angiography (CAG) remains widely used to detect CHD, healthcare facilities, medical expenses, and equipment technology often limit its availability. Therefore, it is imperative to identify a non-invasive diagnostic approach with high accuracy for CHD. METHODS This cross-sectional research included patients with chest pain (≥ 18 years) hospitalized at Chengde Central Hospital between September 2020 and March 2024. Among the participants, 70% were split into the training, and 30% were randomly entered into the validation sets. In the training dataset, univariate and multivariate logistic regression analyses were rigorously employed to ascertain predictors of CHD. A model was formulated by incorporating these predictors in a nomogram, which was evaluated for accuracy using calibration curves. The model's discrimination was quantified by calculating the area under the receiver operating characteristic (ROC) curve, denoted as the area under the curve (AUC), and its clinical application value was determined through decision curve analysis (DCA). Finally, we compare our model against the pretest probability (PTP) calculated by the Update Diamond-Forrester Model (UDFM) as recommended by the ECS guidelines to comprehensively assess its performance. RESULTS This study included 1501 patients who presented with chest pain, with a mean age of 60.45 years, 865 males (57.60%). Multivariate logistic regression analysis revealed TyG index, MHR, male, age, diabetes, systolic blood pressure (SBP), regional wall motion abnormality (RWMA), ST-T changes, and low-density lipoprotein cholesterol (LDL-C) as independent predictors of CHD. A novel nomogram incorporating these independent risk factors exhibited high accuracy and perfect consistency, with a training set AUC calculated to be 0.733 (95% CI: 0.698-0.768), and the validation set maintained a strong performance at 0.721 (95% CI: 0.663-0.779). The calibration curves and the Hosmer-Lemeshow test confirmed the well-fitting model (P = 0.576 and P = 0.694). ROC curve analysis and DCA demonstrated that the model has robust forecasting capability. CONCLUSION The nomogram model in this study exhibited good discriminative ability, calibration, and a favorable net benefit. Its predictive performance exceeds that of the traditional PTP tool and may serve as a non-invasive and promising approach to aid clinicians in the early identification of CHD risk in patients presenting with chest pain.
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Affiliation(s)
- Huihui Yang
- Graduate School of Chengde Medical University, Chengde, 06700, China
| | - Jie Dou
- Graduate School of Chengde Medical University, Chengde, 06700, China
| | - Ruoling Guo
- Graduate School of Chengde Medical University, Chengde, 06700, China
| | - Mingliang Sun
- Graduate School of Chengde Medical University, Chengde, 06700, China
| | - Jie Gao
- Graduate School of Chengde Medical University, Chengde, 06700, China
| | - Hong-Jun Shu
- Department of Cardiology, Chengde Central Hospital, Second Clinical College of Chengde Medical University, Chengde, 067000, China
| | - Hewei Sun
- Department of Cardiology, Chengde Central Hospital, Second Clinical College of Chengde Medical University, Chengde, 067000, China
| | - Xintao Zhao
- Department of Cardiology, Chengde Central Hospital, Second Clinical College of Chengde Medical University, Chengde, 067000, China
| | - Yuhua Song
- Department of Cardiology, Chengde Central Hospital, Second Clinical College of Chengde Medical University, Chengde, 067000, China
| | - Yanchun Hou
- Department of Cardiology, Chengde Central Hospital, Second Clinical College of Chengde Medical University, Chengde, 067000, China
| | - Xiaojun Wang
- Department of Endocrinology, Chengde Central Hospital, Second Clinical College of Chengde Medical University, Chengde, 067000, China.
| | - Donglei Luo
- Department of Cardiology, Chengde Central Hospital, Second Clinical College of Chengde Medical University, Chengde, 067000, China.
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10
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Li Q, Cen W, Yang T, Tao S. Development and validation of a risk prediction model for older adults with social isolation in China. BMC Public Health 2024; 24:2600. [PMID: 39334267 PMCID: PMC11428333 DOI: 10.1186/s12889-024-20142-3] [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: 12/23/2023] [Accepted: 09/19/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Older adults are vulnerable to social isolation due to declining physical and cognitive function, decreased interpersonal interactions, and reduced outdoor activities after retirement. This study aimed to develop and validate a predictive model to assess the risk of social isolation among older adults in China. METHODS Using data from the 2011 China Health and Retirement Longitudinal Study (CHARLS). The study cohort was randomly divided into training and validation groups in a 70:30 ratio. We used least absolute shrinkage and selection operator (LASSO) regression analysis with tenfold cross-validation to identify optimal predictive factors and examined the correlates of social isolation using logistic regression. A nomogram was constructed for the predictive model, and its accuracy was assessed using calibration curves. The predictive performance of the model was assessed using area under the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). RESULTS From the 2011 CHARLS database, 4,747 older adults were included in the final analysis, of whom 1,654 (34.8%) experienced social isolation. Multifactorial logistic regression identified educational level, marital status, gender, physical activity, physical self -maintenance ability, and number of children as predictive factors for social isolation. The predictive model achieved an AUC of 0.739 (95%CI = 0.722-0.756) in the training set and 0.708 (95%CI = 0.681-0.735) in the validation set. The Hosmer-Lemeshow test yielded P values of 0.111 and 0.324, respectively (both P > 0.05), indicating significant agreement between the nomogram and observed outcomes. The nomogram showed excellent predictive ability according to ROC and DCA. CONCLUSIONS The predictive model developed to assess the risk of social isolation in the Chinese older adults shows promising utility for early screening and intervention by clinical healthcare professionals.
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Affiliation(s)
- Qiugui Li
- School of Nursing, Jinan University, Guangzhou, Guangdong, China
| | - Wenjiao Cen
- School of Nursing, Jinan University, Guangzhou, Guangdong, China
| | - Tao Yang
- Department of Neurosurgery, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Shengru Tao
- Department of Healthcare-associated Infection Management, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
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Teng F, Zhu Q, Zhou XL, Shi YB, Sun H. Preoperative predictive model for the probability of lymph node metastasis in gastric cancer: a retrospective study. Front Oncol 2024; 14:1473423. [PMID: 39399177 PMCID: PMC11466724 DOI: 10.3389/fonc.2024.1473423] [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: 07/31/2024] [Accepted: 09/11/2024] [Indexed: 10/15/2024] Open
Abstract
Background Effectively diagnosing lymph node (LN) metastasis (LNM) is crucial in determining the condition of patients with gastric cancer (GC). The present study was devised to develop and validate a preoperative predictive model (PPM) capable of assessing the LNM status of individuals with GC. Methods A retrospective analysis of consecutive GC patients from two centers was conducted over the period from January 2021 to December 2023. These patients were utilized to construct a 289-patient training cohort for identifying LNM-related risk factors and developing a PPM, as well as a 90-patient testing cohort used for PPM validation. Results Of the GC patients included in the training cohort, 67 (23.2%) and 222 (76.8%) were respectively LNM negative and positive. Risk factors independently related to LNM status included cT3 invasion (P = 0.001), CT-reported LN (+) (P = 0.044), and CA199 value (P = 0.030). LNM risk scores were established with the following formula: score = -2.382 + 0.694×CT-reported LN status (+: 1; -: 0)+2.497×invasion depth (cT1: 0; cT2: 1; cT3: 2)+0.032×CA199 value. The area under the curve (AUC) values for PPM and CT-reported LN status were 0.753 and 0.609, respectively, with a significant difference between them (P < 0.001). When clinical data from the testing cohort was included in the PPM, the AUC values for the PPM and CT-reported LN status were 0.756 and 0.568 (P < 0.001). Conclusions The established PPM may be an effective technique for predicting the LNM status of patients preoperatively. This model can better diagnose LNM than CT-reported LN status alone, this model is better able to diagnose LNM.
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Affiliation(s)
- Fei Teng
- Department of Interventional Radiology, The First Affiliated Hospital of Ningbo University, Ningbo, China
| | - Qian Zhu
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Xi-Lang Zhou
- Department of Gastroenterology, Xuzhou Central Hospital, Xuzhou, China
| | - Yi-Bing Shi
- Department of Radiology, Xuzhou Central Hospital, Xuzhou, China
| | - Han Sun
- Department of Gastroenterology, Xuzhou Central Hospital, Xuzhou, China
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12
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Lee S, Jeon J, Park J, Chang YH, Shin CM, Oh MJ, Kim SH, Kang S, Park SH, Kim SG, Lee HJ, Yang HK, Lee HS, Cho SJ. An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video). Gastric Cancer 2024; 27:1088-1099. [PMID: 38954175 PMCID: PMC11335909 DOI: 10.1007/s10120-024-01524-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 06/18/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND Accurate prediction of pathologic results for early gastric cancer (EGC) based on endoscopic findings is essential in deciding between endoscopic and surgical resection. This study aimed to develop an artificial intelligence (AI) model to assess comprehensive pathologic characteristics of EGC using white-light endoscopic images and videos. METHODS To train the model, we retrospectively collected 4,336 images and prospectively included 153 videos from patients with EGC who underwent endoscopic or surgical resection. The performance of the model was tested and compared to that of 16 endoscopists (nine experts and seven novices) using a mutually exclusive set of 260 images and 10 videos. Finally, we conducted external validation using 436 images and 89 videos from another institution. RESULTS After training, the model achieved predictive accuracies of 89.7% for undifferentiated histology, 88.0% for submucosal invasion, 87.9% for lymphovascular invasion (LVI), and 92.7% for lymph node metastasis (LNM), using endoscopic videos. The area under the curve values of the model were 0.992 for undifferentiated histology, 0.902 for submucosal invasion, 0.706 for LVI, and 0.680 for LNM in the test. In addition, the model showed significantly higher accuracy than the experts in predicting undifferentiated histology (92.7% vs. 71.6%), submucosal invasion (87.3% vs. 72.6%), and LNM (87.7% vs. 72.3%). The external validation showed accuracies of 75.6% and 71.9% for undifferentiated histology and submucosal invasion, respectively. CONCLUSIONS AI may assist endoscopists with high predictive performance for differentiation status and invasion depth of EGC. Further research is needed to improve the detection of LVI and LNM.
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Affiliation(s)
- Seunghan Lee
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | | | | | - Young Hoon Chang
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoungnam-Si, Gyeonggi-Do, Republic of Korea
| | - Cheol Min Shin
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seoungnam-Si, Gyeonggi-Do, Republic of Korea
| | - Mi Jin Oh
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Su Hyun Kim
- Center for Health Promotion and Optimal Aging, Seoul National University Hospital, Seoul, Republic of Korea
| | - Seungkyung Kang
- Center for Health Promotion and Optimal Aging, Seoul National University Hospital, Seoul, Republic of Korea
| | - Su Hee Park
- Center for Health Promotion and Optimal Aging, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sang Gyun Kim
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea
| | - Hyuk-Joon Lee
- Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Han-Kwang Yang
- Department of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hey Seung Lee
- Department of Pathology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Soo-Jeong Cho
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, 101 Daehak-Ro, Jongno-Gu, Seoul, 03080, Republic of Korea.
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Seo JW, Park KB, Lim ST, Jun KH, Chin HM. Machine learning models for prediction of lymph node metastasis in patients with T1b gastric cancer. Am J Cancer Res 2024; 14:3842-3851. [PMID: 39267667 PMCID: PMC11387857 DOI: 10.62347/krel8138] [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: 06/19/2024] [Accepted: 08/06/2024] [Indexed: 09/15/2024] Open
Abstract
The prognosis of early gastric cancer (EGC) patients is associated with lymph node metastasis (LNM). Considering the relatively high rate of LNM in T1b EGC patients, it is crucial to determine the factors associated with LNM. In this study, we constructed and validated predictive models based on machine learning (ML) algorithms for LNM in patients with T1b EGC. Data from patients with T1b gastric cancer were extracted from the Korean Gastric Cancer Association database. ML algorithms such as logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM) were applied for model construction utilizing five-fold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical applicability. Moreover, external validation of XGBoost models was performed using the T1b gastric cancer database of The Catholic University Medical Center. In total, 3,468 T1b EGC patients were included in the analysis, whom 550 (15.9%) had LNM. Eleven variables were selected to construct the models. The LR, RF, XGBoost, and SVM models were established, revealing area under the receiver operating characteristic curve values of 0.8284, 0.7921, 0.8776, and 0.8323, respectively. Among the models, the XGBoost model exhibited the best predictive performance in terms of discrimination, calibration, and clinical applicability. ML models are reliable for predicting LNM in T1b EGC patients. The XGBoost model exhibited the best predictive performance and can be used by surgeons for the identification of EGC patients with a high-risk of LNM, thereby facilitating treatment selection.
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Affiliation(s)
- Ji Won Seo
- Department of Surgery, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea Seoul, Republic of Korea
| | - Ki Bum Park
- Department of Surgery, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea Seoul, Republic of Korea
| | - Seung Taek Lim
- Department of Surgery, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea Seoul, Republic of Korea
| | - Kyong Hwa Jun
- Department of Surgery, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea Seoul, Republic of Korea
| | - Hyung Min Chin
- Department of Surgery, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea Seoul, Republic of Korea
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Liu DY, Hu JJ, Zhou YQ, Tan AR. Analysis of lymph node metastasis and survival prognosis in early gastric cancer patients: A retrospective study. World J Gastrointest Surg 2024; 16:1637-1646. [PMID: 38983358 PMCID: PMC11230020 DOI: 10.4240/wjgs.v16.i6.1637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/08/2024] [Accepted: 05/06/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Early gastric cancer (EGC) is a common malignant tumor of the digestive system, and its lymph node metastasis and survival prognosis have been concerning. By retrospectively analyzing the clinical data of EGC patients, we can better understand the status of lymph node metastasis and its impact on survival and prognosis. AIM To evaluate the prognosis of EGC patients and the factors that affect lymph node metastasis. METHODS The clinicopathological data of 1011 patients with EGC admitted to our hospital between January 2015 and December 2023 were collected in a retrospective cohort study. There were 561 males and 450 females. The mean age was 58 ± 11 years. The patient underwent radical gastrectomy. The status of lymph node metastasis in each group was determined according to the pathological examination results of surgical specimens. The outcomes were as follows: (1) Lymph node metastasis in EGC patients; (2) Analysis of influencing factors of lymph node metastasis in EGC; and (3) Analysis of prognostic factors in patients with EGC. Normally distributed measurement data are expressed as mean ± SD, and a t test was used for comparisons between groups. The data are expressed as absolute numbers or percentages, and the chi-square test was used for comparisons between groups. Rank data were compared using a nonparametric rank sum test. A log-rank test and a logistic regression model were used for univariate analysis. A logistic stepwise regression model and a Cox stepwise regression model were used for multivariate analysis. The Kaplan-Meier method was used to calculate the survival rate and construct survival curves. A log-rank test was used for survival analysis. RESULTS Analysis of influencing factors of lymph node metastasis in EGC. The results of the multifactor analysis showed that tumor length and diameter, tumor site, tumor invasion depth, vascular thrombus, and tumor differentiation degree were independent influencing factors for lymph node metastasis in patients with EGC (odds ratios = 1.80, 1.49, 2.65, 5.76, and 0.60; 95%CI: 1.29-2.50, 1.11-2.00, 1.81-3.88, 3.87-8.59, and 0.48-0.76, respectively; P < 0.05). Analysis of prognostic factors in patients with EGC. All 1011 patients with EGC were followed up for 43 (0-13) months. The 3-year overall survival rate was 97.32%. Multivariate analysis revealed that age > 60 years and lymph node metastasis were independent risk factors for prognosis in patients with EGC (hazard ratio = 9.50, 2.20; 95%CI: 3.31-27.29, 1.00-4.87; P < 0.05). Further analysis revealed that the 3-year overall survival rates of gastric cancer patients aged > 60 years and ≤ 60 years were 99.37% and 94.66%, respectively, and the difference was statistically significant (P < 0.05). The 3-year overall survival rates of patients with and without lymph node metastasis were 95.42% and 97.92%, respectively, and the difference was statistically significant (P < 0.05). CONCLUSION The lymph node metastasis rate of EGC patients was 23.64%. Tumor length, tumor site, tumor infiltration depth, vascular cancer thrombin, and tumor differentiation degree were found to be independent factors affecting lymph node metastasis in EGC patients. Age > 60 years and lymph node metastasis are independent risk factors for EGC prognosis.
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Affiliation(s)
- Dong-Yuan Liu
- Department of General Surgery, The 971st Hospital of Chinese People's Liberation Army, Qingdao 266071, Shandong Province, China
| | - Jin-Jin Hu
- Department of Chest Surgery, Feicheng People's Hospital, Feicheng 271600, Shandong Province, China
| | - Yong-Quan Zhou
- Department of Gastrointestinal Surgery, Zhongshan Hospital of Fudan University, Shanghai 200032, China
| | - Ai-Rong Tan
- Department of Oncology, Qingdao Municipal Hospital, Qingdao 266000, Shandong Province, China
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Qiu P, Guo Q, Pan K, Lin J. Development of a nomogram for prediction of central lymph node metastasis of papillary thyroid microcarcinoma. BMC Cancer 2024; 24:235. [PMID: 38378515 PMCID: PMC10877775 DOI: 10.1186/s12885-024-12004-3] [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: 08/10/2023] [Accepted: 02/14/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Papillary thyroid carcinoma (PTC) is the most frequent malignant tumor in thyroid carcinoma. The aim of this study was to explore the risk factors associated with central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) and establish a nomogram model that can assess the probability of central lymph node metastasis (CLNM). METHODS The clinicopathological data of 377 patients with cN0 PTMC were collected and analyzed from The Second Affiliated Hospital of Fujian Medical University from July 1st, 2019 to December 30th, 2021. All patients were examined by underwent ultrasound (US), found without metastasis to central lymph nodes, and diagnosed with PTMC through pathologic examination. All patients received thyroid lobectomy or total thyroidectomy with therapeutic or prophylactic central lymph node dissection (CLND). R software (Version 4.1.0) was employed to conduct a series of statistical analyses and establish the nomogram. RESULTS A total of 119 patients with PTMC had central lymph node metastases (31.56%). After that, age (P < 0.05), gender (P < 0.05), tumor size (P < 0.05), tumor multifocality (P < 0.05), and ultrasound imaging-suggested tumor boundaries (P < 0.05) were identified as the risk factors associated with CLNM. Subsequently, multivariate logistic regression analysis indicated that the area under the receiver operating characteristic (ROC) curve (AUC) of the training cohort was 0.703 and that of the validation cohort was 0.656, demonstrating that the prediction ability of this model is relatively good compared to existing models. The calibration curves indicated a good fit for the nomogram model. Finally, the decision curve analysis (DCA) showed that a probability threshold of 0.15-0.50 could benefit patients clinically. The probability threshold used in DCA captures the relative value the patient places on receiving treatment for the disease, if present, compared to the value of avoiding treatment if the disease is not present. CONCLUSION CLNM is associated with many risk factors, including age, gender, tumor size, tumor multifocality, and ultrasound imaging-suggested tumor boundaries. The nomogram established in our study has moderate predictive ability for CLNM and can be applied to the clinical management of patients with PTMC. Our findings will provide a better preoperative assessment and treatment strategies for patients with PTMC whether to undergo central lymph node dissection.
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Affiliation(s)
- Pengjun Qiu
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Qiaonan Guo
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Kelun Pan
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Jianqing Lin
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
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Wu H, Liu W, Yin M, Liu L, Qu S, Xu W, Xu C. A nomogram based on platelet-to-lymphocyte ratio for predicting lymph node metastasis in patients with early gastric cancer. Front Oncol 2023; 13:1201499. [PMID: 37719022 PMCID: PMC10502215 DOI: 10.3389/fonc.2023.1201499] [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: 04/06/2023] [Accepted: 08/10/2023] [Indexed: 09/19/2023] Open
Abstract
Background Preoperative assessment of the presence of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) remains difficult. We aimed to develop a practical prediction model based on preoperative pathological data and inflammatory or nutrition-related indicators. Methods This study retrospectively analyzed the clinicopathological characteristics of 1,061 patients with EGC who were randomly divided into the training set and validation set at a ratio of 7:3. In the training set, we introduced the least absolute selection and shrinkage operator (LASSO) algorithm and multivariate logistic regression to identify independent risk factors and construct the nomogram. Both internal validation and external validation were performed by the area under the receiver operating characteristic curve (AUC), C-index, calibration curve, and decision curve analysis (DCA). Results LNM occurred in 162 of 1,061 patients, and the rate of LNM was 15.27%. In the training set, four variables proved to be independent risk factors (p < 0.05) and were incorporated into the final model, including depth of invasion, tumor size, degree of differentiation, and platelet-to-lymphocyte ratio (PLR). The AUC values were 0.775 and 0.792 for the training and validation groups, respectively. Both calibration curves showed great consistency in the predictive and actual values. The Hosmer-Lemeshow (H-L) test was carried out in two cohorts, showing excellent performance with p-value >0.05 (0.684422, 0.7403046). Decision curve analysis demonstrated a good clinical benefit in the respective set. Conclusion We established a preoperative nomogram including depth of invasion, tumor size, degree of differentiation, and PLR to predict LNM in EGC patients and achieved a good performance.
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Affiliation(s)
| | | | | | | | | | | | - Chunfang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China
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Paredes O, Baca C, Cruz R, Paredes K, Luque-Vasquez C, Chavez I, Taxa L, Ruiz E, Berrospi F, Payet E. Predictive factors of lymphatic metastasis and evaluation of the Japanese treatment guidelines for endoscopic resection of early gastric cancer in a high-volume center in Perú. Heliyon 2023; 9:e16293. [PMID: 37251889 PMCID: PMC10209413 DOI: 10.1016/j.heliyon.2023.e16293] [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: 06/18/2022] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 05/31/2023] Open
Abstract
Purpose This study aimed to identify the predictive factors of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) and to evaluate the applicability of the Japanese treatment guidelines for endoscopic resection in the western population. Methods Five hundred-one patients with pathological diagnoses of EGC were included. Univariate and multivariate analyses were conducted to identify the predictive factors of LNM. EGC patients were distributed according to the indications for endoscopic resection of the Eastern guidelines. The incidence of LNM was evaluated in each group. Results From 501 patients with EGC, 96 (19.2%) presented LNM. In 279 patients with tumors with submucosal infiltration (T1b), 83 (30%) patients had LNM. Among 219 patients who presented tumors > 3 cm, 63 (29%) patients had LNM. Thirty-one percent of patients with ulcerated tumors presented LMN (33 out of 105). In 76 patients and 24 patients with lymphovascular and perineural invasion, the percentage of LMN was 84% and 87%, respectively. In the multivariate analysis, a tumor diameter >3 cm, submucosal invasion, lymphovascular, and perineural invasion were independent predictors of LMN in EGC. No patient with differentiated, non-ulcerated mucosal tumors presented LNM regardless of tumor size. Three of 17 patients (18%) with differentiated, ulcerated mucosal tumors and ≤ 3 cm presented LNM. No LNM was evidenced in patients with undifferentiated mucosal tumors and ≤ 2 cm. Conclusions The presence of LNM in Western EGC patients was independently related to larger tumors (>3 cm), submucosal invasion, lymphovascular and perineural invasion. The Japanese absolute indications for EMR are safe in the Western population. Likewise, Western patients with differentiated, non-ulcerated mucosal tumors, and larger than 2 cm are susceptible to endoscopic resection. Patients with undifferentiated mucosal tumors smaller than 2 cm presented encouraging results and ESD could be recommended only for selected cases.
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Affiliation(s)
- Oscar Paredes
- Department of Abdominal Surgery, National Institute of Neoplastic Diseases INEN, Lima, Peru
| | - Carlos Baca
- Department of Abdominal Surgery, National Institute of Neoplastic Diseases INEN, Lima, Peru
| | - Renier Cruz
- Department of Pathology, National Institute of Neoplastic Disease INEN, Lima, Peru
| | - Kori Paredes
- Department of Abdominal Surgery, National Institute of Neoplastic Diseases INEN, Lima, Peru
| | - Carlos Luque-Vasquez
- Department of Abdominal Surgery, National Institute of Neoplastic Diseases INEN, Lima, Peru
| | - Iván Chavez
- Department of Abdominal Surgery, National Institute of Neoplastic Diseases INEN, Lima, Peru
| | - Luis Taxa
- Department of Pathology, National Institute of Neoplastic Disease INEN, Lima, Peru
| | - Eloy Ruiz
- Department of Abdominal Surgery, National Institute of Neoplastic Diseases INEN, Lima, Peru
| | - Francisco Berrospi
- Department of Abdominal Surgery, National Institute of Neoplastic Diseases INEN, Lima, Peru
| | - Eduardo Payet
- Department of Abdominal Surgery, National Institute of Neoplastic Diseases INEN, Lima, Peru
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Bu F, Deng XH, Zhan NN, Cheng H, Wang ZL, Tang L, Zhao Y, Lyu QY. Development and validation of a risk prediction model for frailty in patients with diabetes. BMC Geriatr 2023; 23:172. [PMID: 36973658 PMCID: PMC10045211 DOI: 10.1186/s12877-023-03823-3] [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: 11/28/2022] [Accepted: 02/14/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Frailty is the third most common complication of diabetes after macrovascular and microvascular complications. The aim of this study was to develop a validated risk prediction model for frailty in patients with diabetes. METHODS The research used data from the China Health and Retirement Longitudinal Study (CHARLS), a dataset representative of the Chinese population. Twenty-five indicators, including socio-demographic variables, behavioral factors, health status, and mental health parameters, were analyzed in this study. The study cohort was randomly divided into a training set and a validation set at a ratio of 70 to 30%. LASSO regression analysis was used to screen the variables for the best predictors of the model based on a 10-fold cross-validation. The logistic regression model was applied to explore the associated factors of frailty in patients with diabetes. A nomogram was constructed to develop the prediction model. Calibration curves were applied to evaluate the accuracy of the nomogram model. The area under the receiver operating characteristic curve and decision curve analysis were conducted to assess predictive performance. RESULTS One thousand four hundred thirty-six patients with diabetes from the CHARLS database collected in 2013 (n = 793) and 2015 (n = 643) were included in the final analysis. A total of 145 (10.9%) had frailty symptoms. Multivariate logistic regression analysis showed that marital status, activities of daily living, waist circumference, cognitive function, grip strength, social activity, and depression as predictors of frailty in people with diabetes. These factors were used to construct the nomogram model, which showed good concordance and accuracy. The AUC values of the predictive model and the internal validation set were 0.912 (95%CI 0.887-0.937) and 0.881 (95% CI 0.829-0.934). Hosmer-Lemeshow test values were P = 0.824 and P = 0.608 (both > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had a good predictive performance. CONCLUSIONS Comprehensive nomogram constructed in this study was a promising and convenient tool to evaluate the risk of frailty in patients with diabetes, and contributed clinicians to screening the high-risk population.
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Affiliation(s)
- Fan Bu
- School of Nursing, Jinan University, No. 601, West Huangpu Avenue, Guangzhou, People's Republic of China
| | - Xiao-Hui Deng
- School of Nursing, Jinan University, No. 601, West Huangpu Avenue, Guangzhou, People's Republic of China
| | - Na-Ni Zhan
- School of Nursing, Jinan University, No. 601, West Huangpu Avenue, Guangzhou, People's Republic of China
| | - Hongtao Cheng
- School of Nursing, Jinan University, No. 601, West Huangpu Avenue, Guangzhou, People's Republic of China
| | - Zi-Lin Wang
- School of Nursing, Jinan University, No. 601, West Huangpu Avenue, Guangzhou, People's Republic of China
| | - Li Tang
- School of Nursing, Jinan University, No. 601, West Huangpu Avenue, Guangzhou, People's Republic of China
| | - Yu Zhao
- School of Nursing, Jinan University, No. 601, West Huangpu Avenue, Guangzhou, People's Republic of China
| | - Qi-Yuan Lyu
- School of Nursing, Jinan University, No. 601, West Huangpu Avenue, Guangzhou, People's Republic of China.
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Li J, Cui T, Huang Z, Mu Y, Yao Y, Xu W, Chen K, Liu H, Wang W, Chen X. Analysis of risk factors for lymph node metastasis and prognosis study in patients with early gastric cancer: A SEER data-based study. Front Oncol 2023; 13:1062142. [PMID: 37007147 PMCID: PMC10064290 DOI: 10.3389/fonc.2023.1062142] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 03/06/2023] [Indexed: 03/19/2023] Open
Abstract
BackgroundLymph node status is an important factor in determining the prognosis of patients with early gastric cancer (EGC) and preoperative diagnosis of lymph node metastasis (LNM) has some limitations. This study explored the risk factors and independent prognostic factors of LNM in EGC patients and constructed a clinical prediction model to predict LNM.MethodsClinicopathological data of EGC patients was collected from the public Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression was used to identify risk factors for LNM in EGC patients. The performance of the LNM model was evaluated by C-index, calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis (DCA) curve, and clinical impact curve (CIC) based on the results of multivariate regression to develop a nomogram. An independent data set was obtained from China for external validation. The Kaplan-Meier method and Cox regression model were used to identify potential prognostic factors for overall survival (OS) in EGC patients.ResultsA total of 3993 EGC patients were randomly allocated to a training cohort (n=2797) and a validation cohort (n=1196). An external cohort of 106 patients from the Second Hospital of Lanzhou University was used for external validation. Univariate and multivariate logistic regression showed that age, tumor size, differentiation, and examined lymph nodes count (ELNC) were independent risk factors for LNM. Nomogram for predicting LNM in EGC patients was developed and validated. The predictive model had a good discriminatory performance with a concordance index (C-index) of 0.702 (95% CI: 0.679-0.725). The calibration plots showed that the predicted LNM probabilities were the same as the actual observations in both the internal validation cohort and external validation cohort. The AUC values for the training cohort, internal validation cohort and external validation cohort were 0.702 (95% CI: 0.679-0.725), 0.709 (95% CI: 0.674-0.744) and 0.750(95% CI: 0.607-0.892), respectively, and the DCA curves and CIC showed good clinical applicability. The Cox regression model identified age, sex, race, primary site, size, pathological type, LNM, distant metastasis, and ELNC were prognostic factors for OS in EGC patients, while a year at diagnosis, grade, marital status, radiotherapy, and chemotherapy were not independent prognostic factors.ConclusionIn this study, we identified risk factors and independent prognostic factors for the development of LNM in EGC patients, and developed a relatively accurate model to predict the development of LNM in EGC patients.
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Affiliation(s)
- Jinzhou Li
- The Second Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Ting Cui
- The Second Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Zeping Huang
- Department of General Surgery, The Second Hospital of Lanzhou University, Lanzhou, China
| | - Yanxi Mu
- The Second Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Yalong Yao
- The Second Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Wei Xu
- The Second Clinical Medical College, Lanzhou University, Lanzhou, China
| | - Kang Chen
- Department of General Surgery, The Second Hospital of Lanzhou University, Lanzhou, China
| | - Haipeng Liu
- Department of General Surgery, The Second Hospital of Lanzhou University, Lanzhou, China
| | - Wenjie Wang
- Department of General Surgery, The Second Hospital of Lanzhou University, Lanzhou, China
- *Correspondence: Xiao Chen, ; Wenjie Wang,
| | - Xiao Chen
- Department of General Surgery, The Second Hospital of Lanzhou University, Lanzhou, China
- *Correspondence: Xiao Chen, ; Wenjie Wang,
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Guo X, Song X, Long X, Liu Y, Xie Y, Xie C, Ji B. New nomogram for predicting lymph node positivity in pancreatic head cancer. Front Oncol 2023; 13:1053375. [PMID: 36761960 PMCID: PMC9907461 DOI: 10.3389/fonc.2023.1053375] [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: 09/25/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Pancreatic cancer is one of the most malignant cancers worldwide, and it mostly occurs in the head of the pancreas. Existing laparoscopic pancreaticoduodenectomy (LPD) surgical techniques have has undergone a learning curve, a wide variety of approaches for the treatment of pancreatic cancer have been proposed, and the operation has matured. At present, pancreatic head cancer has been gradually changing from "surgeons' evaluation of anatomical resection" to "biologically inappropriate resection". In this study, the risk of lymph node metastasis in pancreatic head cancer was predicted using common preoperative clinical indicators. METHODS The preoperative clinical data of 191 patients with pancreatic head cancer who received LPD in the First Affiliated Hospital of Jilin University from May 2016 to December 2021 were obtained. A univariate regression analysis study was conducted, and the indicators with a significance level of P<0.05 were included in the univariate logistic regression analysis into multivariate. Lastly, a nomogram was built based on age, tumor size, leucocyte,albumin(ALB), and lymphocytes/monocytes(LMR). The model with the highest resolution was selected by obtaining the area under a curve. The clinical net benefit of the prediction model was examined using decision curve analyses.Risk stratification was performed by combining preoperative CT scan with existing models. RESULTS Multivariate logistic regression analysis found age, tumor size, WBC, ALB, and LMR as five independent factors. A nomogram model was constructed based on the above indicators. The model was calibrated by validating the calibration curve within 1000 bootstrap resamples. The ROC curve achieved an AUC of 0.745(confidence interval of 95%: 0.673-0.816), thus indicating that the model had excellent discriminative skills. DCA suggested that the predictive model achieved a high net benefit in the nearly entire threshold probability range. CONCLUSIONS This study has been the first to investigate a nomogram for preoperative prediction of lymphatic metastasis in pancreatic head cancer. The result suggests that age, ALB, tumor size, WBC, and LMR are independent risk factors for lymph node metastasis in pancreatic head cancer. This study may provide a novel perspective for the selection of appropriate continuous treatment regimens, the increase of the survival rate of patients with pancreatic head cancer, and the selection of appropriate neoadjuvant therapy patients.
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Affiliation(s)
| | | | | | | | | | | | - Bai Ji
- The Department of General Surgery Center-Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Jilin University, Changchun, China
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Zhao L, Han W, Niu P, Lu Y, Zhang F, Jiao F, Zhou X, Wang W, Luan X, He M, Guan Q, Li Y, Nie Y, Wu K, Zhao D, Chen Y. Using nomogram, decision tree, and deep learning models to predict lymph node metastasis in patients with early gastric cancer: a multi-cohort study. Am J Cancer Res 2023; 13:204-215. [PMID: 36777507 PMCID: PMC9906085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 12/30/2022] [Indexed: 02/14/2023] Open
Abstract
The accurate assessment of lymph node metastasis (LNM) in patients with early gastric cancer is critical to the selection of the most appropriate surgical treatment. This study aims to develop an optimal LNM prediction model using different methods, including nomogram, Decision Tree, Naive Bayes, and deep learning methods. In this study, we included two independent datasets: the gastrectomy set (n=3158) and the endoscopic submucosal dissection (ESD) set (n=323). The nomogram, Decision Tree, Naive Bayes, and fully convolutional neural networks (FCNN) models were established based on logistic regression analysis of the development set. The predictive power of the LNM prediction models was revealed by time-dependent receiver operating characteristic (ROC) curves and calibration plots. We then used the ESD set as an external cohort to evaluate the models' performance. In the gastrectomy set, multivariate analysis showed that gender (P=0.008), year when diagnosed (2006-2010 year, P=0.265; 2011-2015 year, P=0.001; and 2016-2020 year, P<0.001, respectively), tumor size (2-4 cm, P=0.001; and ≥4 cm, P<0.001, respectively), tumor grade (poorly-moderately, P=0.016; moderately, P<0.001; well-moderately, P<0.001; and well, P<0.001, respectively), vascular invasion (P<0.001), and pT stage (P<0.001) were independent risk factors for LNM in early gastric cancer. The area under the curve (AUC) for the validation set using the nomogram, Decision Tree, Naive Bayes, and FCNN models were 0.78, 0.76, 0.77, and 0.79, respectively. In conclusion, our multi-cohort study systematically investigated different LNM prediction methods for patients with early gastric cancer. These models were validated and shown to be reliable with AUC>0.76 for all. Specifically, the FCNN model showed the most accurate prediction of LNM risks in early gastric cancer patients with AUC=0.79. Based on the FCNN model, patients with LNM rates of >4.77% are strong candidates for gastrectomy rather than ESD surgery.
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Affiliation(s)
- Lulu Zhao
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing, China
| | - Weili Han
- State Key Laboratory of Cancer Biology and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical UniversityXi’an, Shaanxi, China
| | - Penghui Niu
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing, China
| | - Yuanyuan Lu
- State Key Laboratory of Cancer Biology and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical UniversityXi’an, Shaanxi, China
| | - Fan Zhang
- Lanzhou University Second HospitalLanzhou, Gansu, China
| | - Fuzhi Jiao
- The First Hospital of Lanzhou UniversityLanzhou, Gansu, China
| | - Xiadong Zhou
- Gansu Provincial Cancer HospitalLanzhou, Gansu, China
| | - Wanqing Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing, China
| | - Xiaoyi Luan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing, China
| | - Mingyan He
- Gansu Provincial Cancer HospitalLanzhou, Gansu, China
| | - Quanlin Guan
- The First Hospital of Lanzhou UniversityLanzhou, Gansu, China
| | - Yumin Li
- Lanzhou University Second HospitalLanzhou, Gansu, China
| | - Yongzhan Nie
- State Key Laboratory of Cancer Biology and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical UniversityXi’an, Shaanxi, China
| | - Kaichun Wu
- State Key Laboratory of Cancer Biology and National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Fourth Military Medical UniversityXi’an, Shaanxi, China
| | - Dongbing Zhao
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing, China
| | - Yingtai Chen
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing, China
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Liu Z, Tian H, Huang Y, Liu Y, Zou F, Huang C. Construction of a nomogram for preoperative prediction of the risk of lymph node metastasis in early gastric cancer. Front Surg 2023; 9:986806. [PMID: 36684356 PMCID: PMC9852636 DOI: 10.3389/fsurg.2022.986806] [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: 07/05/2022] [Accepted: 11/22/2022] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND The status of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) is particularly important for the formulation of clinical treatment. The purpose of this study was to construct a nomogram to predict the risk of LNM in EGC before operation. METHODS Univariate analysis and logistic regression analysis were used to determine the independent risk factors for LNM. The independent risk factors were included in the nomogram, and the prediction accuracy, discriminant ability and clinical practicability of the nomogram were evaluated by the receiver operating characteristic curve (ROC), calibration curve and clinical decision curve (DCA), and 100 times ten-fold cross-validation was used for internal validation. RESULTS 33 (11.3%) cases of AGC were pathologically confirmed as LNM. In multivariate analysis, T stage, presence of enlarged lymph nodes on CT examination, carbohydrate antigen 199 (CA199), undifferentiated histological type and systemic inflammatory response index (SIRI) were risk factors for LNM. The area under the ROC curve of the nomogram was 0.86, the average area under the ROC curve of the 100-fold ten-fold cross-validation was 0.85, and the P value of the Hosmer-Lemeshow test was 0.60. In addition, the clinical decision curve, net reclassification index (NRI) and Integrated Discriminant Improvement Index (IDI) showed that the nomogram had good clinical utility. CONCLUSIONS We found that SIRI is a novel biomarker for preoperative prediction of LNM in EGC, and constructed a nomogram for preoperative prediction of the risk of LNM in EGC, which is helpful for the formulation of the clinical treatment strategies.
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Affiliation(s)
- Zitao Liu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Huakai Tian
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yongshan Huang
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yu Liu
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Feilong Zou
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chao Huang
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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Liu Y, Wen H, Wang Q, Du S. Research trends in endoscopic applications in early gastric cancer: A bibliometric analysis of studies published from 2012 to 2022. Front Oncol 2023; 13:1124498. [PMID: 37114137 PMCID: PMC10129370 DOI: 10.3389/fonc.2023.1124498] [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: 12/15/2022] [Accepted: 03/13/2023] [Indexed: 04/29/2023] Open
Abstract
Background Endoscopy is the optimal method of diagnosing and treating early gastric cancer (EGC), and it is therefore important to keep up with the rapid development of endoscopic applications in EGC. This study utilized bibliometric analysis to describe the development, current research progress, hotspots, and emerging trends in this field. Methods We retrieved publications about endoscopic applications in EGC from 2012 to 2022 from Web of Science™ (Clarivate™, Philadelphia, PA, USA) Core Collection (WoSCC). We mainly used CiteSpace (version 6.1.R3) and VOSviewer (version 1.6.18) to perform the collaboration network analysis, co-cited analysis, co-occurrence analysis, cluster analysis, and burst detection. Results A total of 1,333 publications were included. Overall, both the number of publications and the average number of citations per document per year increased annually. Among the 52 countries/regions that were included, Japan contributed the most in terms of publications, citations, and H-index, followed by the Republic of Korea and China. The National Cancer Center, based in both Japan and the Republic of Korea, ranked first among institutions in terms of number of publications, citation impact, and the average number of citations. Yong Chan Lee was the most productive author, and Ichiro Oda had the highest citation impact. In terms of cited authors, Gotoda Takuji had both the highest citation impact and the highest centrality. Among journals, Surgical Endoscopy and Other Interventional Techniques had the most publications, and Gastric Cancer had the highest citation impact and H-index. Among all publications and cited references, a paper by Smyth E C et al., followed by one by Gotoda T et al., had the highest citation impact. Using keywords co-occurrence and cluster analysis, 1,652 author keywords were categorized into 26 clusters, and we then divided the clusters into six groups. The largest and newest clusters were endoscopic submucosal dissection and artificial intelligence (AI), respectively. Conclusions Over the last decade, research into endoscopic applications in EGC has gradually increased. Japan and the Republic of Korea have contributed the most, but research in this field in China, from an initially low base, is developing at a striking speed. However, a lack of collaboration among countries, institutions, and authors, is common, and this should be addressed in future. The main focus of research in this field (i.e., the largest cluster) is endoscopic submucosal dissection, and the topic at the frontier (i.e., the newest cluster) is AI. Future research should focus on the application of AI in endoscopy, and its implications for the clinical diagnosis and treatment of EGC.
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Affiliation(s)
- Yuan Liu
- Graduate School of Beijing University of Chinese Medicine, Beijing, China
| | - Haolang Wen
- Graduate School of Beijing University of Chinese Medicine, Beijing, China
| | - Qiao Wang
- Graduate School of Beijing University of Chinese Medicine, Beijing, China
| | - Shiyu Du
- Department of Gastroenterology, China-Japan Friendship Hospital, Beijing, China
- *Correspondence: Shiyu Du,
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Zeng Q, Feng Z, Zhu Y, Zhang Y, Shu X, Wu A, Luo L, Cao Y, Xiong J, Li H, Zhou F, Jie Z, Tu Y, Li Z. Deep learning model for diagnosing early gastric cancer using preoperative computed tomography images. Front Oncol 2022; 12:1065934. [PMID: 36531076 PMCID: PMC9748811 DOI: 10.3389/fonc.2022.1065934] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 11/07/2022] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Early gastric cancer (EGC) is defined as a lesion restricted to the mucosa or submucosa, independent of size or evidence of regional lymph node metastases. Although computed tomography (CT) is the main technique for determining the stage of gastric cancer (GC), the accuracy of CT for determining tumor invasion of EGC was still unsatisfactory by radiologists. In this research, we attempted to construct an AI model to discriminate EGC in portal venous phase CT images. METHODS We retrospectively collected 658 GC patients from the first affiliated hospital of Nanchang university, and divided them into training and internal validation cohorts with a ratio of 8:2. As the external validation cohort, 93 GC patients were recruited from the second affiliated hospital of Soochow university. We developed several prediction models based on various convolutional neural networks, and compared their predictive performance. RESULTS The deep learning model based on the ResNet101 neural network represented sufficient discrimination of EGC. In two validation cohorts, the areas under the curves (AUCs) for the receiver operating characteristic (ROC) curves were 0.993 (95% CI: 0.984-1.000) and 0.968 (95% CI: 0.935-1.000), respectively, and the accuracy was 0.946 and 0.914. Additionally, the deep learning model can also differentiate between mucosa and submucosa tumors of EGC. CONCLUSIONS These results suggested that deep learning classifiers have the potential to be used as a screening tool for EGC, which is crucial in the individualized treatment of EGC patients.
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Affiliation(s)
- Qingwen Zeng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zongfeng Feng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yang Zhang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xufeng Shu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Ahao Wu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Lianghua Luo
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yi Cao
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Jianbo Xiong
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Hong Li
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Zhigang Jie
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Yi Tu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Zhengrong Li
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
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Zhang X, Yang D, Wei Z, Yan R, Zhang Z, Huang H, Wang W. Establishment of a nomogram for predicting lymph node metastasis in patients with early gastric cancer after endoscopic submucosal dissection. Front Oncol 2022; 12:898640. [PMID: 36387114 PMCID: PMC9651963 DOI: 10.3389/fonc.2022.898640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 09/20/2022] [Indexed: 01/19/2023] Open
Abstract
Background Endoscopic submucosal dissection (ESD) has been accepted as the standard treatment for the appropriate indication of early gastric cancer (EGC). Determining the risk of lymph node metastasis (LNM) is critical for the following treatment selection after ESD. This study aimed to develop a predictive model to quantify the probability of LNM in EGC to help minimize the invasive procedures. Methods A total of 952 patients with EGC who underwent radical gastrectomy were retrospectively reviewed. LASSO regression was used to help screen the potential risk factors. Multivariate logistic regression was used to establish a predictive nomogram, which was subjected to discrimination and calibration evaluation, bootstrapping internal validation, and decision curve analysis. Results Results of multivariate analyses revealed that gender, fecal occult blood test, CEA, CA19-9, histologic differentiation grade, lymphovascular invasion, depth of infiltration, and Ki67 labeling index were independent prognostic factors for LNM. The nomogram had good discriminatory performance, with a concordance index of 0.816 (95% CI 0.781–0.853). The validation dataset yielded a corrected concordance index of 0.805 (95% CI 0.770–0.842). High agreements between ideal curves and calibration curves were observed. Conclusions The nomogram is clinically useful for predicting LNM after ESD in EGC, which is beneficial to identifying patients who are at low risk for LNM and would benefit from avoiding an unnecessary gastrectomy.
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Affiliation(s)
- Xin Zhang
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Dejun Yang
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Ziran Wei
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Ronglin Yan
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Zhengwei Zhang
- Department of Pathology, Second Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Hejing Huang
- Department of Ultrasound, Second Affiliated Hospital of Naval Medical University, Shanghai, China
- *Correspondence: Hejing Huang, ; Weijun Wang,
| | - Weijun Wang
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Shanghai, China
- *Correspondence: Hejing Huang, ; Weijun Wang,
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Zeng Q, Li H, Zhu Y, Feng Z, Shu X, Wu A, Luo L, Cao Y, Tu Y, Xiong J, Zhou F, Li Z. Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer. Front Med (Lausanne) 2022; 9:986437. [PMID: 36262277 PMCID: PMC9573999 DOI: 10.3389/fmed.2022.986437] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/09/2022] [Indexed: 01/19/2023] Open
Abstract
Background This study aims to develop and validate a predictive model combining deep transfer learning, radiomics, and clinical features for lymph node metastasis (LNM) in early gastric cancer (EGC). Materials and methods This study retrospectively collected 555 patients with EGC, and randomly divided them into two cohorts with a ratio of 7:3 (training cohort, n = 388; internal validation cohort, n = 167). A total of 79 patients with EGC collected from the Second Affiliated Hospital of Soochow University were used as external validation cohort. Pre-trained deep learning networks were used to extract deep transfer learning (DTL) features, and radiomics features were extracted based on hand-crafted features. We employed the Spearman rank correlation test and least absolute shrinkage and selection operator regression for feature selection from the combined features of clinical, radiomics, and DTL features, and then, machine learning classification models including support vector machine, K-nearest neighbor, random decision forests (RF), and XGBoost were trained, and their performance by determining the area under the curve (AUC) were compared. Results We constructed eight pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that 1,048 DTL features extracted based on the pre-trained Resnet152 network combined in the predictive model had the best performance in discriminating the LNM status of EGC, with an AUC of 0.901 (95% CI: 0.847-0.956) and 0.915 (95% CI: 0.850-0.981) in the internal validation and external validation cohorts, respectively. Conclusion We first utilized comprehensive multidimensional data based on deep transfer learning, radiomics, and clinical features with a good predictive ability for discriminating the LNM status in EGC, which could provide favorable information when choosing therapy options for individuals with EGC.
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Affiliation(s)
- Qingwen Zeng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
- Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hong Li
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Yanyan Zhu
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Zongfeng Feng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Xufeng Shu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Ahao Wu
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Lianghua Luo
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yi Cao
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Yi Tu
- Department of Pathology, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
| | - Jianbo Xiong
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Fuqing Zhou
- Department of Radiology, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
| | - Zhengrong Li
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Nanchang University, Nanchang, Jiangxi, China
- Institute of Digestive Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China
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Ding B, Luo P, Yong J. Model based on preoperative clinical characteristics to predict lymph node metastasis in patients with gastric cancer. Front Surg 2022; 9:976743. [PMID: 36211286 PMCID: PMC9538964 DOI: 10.3389/fsurg.2022.976743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/09/2022] [Indexed: 11/16/2022] Open
Abstract
Background The risk factors of lymph node metastasis (LNM) in gastric cancer (GC) remain controversial. We aimed to identify risk factors of LNM in GC and construct a predictive model. Methods A total of 1,337 resectable GC patients who underwent radical D2 lymphadenectomy at the first affiliated Hospital of Anhui Medical University from January 2011 to January 2014 were retrospectively analyzed and randomly divided into training and validation cohorts (n = 1,003 and n = 334, respectively) in a 3:1 ratio. Collecting indicators include age, gender, body mass index (BMI), tumor location, pathology, histological grade, tumor size, preoperative neutrophils to lymphocytes ratio (NLR), platelets to lymphocytes ratio (PLR), fibrinogen to albumin ratio (FAR), carcinoembryonic antigen (CEA), cancer antigen19-9 (CA19-9) and lymph nodes status. Significant risk factors were identified through univariate and multivariate logistic regression analysis, which were then included and presented as a nomogram. The performance of the model was assessed with receiver operating characteristic curves (ROC curves), calibration plots, and Decision curve analysis (DCA), and the risk groups were divided into low-and high-risk groups according to the cutoff value which was determined by the ROC curve. Results BMI, histological grade, tumor size, CEA, and CA19-9 were enrolled in the model as independent risk factors of LNM. The model showed good resolution, with a C-index of 0.716 and 0.727 in the training and validation cohort, respectively, and good calibration. The cutoff value for predicted probability is 0.594, the proportion of patients with LNM in the high-risk group was significantly higher than that in the low-risk group. Decision curve analysis also indicated that the model had a good positive net gain. Conclusions The nomogram-based prediction model developed in this study is stable with good resolution, reliability, and net gain. It can be used by clinicians to assess preoperative lymph node metastasis and risk stratification to develop individualized treatment plans.
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Affiliation(s)
- Baicheng Ding
- Department of Emergency Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Panquan Luo
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jiahui Yong
- Department of Transfusion, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Correspondence: Jiahui Yong
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Li Y, Zhang X. Prognostic nomograms for gastric carcinoma after surgery to assist decision-making for postoperative treatment with chemotherapy cycles <9 or chemotherapy cycles ≥9. Front Surg 2022; 9:916483. [PMID: 36090344 PMCID: PMC9458925 DOI: 10.3389/fsurg.2022.916483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveWe sought to develop novel nomograms to accurately predict overall survival (OS) of chemotherapy cycles <9 and chemotherapy cycles ≥9 and construct risk stratification to differentiate low-risk and high-risk of two cohorts.MethodsPatients who underwent curative-intent resection for gastric cancer between January 2002 and May 2020 at a single China institution were identified. Variables associated with OS were recorded and analyzed according to multivariable Cox models. Nomograms predicting 3- and 5-year OS were built according to variables resulting from multivariable Cox models. Discrimination ability was calculated using the Harrell's Concordance Index. The constructed nomogram was subjected to 1,000 resamples bootstrap for internal validation. Calibration curves for the new nomograms were used to test the consistency between the predicted and actual 3- and 5-year OS. Decision curve analysis (DCA) was performed to assess the clinical net benefit. The Concordance index (C-index) and time-dependent receiver operating characteristic (t-ROC) curves were used to evaluate and compare the discriminative abilities of the new nomograms. Finally, prognostic risk stratification of gastric cancer was conducted with X-tile software and nomograms converted into a risk-stratified prognosis model.ResultsFor the nomogram predict OS of chemotherapy cycles <9, C-index was 0.711 (95% CI, 0.663–0.760) in internal validation and 0.722 (95% CI, 0.662–0.783) in external validation, which was better than AJCC 8th edition TNM staging (internal validation: 0.627, 95% CI, 0.585–0.670) and (external validation: 0.595,95% CI, 0.543–0.648). The C-index of the nomogram for chemotherapy cycles ≥9 in internal validation was 0.755 (95% CI, 0.728–0.782) and 0.785 (95% CI, 0.747–0.823) in external validation, which was superior to the AJCC 8th edition TNM staging (internal validation: 0.712 95% CI, 0.688–0.737) and (external validation 0.734, 95% CI, 0.699–0.770).The calibration curves, t-ROC curves and DCA of the two nomogram models show that the recognition performance of the two nomogram models was outstanding. The statistical differences in the prognosis among the two risk stratification groups further showed that our model had an excellent risk stratification performance.ConclusionThis is first reported risk stratification for chemotherapy cycles of gastric carcinoma. Our proposed nomograms can effectively evaluate postoperative prognosis of patients with different chemotherapy cycles of gastric carcinoma. Chemotherapy cycles ≥9 is therefore recommended for high-risk patients with chemotherapy cycles <9, but not for low-risk patients. Meanwhile, combination with multiple therapies are essential to high-risk patients with chemotherapy cycles ≥9 and unnecessary for low-risk patients.
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Affiliation(s)
- Yifan Li
- Second Department of General Surgery, Chinese Academy of Medical Sciences, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
| | - Xiaojuan Zhang
- Radiology Department, Chinese Academy of Medical Sciences, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
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Li Y, Xie F, Xiong Q, Lei H, Feng P. Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:946038. [PMID: 36059703 PMCID: PMC9433672 DOI: 10.3389/fonc.2022.946038] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/01/2022] [Indexed: 01/19/2023] Open
Abstract
Objective To evaluate the diagnostic performance of machine learning (ML) in predicting lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors applicable to the models. Methods PubMed, EMBASE, Web of Science, and Cochrane Library were searched from inception to March 16, 2022. The pooled c-index and accuracy were used to assess the diagnostic accuracy. Subgroup analysis was performed based on ML types. Meta-analyses were performed using random-effect models. Risk of bias assessment was conducted using PROBAST tool. Results A total of 41 studies (56182 patients) were included, and 33 of the studies divided the participants into a training set and a test set, while the rest of the studies only had a training set. The c-index of ML for LNM prediction in training set and test set was 0.837 [95%CI (0.814, 0.859)] and 0.811 [95%CI (0.785-0.838)], respectively. The pooled accuracy was 0.781 [(95%CI (0.756-0.805)] in training set and 0.753 [95%CI (0.721-0.783)] in test set. Subgroup analysis for different ML algorithms and staging of GC showed no significant difference. In contrast, in the subgroup analysis for predictors, in the training set, the model that included radiomics had better accuracy than the model with only clinical predictors (F = 3.546, p = 0.037). Additionally, cancer size, depth of cancer invasion and histological differentiation were the three most commonly used features in models built for prediction. Conclusion ML has shown to be of excellent diagnostic performance in predicting the LNM of GC. One of the models covering radiomics and its ML algorithms showed good accuracy for the risk of LNM in GC. However, the results revealed some methodological limitations in the development process. Future studies should focus on refining and improving existing models to improve the accuracy of LNM prediction. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752
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