Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American population
Objective To develop and validate machine learning (ML)-based models to predict lymph node metastasis (LNM) in patients with gastric cancer (GC).Design Retrospective cohort study.Setting Second Affiliated Hospital of Soochow University.Participants A total of 500 inpatients from the Second Affiliate...
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BMJ Publishing Group
2025-03-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/3/e098476.full |
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| author | Qian Li Yuan Tian Wei Peng Shangcheng Yan Weiran Yang Zhuan Du Ming Cheng Renwei Chen Qiankun Shao Mengchao Sheng Yongyou Wu |
| author_facet | Qian Li Yuan Tian Wei Peng Shangcheng Yan Weiran Yang Zhuan Du Ming Cheng Renwei Chen Qiankun Shao Mengchao Sheng Yongyou Wu |
| author_sort | Qian Li |
| collection | DOAJ |
| description | Objective To develop and validate machine learning (ML)-based models to predict lymph node metastasis (LNM) in patients with gastric cancer (GC).Design Retrospective cohort study.Setting Second Affiliated Hospital of Soochow University.Participants A total of 500 inpatients from the Second Affiliated Hospital of Soochow University, collected retrospectively between 1 April 2018 and 31 March 2023, were used as the training set, while 824 Asian patients from the Surveillance, Epidemiology and End Results database comprised the external validation set.Main outcome measures Prediction models were developed using multiple ML algorithms, including logistic regression, support vector machine, k-nearest neighbours, naive Bayes, decision tree (DT), gradient boosting DT, random forest and artificial neural network (ANN). The predictive value of these models was validated and evaluated through receiver operating characteristic curves, precision-recall (PR) curves, calibration curves, decision curve analysis and accuracy metrics.Results Among the ML algorithms, the ANN outperformed others, achieving the highest accuracy (0.722; 95% CI: 0.692 to 0.751), precision (0.732; 95% CI: 0.694 to 0.776), F1 score (0.733; 95% CI: 0.695 to 0.773), specificity (0.728; 95% CI: 0.684 to 0.770) and area under the PR curve (0.781; 95% CI: 0.740 to 0.821) in the external validation results. Moreover, it demonstrated superior calibration and clinical utility. Shapley Additive Explanations analysis identified the depth of invasion, tumour size and Lauren classification as the most influential predictors of LNM in patients with GC. Furthermore, a user-friendly web application was developed to provide individual prediction results.Conclusions This study introduces an accurate, reliable and clinically applicable approach for predicting the risk of LNM in patients with GC. The model demonstrates its potential to enhance the personalised management of GC in diverse populations, supported by external validation and an accessible web application for practical use. |
| format | Article |
| id | doaj-art-c6c28ca9ffa246c8ac75ece0bd970e80 |
| institution | DOAJ |
| issn | 2044-6055 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-c6c28ca9ffa246c8ac75ece0bd970e802025-08-20T02:54:22ZengBMJ Publishing GroupBMJ Open2044-60552025-03-0115310.1136/bmjopen-2024-098476Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American populationQian Li0Yuan Tian1Wei Peng2Shangcheng Yan3Weiran Yang4Zhuan Du5Ming Cheng6Renwei Chen7Qiankun Shao8Mengchao Sheng9Yongyou Wu101 Department of Gastrointestinal Surgery, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China1 Department of Gastrointestinal Surgery, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China1 Department of Gastrointestinal Surgery, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China2 Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China3 Institute of Exercise Training and Sport Informatics, German Sport University, Cologne, Germany1 Department of Gastrointestinal Surgery, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China1 Department of Gastrointestinal Surgery, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China1 Department of Gastrointestinal Surgery, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China1 Department of Gastrointestinal Surgery, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China1 Department of Gastrointestinal Surgery, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China1 Department of Gastrointestinal Surgery, Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, ChinaObjective To develop and validate machine learning (ML)-based models to predict lymph node metastasis (LNM) in patients with gastric cancer (GC).Design Retrospective cohort study.Setting Second Affiliated Hospital of Soochow University.Participants A total of 500 inpatients from the Second Affiliated Hospital of Soochow University, collected retrospectively between 1 April 2018 and 31 March 2023, were used as the training set, while 824 Asian patients from the Surveillance, Epidemiology and End Results database comprised the external validation set.Main outcome measures Prediction models were developed using multiple ML algorithms, including logistic regression, support vector machine, k-nearest neighbours, naive Bayes, decision tree (DT), gradient boosting DT, random forest and artificial neural network (ANN). The predictive value of these models was validated and evaluated through receiver operating characteristic curves, precision-recall (PR) curves, calibration curves, decision curve analysis and accuracy metrics.Results Among the ML algorithms, the ANN outperformed others, achieving the highest accuracy (0.722; 95% CI: 0.692 to 0.751), precision (0.732; 95% CI: 0.694 to 0.776), F1 score (0.733; 95% CI: 0.695 to 0.773), specificity (0.728; 95% CI: 0.684 to 0.770) and area under the PR curve (0.781; 95% CI: 0.740 to 0.821) in the external validation results. Moreover, it demonstrated superior calibration and clinical utility. Shapley Additive Explanations analysis identified the depth of invasion, tumour size and Lauren classification as the most influential predictors of LNM in patients with GC. Furthermore, a user-friendly web application was developed to provide individual prediction results.Conclusions This study introduces an accurate, reliable and clinically applicable approach for predicting the risk of LNM in patients with GC. The model demonstrates its potential to enhance the personalised management of GC in diverse populations, supported by external validation and an accessible web application for practical use.https://bmjopen.bmj.com/content/15/3/e098476.full |
| spellingShingle | Qian Li Yuan Tian Wei Peng Shangcheng Yan Weiran Yang Zhuan Du Ming Cheng Renwei Chen Qiankun Shao Mengchao Sheng Yongyou Wu Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American population BMJ Open |
| title | Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American population |
| title_full | Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American population |
| title_fullStr | Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American population |
| title_full_unstemmed | Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American population |
| title_short | Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American population |
| title_sort | machine learning models for prediction of lymph node metastasis in patients with gastric cancer a chinese single centre study with external validation in an asian american population |
| url | https://bmjopen.bmj.com/content/15/3/e098476.full |
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