Application of machine learning algorithms and establishment of a web calculator in predicting distant metastasis of T2-T4 gastric cancer.
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| Title: | Application of machine learning algorithms and establishment of a web calculator in predicting distant metastasis of T2-T4 gastric cancer. |
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| Authors: | Wang H; The First Clinical Medical College, Lanzhou University, Lanzhou, China; Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Digestive Diseases, The First Hospital of Lanzhou University, Lanzhou, China., Zhang H; The First Clinical Medical College, Lanzhou University, Lanzhou, China; Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Digestive Diseases, The First Hospital of Lanzhou University, Lanzhou, China., Ma X; The First Clinical Medical College, Lanzhou University, Lanzhou, China; Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Digestive Diseases, The First Hospital of Lanzhou University, Lanzhou, China., Bai Y; The First Clinical Medical College, Lanzhou University, Lanzhou, China; Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Digestive Diseases, The First Hospital of Lanzhou University, Lanzhou, China., Wang Y; The First Clinical Medical College, Lanzhou University, Lanzhou, China; Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Digestive Diseases, The First Hospital of Lanzhou University, Lanzhou, China., Zheng Y; Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Digestive Diseases, The First Hospital of Lanzhou University, Lanzhou, China., Yuan H; Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Digestive Diseases, The First Hospital of Lanzhou University, Lanzhou, China., Chen Z; Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China; Gansu Province Clinical Research Center for Digestive Diseases, The First Hospital of Lanzhou University, Lanzhou, China. Electronic address: zhfchen@lzu.edu.cn. |
| Source: | European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology [Eur J Surg Oncol] 2026 Jan; Vol. 52 (1), pp. 111170. Date of Electronic Publication: 2025 Nov 05. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Elsevier Country of Publication: England NLM ID: 8504356 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-2157 (Electronic) Linking ISSN: 07487983 NLM ISO Abbreviation: Eur J Surg Oncol Subsets: MEDLINE |
| Imprint Name(s): | Publication: Original Publication: London ; New York : Academic Press, [1985- |
| MeSH Terms: | Stomach Neoplasms*/pathology , Machine Learning* , Algorithms*, Humans ; Male ; Female ; Middle Aged ; Aged ; Neoplasm Staging ; SEER Program ; ROC Curve ; Internet ; China ; Risk Factors ; Risk Assessment ; Logistic Models ; Adult ; Neoplasm Metastasis |
| Abstract: | Background: T2-T4 gastric cancer often has distant metastasis.The aim of this study is to establish and validate a prediction model for distant metastasis of T2-T4 gastric cancer using machine learning algorithms. Methods: We developed nine machine learning models using 17030 patients with T2-T4 gastric cancer in the Surveillance, Epidemiology, and End Results (SEER) database. 100 patients from a Chinese hospital were selected for external verification of the performance of the model. We evaluated the model using the area under the receiver operating characteristic curve (AUC), the area under the exact recall curve (AUPRC), decision curve analysis, calibration curve, accuracy, F1-score, precision, and specificity for the internal test set and the external validation cohort. We used Shapley's Additive explanation (SHAP) to explain the machine-learning models. Finally, the best model was applied to develop a network calculator for predicting the risk of distant metastasis of gastric cancer. Results: Multivariate logistic regression analysis showed that age, AJCC N, tumor size, tumor number, primary site, differentiation, and histology are independent risk factors for distant metastasis of gastric cancer. The GBDT model was the best model compared with the other 8 machine learning models in three sets. Finally, we constructed a network calculator using the GBDT model. Conclusion: The GBDT model has a good predictive efficiency for predicting the risk of distant metastasis in patients with T2-T4 gastric cancer, and the construction of a network calculator can help clinicians make clinical decisions. (Copyright © 2025 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Zhaofeng Chen reports financial support was provided by the Foundation of The First Hospital of Lanzhou University, study on the mechanism of exosome LINC00957 encoded micropeptide promoting the occurrence and development of gastric cancer (ldyyyn2021-6). Hao Yuan reports financial support was provided by the Natural Science Foundation of China,encoded micropeptide protein in promoting the occurrence and development of gastric cancer (82160498). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Distant metastasis; External validation; Gastric cancer; Machine learning algorithm; Network calculator |
| Entry Date(s): | Date Created: 20251116 Date Completed: 20251213 Latest Revision: 20251213 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.ejso.2025.111170 |
| PMID: | 41242086 |
| Database: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstract: | Background: T2-T4 gastric cancer often has distant metastasis.The aim of this study is to establish and validate a prediction model for distant metastasis of T2-T4 gastric cancer using machine learning algorithms.<br />Methods: We developed nine machine learning models using 17030 patients with T2-T4 gastric cancer in the Surveillance, Epidemiology, and End Results (SEER) database. 100 patients from a Chinese hospital were selected for external verification of the performance of the model. We evaluated the model using the area under the receiver operating characteristic curve (AUC), the area under the exact recall curve (AUPRC), decision curve analysis, calibration curve, accuracy, F1-score, precision, and specificity for the internal test set and the external validation cohort. We used Shapley's Additive explanation (SHAP) to explain the machine-learning models. Finally, the best model was applied to develop a network calculator for predicting the risk of distant metastasis of gastric cancer.<br />Results: Multivariate logistic regression analysis showed that age, AJCC N, tumor size, tumor number, primary site, differentiation, and histology are independent risk factors for distant metastasis of gastric cancer. The GBDT model was the best model compared with the other 8 machine learning models in three sets. Finally, we constructed a network calculator using the GBDT model.<br />Conclusion: The GBDT model has a good predictive efficiency for predicting the risk of distant metastasis in patients with T2-T4 gastric cancer, and the construction of a network calculator can help clinicians make clinical decisions.<br /> (Copyright © 2025 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.) |
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| ISSN: | 1532-2157 |
| DOI: | 10.1016/j.ejso.2025.111170 |