A discrimination model by machine learning to avoid gastrectomy for early gastric cancer

• Published in: Annals of gastroenterological surgery Vol. 7; no. 6; pp. 913 - 921
• Main Authors: Hayashi, Tsutomu, Takasawa, Ken, Yoshikawa, Takaki, Hashimoto, Taiki, Sekine, Shigeki, Wada, Takeyuki, Yamagata, Yukinori, Suzuki, Haruhisa, Abe, Seiichirou, Yoshinaga, Shigetaka, Saito, Yutaka, Kouno, Nobuji, Hamamoto, Ryuji
• Format: Journal Article
• Language: English
• Published: Japan John Wiley & Sons, Inc 01.11.2023; John Wiley and Sons Inc; Wiley
• Subjects: Classification; Datasets; discrimination model; early gastric cancer; Endoscopy; Gastric cancer; Gastrointestinal surgery; lymph node metastasis; Machine learning; Metastasis; Original; Patients; Physicians; surgical indication; Tumors; surgical indication; machine learning; discrimination model; early gastric cancer; lymph node metastasis
• ISSN: 2475-0328; 2475-0328
• DOI: 10.1002/ags3.12714

Aim Gastrectomy is recommended for patients with early gastric cancer (EGC) because the possibility of lymph node metastasis (LNM) cannot be completely denied. The aim of this study was to develop a discrimination model to select patients who do not require surgery using machine learning. Methods Data from 382 patients who received gastrectomy for gastric cancer and who were diagnosed with pT1b were extracted for developing a discrimination model. For the validation of this discrimination model, data from 140 consecutive patients who underwent endoscopic resection followed by gastrectomy, with a diagnosis of pT1b EGC, were extracted. We applied XGBoost to develop a discrimination model for clinical and pathological variables. The performance of the discrimination model was evaluated based on the number of cases classified as true negatives for LNM, with no false negatives for LNM allowed. Results Lymph node metastasis was observed in 95 patients (25%) in the development cohort and 11 patients (8%) in the validation cohort. The discrimination model was developed to identify 27 (7%) patients with no indications for additional surgery due to the prediction of an LNM‐negative status with no false negatives. In the validation cohort, 13 (9%) patients were identified as having no indications for additional surgery and no patients with LNM were classified into this group. Conclusion The discrimination model using XGBoost algorithms could select patients with no risk of LNM from patients with pT1b EGC. This discrimination model was considered promising for clinical decision‐making in relation to patients with EGC. A discrimination model using machine leaning could select some pT1b tumors without LNM using the clinical data and pathological findings of the primary tumor.