A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria

• DOI: 10.1007/s10120-024-01511-8
• Publication Date: 2024-05-25T12:01:55Z
• Authors: Minoru Kato; Yoshito Hayashi; Ryotaro Uema; Takashi Kanesaka; Shinjiro Yamaguchi; Akira Maekawa; Takuya Yamada; Masashi Yamamoto; Shinji Kitamura; Takuya Inoue; Shunsuke Yamamoto; Takashi Kizu; Risato Takeda; Hideharu Ogiyama; Katsumi Yamamoto; Kenji Aoi; Koji Nagaike; Yasutaka Sasai; Satoshi Egawa; Haruki Akamatsu; Hiroyuki Ogawa; Masato Komori; Nishihara Akihiro; Takeo Yoshihara; Yoshiki Tsujii; Tetsuo Takehara
• Keywords: Male; Endoscopic Mucosal Resection; Middle Aged; Machine Learning; 03 medical and health sciences; 0302 clinical medicine; ROC Curve; Stomach Neoplasms; Lymphatic Metastasis; Humans; Original Article; Female; Lymph Nodes; Neural Networks, Computer; Aged; Retrospective Studies

Abstract Background We developed a machine learning (ML) model to predict the risk of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) who did not meet the existing Japanese endoscopic curability criteria and compared its performance with that of the most common clinical risk scoring system, the eCura system.

Methods We used data from 4,042 consecutive patients with EGC from 21 institutions who underwent endoscopic submucosal dissection (ESD) and/or surgery between 2010 and 2021. All resected EGCs were histologically confirmed not to satisfy the current Japanese endoscopic curability criteria. Of all patients, 3,506 constituted the training cohort to develop the neural network-based ML model, and 536 constituted the validation cohort. The performance of our ML model, as measured by the area under the receiver operating characteristic curve (AUC), was compared with that of the eCura system in the validation cohort.

Results LNM rates were 14% (503/3,506) and 7% (39/536) in the training and validation cohorts, respectively. The ML model identified patients with LNM with an AUC of 0.83 (95% confidence interval, 0.76–0.89) in the validation cohort, while the eCura system identified patients with LNM with an AUC of 0.77 (95% confidence interval, 0.70–0.85) (P = 0.006, DeLong’s test).

Conclusions Our ML model performed better than the eCura system for predicting LNM risk in patients with EGC who did not meet the existing Japanese endoscopic curability criteria.

Mini-abstract We developed a neural network-based machine learning model that predicts the risk of lymph node metastasis in patients with early gastric cancer who did not meet the endoscopic curability criteria.