| Unique ID issued by UMIN | UMIN000061288 |
|---|---|
| Receipt number | R000070124 |
| Scientific Title | Development and External Validation of Machine Learning Models for Predicting Postoperative Recurrence in Gastric Cancer: A TRIPOD+AI-Compliant Single-Center Cohort Study |
| Date of disclosure of the study information | 2026/05/01 |
| Last modified on | 2026/04/17 12:59:01 |
Development and External Validation of Machine Learning Models for Predicting Postoperative Recurrence in Gastric Cancer: A TRIPOD+AI-Compliant Single-Center Cohort Study
ML Models for Gastric Cancer Recurrence
Development and External Validation of Machine Learning Models for Predicting Postoperative Recurrence in Gastric Cancer: A TRIPOD+AI-Compliant Single-Center Cohort Study
ML Models for Gastric Cancer Recurrence
| Japan |
Gastric cancer
| Gastrointestinal surgery |
Malignancy
NO
The aims of this study were to: (1) develop ML models for postoperative gastric cancer recurrence in a Japanese single-center curative resection cohort using multivariate iterative imputation; (2) evaluate external generalizability in an independent holdout cohort; (3) improve probability calibration via post-hoc sigmoid calibration; (4) quantify clinical utility using bootstrap-corrected DCA; and (5) assess model interpretability using SHAP values.
Efficacy
Discrimination is assessed by ROC-AUC and precision-recall AUC. Clinical utility is evaluated by DCA.
Observational
| 20 | years-old | <= |
| 100 | years-old | >= |
Male and Female
Age is greater than or equal to 20 years
Histologically confirmed gastric adenocarcinoma
Underwent curative (R0) gastrectomy (total, distal, or proximal gastrectomy with lymph node dissection)
Surgery performed between April 2008 and March 2020 at the study institution
Follow-up duration of at least 3 months
Presence of concurrent non-gastric malignancies
Receipt of neoadjuvant chemotherapy or chemoradiotherapy
Emergency surgery or reduced-function procedures
Follow-up duration of less than 3 months
1162
| 1st name | Goshi |
| Middle name | |
| Last name | Fujimoto |
Kameda Medical Center
Gastroenterological Surgery
296-0041
929 Higashi-cho, Kamogawa City, Chiba Prefecture 296-8602, Japan
0470922211
g_chimera_7@yahoo.co.jp
| 1st name | Goshi |
| Middle name | |
| Last name | Fujimoto |
Kameda Medical Center
Gastroenterological Surgery
296-0041
929 Higashi-cho, Kamogawa City, Chiba Prefecture 296-8602, Japan
0470922211
g_chimera_7@yahoo.co.jp
Kameda Medical Center
Goshi Fujimoto
None
Other
Japan
Kameda Medical Center
929 Higashi-cho, Kamogawa City, Chiba Prefecture 296-8602, Japan
0470922211
g_chimera_7@yahoo.co.jp
NO
静岡県
| 2026 | Year | 05 | Month | 01 | Day |
Unpublished
Preinitiation
| 2025 | Year | 04 | Month | 24 | Day |
| 2025 | Year | 04 | Month | 25 | Day |
| 2026 | Year | 03 | Month | 31 | Day |
This study is a single-center retrospective observational study. Using routinely collected clinical data from medical records, we develop machine learning based prediction models to estimate postoperative recurrence risk after curative resection for gastric cancer and to evaluate their external validity.
This study involves no intervention. All analyses are conducted using existing data obtained as part of standard clinical care, and no changes are made to treatment strategies, follow-up schedules, examinations, or outcome assessments for research purposes.
The study period is temporally divided into a development cohort and an independent external validation cohort (temporal validation design). The primary outcome is postoperative recurrence, defined as recurrence confirmed by imaging studies and/or histopathological diagnosis.
| 2026 | Year | 04 | Month | 17 | Day |
| 2026 | Year | 04 | Month | 17 | Day |
Value
https://center6.umin.ac.jp/cgi-open-bin/icdr_e/ctr_view.cgi?recptno=R000070124