Development and Temporal Validation of Machine Learning Models for Predicting Postoperative Recurrence in Gastric Cancer: A TRIPOD+AI-Compliant Single-Center Cohort Study

Development and Temporal 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

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000061288

Enrollment

1162

Registered

2026-05-01

Start date

2025-04-25

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Gastric cancer

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: 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

Exclusion criteria

Exclusion criteria: 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

Design outcomes

Primary

Measure	Time frame
Discrimination is assessed by ROC-AUC and precision-recall AUC. Clinical utility is evaluated by DCA.	—

Countries

Japan

Contacts

Public ContactGoshi Fujimoto

Kameda Medical Center Gastroenterological Surgery

g_chimera_7@yahoo.co.jp0470922211

Outcome results