Tokyo, May 26 -- UMIN Clinical Trials Registry (UMIN-CTR) received information related to the study (UMIN000061679) titled 'Machine learning-based prediction of lymph node metastases for individualized surgical decision-making in older patients with gastric cancer: A retrospective simulation study compliant with TRIPOD+AI' on May 25.
Study Type:
Observational
Primary Sponsor:
Institute - Kameda Medical Center
Condition:
Condition - Gastric cancer
Classification by malignancy - Malignancy
Genomic information - NO
Objective:
Narrative objectives1 - (1) to develop and temporally validate an LNM prediction model based on preoperative variables in patients aged >=70 years using six machine learning algorithms
(2) to quantify the oncological safety [negative predictive value (NPV), false-negative count] of a threshold-guided reduced-surgery strategy via retrospective simulation
(3) to perform a comprehensive evaluation of calibration, fairness, and uncertainty compliant with the TRIPOD+AI reporting guidelines
Basic objectives2 - Efficacy
Eligibility:
Age-lower limit - 70
years-old
=
Gender - Male and Female
Key inclusion criteria - patients aged 70 years and above who underwent gastrectomies with lymph node dissections for gastric cancer between April 1995 and March 2025.
Key exclusion criteria - (i) neoadjuvant chemotherapy
(ii) distant metastases (M1)
Target Size - 1405
Recruitment Status:
Recruitment status - No longer recruiting
Date of protocol fixation - 2026 Year 05 Month 18 Day
Date of IRB - 2026 Year 05 Month 18 Day
Anticipated trial start date - 2026 Year 05 Month 18 Day
Last follow-up date - 2026 Year 12 Month 31 Day
To know more, visit https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000070576
Disclaimer: Curated by HT Syndication.
