Artificial Intelligence Models May Aid in Predicting Lymph Node Metastasis in Patients with T1 Colorectal Cancer

Overview

Journal Gut Liver

Date 2025 Jan 8

PMID 39778879

Authors

Ji Eun Baek

Hahn Yi

Seung Wook Hong

Subin Song

Ji Young Lee

Sung Wook Hwang

Sang Hyoung Park

Dong-Hoon Yang

Byong Duk Ye

Seung-Jae Myung

Suk-Kyun Yang

Namkug Kim

Jeong-Sik Byeon

Affiliations

Soon will be listed here.

Abstract

Background/aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC.

Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines.

Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845).

Conclusions: AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Citing Articles

Artificial Intelligence in Lymph Node Metastasis Prediction for T1 Colorectal Cancer: Promise and Challenges.

Bae J Gut Liver. 2025; 19(1):3-5.

PMID: 39809462 PMC: 11736322. DOI: 10.5009/gnl250001.

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