Gut Liver.
2025 Jan;19(1):69-76. 10.5009/gnl240273.
Artificial Intelligence Models May Aid in Predicting Lymph Node Metastasis in Patients with T1 Colorectal Cancer
- Baek JE
1,2 - Yi H3
- Hong SW1
- Song S1
- Lee JY4
- Hwang SW1
- Park SH1
- Yang DH1
- Ye BD1
- Myung SJ1
- Yang SK1
- Kim N3
- Byeon JS1
- Affiliations
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- 1Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- 2Department of Gastroenterology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Korea
- 3Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- 4Health Screening and Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- KMID: 2564735
- DOI: http://doi.org/10.5009/gnl240273
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.
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