• Katsuro Ichimasa, Caterina Foppa, Shin-Ei Kudo, Masashi Misawa, Yuki Takashina, Hideyuki Miyachi, Fumio Ishida, Tetsuo Nemoto, Jonathan Wei Jie Lee, Khay Guan Yeoh, Elisa Paoluzzi Tomada, Roberta Maselli, Alessandro Repici, Luigi Maria Terracciano, Paola Spaggiari, Yuichi Mori, Cesare Hassan, Antonino Spinelli, and early CRC group.
• Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan.
• Ann. Surg. 2024 Jul 30.

ObjectiveTo develop and externally validate an updated artificial intelligence (AI) prediction system for stratifying the risk of lymph node metastasis (LNM) in T2 colorectal cancer (CRC).Summary Background DataRecent technical advances allow complete local excision of T2 CRC, traditionally treated with surgical resection. Yet, the widespread adoption of this approach is hampered by the inability to stratify the risk of LNM.MethodsData from pT2 CRC patients undergoing surgical resection between April 2000 and May 2022 at one Japanese and one Italian center were analyzed. Primary goal was AI system development for accurate LNM prediction. Predictors encompassed seven variables: age, sex, tumor size and location, lympho-vascular invasion, histological differentiation, and carcinoembryonic antigen level. The tool's discriminating power was assessed via Area Under the Curve (AUC), sensitivity, and specificity.ResultsOut of 735 initial patients, 692 were eligible. Training and validation cohorts comprised of 492 and 200 patients, respectively. The AI model displayed an AUC of 0.75 in the combined validation dataset. Sensitivity for LNM prediction was 97.8% and specificity was 15.6%. The Positive and the Negative Predictive Value were 25.7% and 96% respectively. The False Negative (FN) rate was 2.2%, the False Positive was 84.4%.ConclusionsOur AI model, based on easily accessible clinical and pathological variables, moderately predicts LNM in T2 CRC. However, the risk of FN needs to be considered. The training of the model including more patients across Western and Eastern centers -differentiating between colon and rectal cancers- may improve its performance and accuracy.Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.

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