Artificial Intelligence-Based Prediction of Invasion Depth in Colorectal Cancer via Endoscopic Imaging (With Video):...

Endoscopic prediction of colorectal cancer (CRC) invasion depth is essential for determining optimal treatment strategy. Artificial intelligence (AI) may assist in distinguishing between superficial (Tis/T1a) and deeply invasive (T1b) lesions to avoid unnecessary surgery. In this narrative review, we summarize recent advances in this field.

A database search of PubMed, Scopus, Cochrane Library, and ClinicalTrials.gov was conducted in September 2025 to identify original peer-reviewed studies that developed or validated AI-based models using endoscopic imaging for invasion-depth prediction. Studies reporting diagnostic metrics such as sensitivity, specificity, accuracy, and AUC were included.

Ten studies met the inclusion criteria, categorized into three groups: (1) AI prediction using image-enhanced endoscopy, (2) AI prediction using white-light imaging, and (3) AI prediction using multi-modal data (imaging with clinical information). The latest models achieved high performance for T1b CRC diagnosis (e.g., AUC 0.851), with some demonstrating performance comparable to expert endoscopists. However, a pooled analysis was not performed due to dataset heterogeneity and limited sample sizes.

AI-assisted systems show promise for improving the prediction of invasion depth in CRC and supporting real-time decision-making. However, limited sample sizes for training and test datasets and an imbalance in the training dataset remain key challenges. Large-scale, multicenter validation studies and the development of open-access databases are essential for clinical implementation.