Artificial intelligence–assisted transrectal ultrasound for rectal cancer invasion depth classification

Accurate assessment of tumor invasion depth is essential for treatment selection in rectal cancer. Transrectal ultrasound (TRUS) provides high-resolution visualization of rectal wall layers but is highly operator-dependent, limiting reproducibility outside expert centers. Artificial intelligence–based approaches may help standardize TRUS interpretation, particularly for depth-of-invasion assessment. We developed and evaluated a two-stage deep learning model for automated analysis of standard TRUS images. A total of 677 two-dimensional images from 103 patients were included. In Stage 1, images were classified as normal or cancer. Images identified as cancer were then analyzed in Stage 2 to classify invasion depth as superficial (T0–T1) or deep (T2–T4). Models were trained using transfer learning with five-fold cross-validation and evaluated on an independent held-out test set. Performance was assessed using accuracy, area under the receiver operating characteristic curve (AUC), and class-specific recall. On the independent test set, the two-stage pipeline achieved an overall three-class accuracy of 82.4%. Stage 1 discriminated normal from cancer with an accuracy of 89.7% and an AUC of 0.93, ensuring reliable identification of tumor-containing images. Among cancer-positive images, Stage 2 differentiated superficial from deep invasion with an accuracy of 85.6% and an AUC of 0.91. Performance was consistent across the dataset and comparable to reported accuracies of expert-performed TRUS. A two-stage deep learning approach applied to standard TRUS images enables automated, practice-oriented classification of rectal tumor invasion depth. By reflecting real-world diagnostic workflows and reducing operator dependence, this method may support more consistent TRUS-based staging and assist treatment decision-making, particularly in nonspecialized centers.