A transformer-based pathomics model using endoscopic biopsy WSIs for predicting pathological complete response to...

Immune checkpoint inhibitors have improved the survival of colorectal cancer (CRC) patients. Patients who respond well to preoperative immunotherapy and achieve a complete response may potentially avoid surgery. Leveraging whole slide images (WSIs) from endoscopic biopsies, this study aims to construct a predictive model using a deep learning approach to identify potential pathological complete response (pCR) in CRC patients undergoing preoperative immunotherapy.

CRC patients who received preoperative immunotherapy from West China Hospital, Sichuan University were retrospectively included and randomized into the training and the validation sets at a 3:1 ratio. Using pathological outcomes as the reference standard, a predictive model was developed based on the H&E-stained WSIs of endoscopic biopsy, incorporating the Swin Transformer architecture and a self-attention mechanism augmented by convolutional neural networks (CNNs). And the Clustering-constrained Attention Multiple Instance Learning (CLAM) framework was used to optimize pathological image analysis. Through attention-based visualization, the top 5 % of patches most influential for determining tumor response to preoperative immunotherapy were identified.

The pCR rate was 30.6 % (22/72) in the training cohort and 30.4 % (7/23) in the validation cohort. The predictive model yielded an area under curve (AUC) of 0.830. Attention-based visualization in the validation set revealed that among the top 5 % of patches contributing to the prediction, 62.25 % were tumor patches and 37.75 % were non-tumor patches.

A deep learning-based pathomics model, which distinctly focuses on both tumor and non-tumor regions, has the potential to predict tumor response to preoperative immunotherapy in CRC patients.