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Colorectal liver metastases (CLM) significantly impact colon cancer patients,influencing survival based on systemic chemotherapy response. Traditionalmethods like tumor grading scores (e.g., tumor regression grade - TRG) forprognosis suffer from subjectivity, time constraints, and expertise demands.Current machine learning approaches often focus on radiological data, yet therelevance of histological images for survival predictions, capturing intricatetumor microenvironment characteristics, is gaining recognition. To addressthese limitations, we propose an end-to-end approach for automated prognosisprediction using histology slides stained with H&E and HPS. We first employ aGenerative Adversarial Network (GAN) for slide normalization to reduce stainingvariations and improve the overall quality of the images that are used as inputto our prediction pipeline. We propose a semi-supervised model to performtissue classification from sparse annotations, producing feature maps. We usean attention-based approach that weighs the importance of different slideregions in producing the final classification results. We exploit the extractedfeatures for the metastatic nodules and surrounding tissue to train a prognosismodel. In parallel, we train a vision Transformer (ViT) in a knowledgedistillation framework to replicate and enhance the performance of theprognosis prediction. In our evaluation on a clinical dataset of 258 patients,our approach demonstrates superior performance with c-indexes of 0.804 (0.014)for OS and 0.733 (0.014) for TTR. Achieving 86.9% to 90.3% accuracy inpredicting TRG dichotomization and 78.5% to 82.1% accuracy for the 3-class TRGclassification task, our approach outperforms comparative methods. Our proposedpipeline can provide automated prognosis for pathologists and oncologists, andcan greatly promote precision medicine progress in managing CLM patients.

Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction