结直肠肝转移（CLM）影响了几乎一半的结肠癌患者，对全身化疗的反应对患者的生存起着至关重要的作用。虽然肿瘤学家通常使用肿瘤分级评分（例如肿瘤消退分级 (TRG)）来对患者结果（包括总生存期 (OS) 和复发时间 (TTR)）建立准确的预后，但这些传统方法有一些局限性。它们是主观的、耗时的，并且需要广泛的专业知识，这限制了它们的可扩展性和可靠性。此外，现有的使用机器学习进行预后预测的方法主要依赖于放射成像数据，但最近组织学图像已被证明可以完全捕获肿瘤的复杂微环境和细胞特征，从而与生存预测相关。为了解决这些局限性，我们提出了一种使用苏木精和曙红染色的组织学载玻片进行自动预后预测的端到端方法（H

Colorectal liver metastases (CLM) affect almost half of all colon cancer patients and the response to systemic chemotherapy plays a crucial role in patient survival. While oncologists typically use tumor grading scores, such as tumor regression grade (TRG), to establish an accurate prognosis on patient outcomes, including overall survival (OS) and time-to-recurrence (TTR), these traditional methods have several limitations. They are subjective, time-consuming, and require extensive expertise, which limits their scalability and reliability. Additionally, existing approaches for prognosis prediction using machine learning mostly rely on radiological imaging data, but recently histological images have been shown to be relevant for survival predictions by allowing to fully capture the complex microenvironmental and cellular characteristics of the tumor. To address these limitations, we propose an end-to-end approach for automated prognosis prediction using histology slides stained with Hematoxylin and Eosin (H&E) and Hematoxylin Phloxine Saffron (HPS). We first employ a Generative Adversarial Network (GAN) for slide normalization to reduce staining variations and improve the overall quality of the images that are used as input to our prediction pipeline. We propose a semi-supervised model to perform tissue classification from sparse annotations, producing segmentation and feature maps. Specifically, we use an attention-based approach that weighs the importance of different slide regions in producing the final classification results. Finally, we exploit the extracted features for the metastatic nodules and surrounding tissue to train a prognosis model. In parallel, we train a vision Transformer model in a knowledge distillation framework to replicate and enhance the performance of the prognosis prediction. We evaluate our approach on an in-house clinical dataset of 258 CLM patients, achieving superior performance compared to other comparative models with a c-index of 0.804 (0.014) for OS and 0.735 (0.016) for TTR, as well as on two public datasets. The proposed approach achieves an accuracy of 86.9% to 90.3% in predicting TRG dichotomization. For the 3-class TRG classification task, the proposed approach yields an accuracy of 78.5% to 82.1%, outperforming the comparative methods. Our proposed pipeline can provide automated prognosis for pathologists and oncologists, and can greatly promote precision medicine progress in managing CLM patients.Copyright © 2024 Elsevier B.V. All rights reserved.