PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images
Yang Luo, Shiru Wang, Jun Liu, Jiaxuan Xiao, Rundong Xue, Zeyu Zhang, Hao Zhang, Yu Lu, Yang Zhao, Yutong Xie
2025-03-27
Summary
This paper is about using AI to predict how long a person with breast cancer will live by looking at detailed images of their tumor.
What's the problem?
It's hard to predict breast cancer survival because tumors are complex, and different parts of the same tumor can look and act differently.
What's the solution?
The researchers developed a new AI system called PathoHR that uses high-resolution images of tumors to extract more detailed information and make better predictions.
Why it matters?
This work matters because it can help doctors make more informed decisions about treatment and improve the chances of survival for people with breast cancer.
Abstract
Breast cancer survival prediction in computational pathology presents a remarkable challenge due to tumor heterogeneity. For instance, different regions of the same tumor in the pathology image can show distinct morphological and molecular characteristics. This makes it difficult to extract representative features from whole slide images (WSIs) that truly reflect the tumor's aggressive potential and likely survival outcomes. In this paper, we present PathoHR, a novel pipeline for accurate breast cancer survival prediction that enhances any size of pathological images to enable more effective feature learning. Our approach entails (1) the incorporation of a plug-and-play high-resolution Vision Transformer (ViT) to enhance patch-wise WSI representation, enabling more detailed and comprehensive feature extraction, (2) the systematic evaluation of multiple advanced similarity metrics for comparing WSI-extracted features, optimizing the representation learning process to better capture tumor characteristics, (3) the demonstration that smaller image patches enhanced follow the proposed pipeline can achieve equivalent or superior prediction accuracy compared to raw larger patches, while significantly reducing computational overhead. Experimental findings valid that PathoHR provides the potential way of integrating enhanced image resolution with optimized feature learning to advance computational pathology, offering a promising direction for more accurate and efficient breast cancer survival prediction. Code will be available at https://github.com/AIGeeksGroup/PathoHR.