ccRCC Metastasis Prediction via Exploring High-Order Correlations on Multiple WSIs

Metastasis prediction based on gigapixel histopathology whole-slide images (WSIs) is crucial for early diagnosis and clinical decision-making of clear cell renal cell carcinoma (ccRCC). However, most existing methods focus on extracting task-related features from a single WSI, while ignoring the correlations among WSIs, which is important for metastasis prediction when a single patient has multiple pathological slides. In this case, we propose a multi-slice-based hypergraph computation (MSHGC) method for metastasis prediction, which considers the intra-correlations within a single WSI and cross-correlations among multiple WSIs of a single patient simultaneously. Specifically, intra-correlations are captured within both topology and semantic feature spaces, while cross-correlations are modeled between the patches from different WSIs. Finally, the attention mechanism is used to suppress the contribution of task-irrelevant patches and enhance the contribution of task-relevant patches. MSHGC achieves the C-index of 0.8441 and 0.8390 on two carcinoma datasets, outperforming state-of-the-art methods, which demonstrates the effectiveness of the proposed MSHGC.