AI-based histopathology and radiomics fusion for predicting surgical margins in colorectal cancer: improving oncological outcomes through multimodal AI integration.

Achieving negative surgical margins is fundamental to curative colorectal cancer (CRC) surgery. Despite advancements in imaging, preoperative identification of margin risk remains limited. Recent developments in artificial intelligence (AI) now enable fusion of radiomics, quantitative imaging analysis, and histopathology ("pathomics") to predict microscopic tumor spread more accurately. Radiomics captures sub-visual textural and spatial features from CT and MRI, while AI-driven histopathology interprets digital slides at cellular resolution. Integrating these modalities yields a multi-scale model that reflects both macroscopic tumor architecture and microscopic invasiveness. Multicentric studies in China and the US have demonstrated superior performance of radiopathomic models over single-modality approaches for predicting therapeutic response and margin status. As countries such as the United Kingdom and South Korea implement AI-driven precision oncology frameworks, transparent validation remains essential. By enabling more informed surgical planning and tailored resections, multimodal AI fusion could markedly enhance oncological outcomes in CRC.