Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer
Abstract
In the field of oncology research, patient data typically encompasses diverse multimodal characteristics, including age, survival status, radiological imaging features, histopathological features, and genomic information. The integrated analysis of these multidimensional data holds significant scientific value for in-depth understanding of tumor biology, development of precise prognosis models, and identification of potential therapeutic targets. Current research and literature indicate that deep learning-based multimodal data fusion techniques can uncover underlying biological mechanisms behind individual patient differences through comprehensive analysis of integrated multi-dimensional data, thereby substantially improving the accuracy of artificial intelligence (AI) in prognosis prediction. This review examines the research progress of multimodal data integration techniques in gastric cancer prognosis prediction, while conducting an in-depth discussion on innovative applications of interpretable deep learning models along with the challenges and issues they face.
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