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Clinical information can significantly enhance survival prediction accuracy when effectively integrated with multimodal data, as shown by CIGTSurv's state-of-the-art results.
Mamba, typically used for sequence modeling, now powers state-of-the-art cancer survival risk prediction by fusing histological data with AI-generated protein features, outperforming existing methods.
