arXiv:2610.22422v1 Announce Type: new Abstract: Accurate prognosis prediction for head and neck cancer typically necessitates integrating highly heterogeneous clinical and pathological evidence by sophisticated multimodal fusion. However, existing fusion techniques are usually grounded in case-generic clinical rationales about complementary synergies across modalities, underestimating case-specific variance. In this study, we propose a prognosis prediction framework that explicitly incorporates Case-specific Priors to guide Multimodal fusion (named CPM). It leverages broad medical knowledge of LLMs to characterize each case's (case-specific) intra-modal contexts and inter-modal relations, as well as case-generic multimodal rationale, as priors, enabling three-stage priors-guided multimodal fusion: (i) Intra-modal Context-guided Calibration (IntraCC) that leverages case-specific intra-modal priors to guide feature calibration within each modality; (ii) Inter-modal Relation-guided Interaction (InterRI) that leverages case-specific inter-modal priors to guide pair-wise feature interaction in modality pairs; and (iii) Multimodal Rationale-guided Aggregation (MultiRA) that leverages case-generic multimodal priors to guide final feature aggregation over all modalities. On the well-established HANCOCK challenge dataset involving four distinct medical modalities, our CPM achieved a mean AUC of 83.98% for predicting 5-year overall survival and 2-year recurrence-free survival, improving upon the baseline by 7.58%, pioneeringly demonstrating the effectiveness of LLM-derived medical priors in guiding multimodal fusion for prognosis prediction. The code is available at https://github.com/yuelin421/CPM.