An Artificial Intelligence (AI) model integrating multiscale foundation model histopathology representations with molecular and clinical features predicts early and late distant recurrence in TAILORx

Résumé fourni par la source

We developed and validated IICM+, a multimodal model integrating clinicopathologic variables, transcriptomic features, and multiscale histopathology-derived image representations to predict distant recurrence in hormone receptor-positive, HER2-negative, node-negative early breast cancer. Image features included tile-level embeddings and slide-level representations generated by a custom multimodal foundation model pretrained on paired histopathology images, RNA sequencing, and pathology reports. Using TAILORx specimens with long-term follow-up, models were trained in a development cohort with five-fold cross-validation ( n = 2808) and evaluated in an independent institutional holdout validation set ( n = 1621). In holdout validation, IICM+ showed strong prognostic discrimination for overall distant recurrence (C-index 0.735, 95% CI 0.681–0.782), early distant recurrence (0.791, 95% CI 0.715–0.858), and late distant recurrence (0.710, 95% CI 0.645–0.773). IICM+ separated high- versus low-risk groups for overall distant recurrence (HR 5.25, 95% CI 3.50–7.86; P < 0.001) and remained prognostic (HR 3.56, 95% CI 2.22–5.70, P < 0.001) after adjustment for clinicopathologic covariates and RS category. IICM+ also identified RS/IICM+ discordant groups with different observed recurrence risks, supporting additional prognostic stratification beyond RS.

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