Combining pathology artificial intelligence and genomic biomarkers to refine long-term postprostatectomy outcome prediction

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ID: 320421
2026
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Abstract
Abstract Background A multimodal AI (MMAI) model has been validated in prostate biopsy specimens to guide treatment intensification in men receiving radiation. The MMAI has been explored to an extent for prostatectomy patients and has not yet been examined in relation to established genomic scores. Methods We applied the MMAI biopsy model to a tissue microarray (TMA) of 424 prostatectomy cases with long-term follow-up. MMAI scores were derived from digitized pathology images and clinical variables. Associations with biochemical recurrence and metastasis were tested using logistic and Cox regression, adjusting for the Cancer of the Prostate Risk Assessment (CAPRA) and genomic cell cycle progression (CCP) scores. Results MMAI scores were generated from one TMA spot for each of 414 patients (98%). At 10 years, recurrence-free and metastasis-free survival were 74% and 96%, respectively. In univariable models, MMAI was significantly associated with BCR (HR 1.04, 95% CI 1.02 to 1.06) and metastasis (HR 1.05, 95% CI 1.02 to 1.07). MMAI was not independently prognostic after adjustment for CAPRA, but remained significant when adjusted for CCP. Correlation between MMAI and either CAPRA or CCP was modest (r < 0.35). The model combining MMAI and CCP achieved the highest discrimination for metastasis (c-index 0.76), comparable to CAPRA (c-index 0.75). Conclusions The MMAI score, originally developed for whole slide biopsy specimens, was prognostic when applied to a TMA of prostatectomy specimens despite not being designed for TMAs. Although not outperforming established clinical tools, it provided complementary information when combined with genomic data. The MMAI platform merits further refinement and validation in diverse clinical settings.
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Authors Matthew R. Cooperberg, Kevin Shee, Janet E. Cowan, Chien‐Kuang Cornelia Ding, Tamara Todorovic, Imelda Tenggara, Siyi Tang, Rikiya Yamashita, Trevor Royce, Emmalyn Chen, Meghan Tierney, Xiao Ma, Yi Ren, Huei-Chung Huang, Danielle Croucher, Jeffry Simko, Felix Y Feng, Timothy Showalter, Peter R Carroll
Journal JNCI Journal of the National Cancer Institute
Year 2026
DOI
10.1093/jnci/djag210
URL
https://doi.org/10.1093/jnci/djag210
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