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Life sciences · Journal article
Advanced Science · October 31, 2026
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We established and validated perineural invasion density (PNID) as a superior prognostic marker and developed an MRI-based AI model for its preoperative prediction. In this multicenter study of 1091 PCa patients undergoing radical prostatectomy, we evaluated PNID's association with biochemical recurrence (BCR) and radiographic progression-free survival (rPFS). We also analyzed concurrent β-blocker or antimuscarinic use with adjuvant ADT. A deep learning model was developed in a primary cohort (n = 523) and validated in two external cohorts (n = 317, 251). High-PNID was an independent prognostic factor for BCR (HR 4.06, 95% CI: 2.75-5.97, P < 0.001) and rPFS (HR 4.61, 95% CI: 2.97-7.16, P < 0.001). Notably, concurrent β-blocker use was significantly associated with favorable BCR outcomes exclusively in the High-PNID subgroup (HR 0.32, 95% CI: 0.12-0.84, P = 0.021), with no benefit in the Non-High PNID population or with antimuscarinics. The AI model showed excellent performance predicting High-PNID, with AUCs of 0.917 (training), 0.842 (internal validation), 0.889 and 0.915 (external validations). Model-identified High-PNID patients had significantly worse outcomes. PNID surpasses traditional PNI in prognostic accuracy and identifies a high-risk phenotype. High-PNID may define a subgroup benefiting from adjuvant β-blocker therapy, providing an exploratory clinical rationale for targeting the nerve-tumor microenvironment in stratified subpopulations. Hence, this model may be a reliable, non-invasive tool for preoperative identification of this high-risk subgroup, aiding clinicians in personalized risk stratification and treatment planning.