Non‐Invasive Assessment of Perineural Invasion Density: A Novel Marker for Prognosis and Treatment Stratification in Prostate Cancer
Bohao Liu, Long Zhang, Weian Zhu, Meng Yang, Xiang Liu, Xiaoyang Li, Peng Qin, Zhengxu Lin, Qian Cai, Jiayu Zheng, Xiao Zhao, Chen Zou, Xuan Wen, Longhao Xu, Daqin Wu, Lin Zhang, Cuilian Zhang, Kaiwen Li, Chunhui Wang, Wenlong Zhong, Yun LuoABSTRACT
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.