Non‐Invasive Assessment of Perineural Invasion Density: A Novel Marker for Prognosis and Treatment Stratification in Prostate Cancer
6 affiliations
- 1Department of Urology The Third Affiliated Hospital of Sun Yat‐sen University Guangzhou China
- 2Department of Pathology Sun Yat‐sen Memorial Hospital Sun Yat‐sen University Guangzhou China
- 3Department of Urology Yan'an Hospital Affiliated to Kunming Medical University Kunming Medical University Kunming China
- 4Department of Radiology Sun Yat‐sen Memorial Hospital Sun Yat‐sen University Guangzhou China
- 5Department of Urology Peking University People's Hospital Peking University Beijing China
- 6Department of Urology Sun Yat‐sen Memorial Hospital Sun Yat‐sen University Guangzhou China
Abstract
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.
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BibTeX
@article{liu2026noninvasive,
title = {{Non‐Invasive Assessment of Perineural Invasion Density: A Novel Marker for Prognosis and Treatment Stratification in Prostate Cancer}},
author = {Bohao Liu and Long Zhang and Weian Zhu and Meng Yang and Xiang Liu and Xiaoyang Li and Peng Qin and Zhengxu Lin and Qian Cai and Jiayu Zheng and Xiao Zhao and Chen Zou and Xuan Wen and Longhao Xu and Daqin Wu and Lin Zhang and Cuilian Zhang and Kaiwen Li and Chunhui Wang and Wenlong Zhong and Yun Luo},
journal = {Advanced Science},
year = {2026},
month = oct,
doi = {10.1002/advs.78460},
url = {https://doi.org/10.1002/advs.78460},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Non‐Invasive Assessment of Perineural Invasion Density: A Novel Marker for Prognosis and Treatment Stratification in Prostate Cancer
AU - Bohao Liu
AU - Long Zhang
AU - Weian Zhu
AU - Meng Yang
AU - Xiang Liu
AU - Xiaoyang Li
AU - Peng Qin
AU - Zhengxu Lin
AU - Qian Cai
AU - Jiayu Zheng
AU - Xiao Zhao
AU - Chen Zou
AU - Xuan Wen
AU - Longhao Xu
AU - Daqin Wu
AU - Lin Zhang
AU - Cuilian Zhang
AU - Kaiwen Li
AU - Chunhui Wang
AU - Wenlong Zhong
AU - Yun Luo
PY - 2026
DA - 2026/10/31
JO - Advanced Science
DO - 10.1002/advs.78460
UR - https://doi.org/10.1002/advs.78460
AB - 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.
SN - 2198-3844
ER -