Preoperative prediction of pathological pelvic lymph node metastasis in prostate cancer using an elastic net model integrating clinicopathologic and 68Ga-PSMA PET/CT features: development and temporal validation
Yu Chen, Long Gao, Zhongyi Zheng, Pei Feng, Chenqi Zhang, Guofeng Shi, Shaonan Li, Jingxi Hu, Liqiang Wang, Bo Wu, Xiaoming Cao · 2026
Read Preoperative prediction of pathological pelvic lymph node metastasis in prostate cancer using an elastic net model integrating clinicopathologic and 68Ga-PSMA PET/CT features: development and temporal validation by Yu Chen, Long Gao, Zhongyi Zheng, Pei Feng, Chenqi Zhang, Guofeng Shi, Shaonan Li, Jingxi Hu, Liqiang Wang, Bo Wu, Xiaoming Cao (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13500834.txt.
Description
Yu Chen leads an eleven-author team reporting a 2026 prediction model for prostate cancer that has spread to pelvic lymph nodes. Surgeons must decide before operating whether to remove those nodes, a costly step, and older risk tools use only standard clinical variables. The team studied 315 men at one Chinese centre who had robot-assisted prostate removal, node dissection, and a PSMA PET/CT scan beforehand. Cases from 2020 to 2024 formed the development group of 210, and 105 cases from 2025 formed a later validation group.
Two elastic net models were built: one on clinicopathologic variables alone, one adding PSMA scan features. Across repeated splits of the development data the combined model averaged an AUC of 0.962 against 0.910 for the clinical-only model. In the 2025 group the combined model reached an AUC of 0.943, with a negative predictive value of 0.946, and beat a Memorial Sloan Kettering benchmark on decision curve analysis.
Catalog record
| CREATOR | Yu Chen, Long Gao, Zhongyi Zheng, Pei Feng, Chenqi Zhang, Guofeng Shi, Shaonan Li, Jingxi Hu, Liqiang Wang, Bo Wu, Xiaoming Cao |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 53073 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |