Dual-center development and external validation of machine learning models integrating PSA-derived and peripheral inflammatory markers for prostate cancer diagnosis
Jing Yang, Qianshi Jiang, Yang Wang, Jiaquan Zhou, Guoping Li, Zuobing Yang, Xiangcheng Zhou, Bin Pu, Fanchang Zeng · 2026
Read Dual-center development and external validation of machine learning models integrating PSA-derived and peripheral inflammatory markers for prostate cancer diagnosis by Jing Yang, Qianshi Jiang, Yang Wang, Jiaquan Zhou, Guoping Li, Zuobing Yang, Xiangcheng Zhou, Bin Pu, Fanchang Zeng (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/pmc-13561729.txt.
Description
Yang and co-authors blend blood markers with learning models to spot prostate cancer in a 2026 study. The standard PSA test blurs in its gray zone, where benign growth and cancer overlap. Records from over three hundred men at one hospital trained three model types. A second hospital's sixty patients served as an outside check.
Ratios built from PSA forms and blood cell counts fed the models. On home data the tree and boosting models scored high; outside scores fell but stayed useful. On one shared test set the three models sat close, with no firm winner. In the gray zone a PSA ratio alone beat total PSA. The authors call for bigger trials.
Catalog record
| CREATOR | Jing Yang, Qianshi Jiang, Yang Wang, Jiaquan Zhou, Guoping Li, Zuobing Yang, Xiangcheng Zhou, Bin Pu, Fanchang Zeng |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |