Multicenter Study Suggests Unsupervised Learning Derived From MRI Identifies Prognostic Subgroups in Prostate Cancer Patients After Prostatectomy
Guoqing Hu, Xiaofeng Liu, Zhangzhe Chen, Bingni Zhou, Hualei Gan, Ruchuan Chen, Liangping Zhou, Yajia Gu, Xiaohang Liu · 2026
Read Multicenter Study Suggests Unsupervised Learning Derived From MRI Identifies Prognostic Subgroups in Prostate Cancer Patients After Prostatectomy by Guoqing Hu, Xiaofeng Liu, Zhangzhe Chen, Bingni Zhou, Hualei Gan, Ruchuan Chen, Liangping Zhou, Yajia Gu, Xiaohang Liu (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/PMC13250766.txt.
Description
Guoqing Hu and colleagues use machine learning on scans to sort prostate cancer outlooks in this 2026 multicentre study. After prostate removal, some cancers return within years, and better forecasts could guide extra treatment.
Records and MRI scans from 400 patients at three centres were used, 185 of whom had a recurrence. A feature-picking method kept five clinical and thirteen scan features, and a clustering method split patients into three outlook groups. The combined model forecast freedom from recurrence more sharply than three standard risk tools in both training and check groups. The authors say scan patterns carry forecast value that clinic scores miss.
Catalog record
| CREATOR | Guoqing Hu, Xiaofeng Liu, Zhangzhe Chen, Bingni Zhou, Hualei Gan, Ruchuan Chen, Liangping Zhou, Yajia Gu, Xiaohang Liu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33842 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |