Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers
Jianqiu Kong, Yi Huang, Yichun Xing, Shuogui Fang, Kaiwen Tan, Juanjuan Yong, Sha Fu, Yaqiang Huang, Chun Jiang, Xinxiang Fan · 2026
Read Unlocking the prognostic power of pathomics in bladder cancer: a machine learning odyssey across multiple centers by Jianqiu Kong, Yi Huang, Yichun Xing, Shuogui Fang, Kaiwen Tan, Juanjuan Yong, Sha Fu, Yaqiang Huang, Chun Jiang, Xinxiang Fan (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-13366761.txt.
Description
Bladder cancer (BCa) prognostication is pivotal for tailored clinical interventions. Using machine learning, this study assesses prognostic capabilities of H&E-stained BCa images. From 569 slides across The Cancer Genome Atlas, Sun Yat-sen Memorial Hospital, and Zhongshan City People’s Hospital, we extracted 150 histopathological markers each. LASSO regression yielded a pathomic fingerprint, which was further validated.
Catalog record
| CREATOR | Jianqiu Kong, Yi Huang, Yichun Xing, Shuogui Fang, Kaiwen Tan, Juanjuan Yong, Sha Fu, Yaqiang Huang, Chun Jiang, Xinxiang Fan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 36 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |