Bio-interpretable ensemble learning model for invasive pulmonary adenocarcinoma grade using CT and histopathology images
Zhihe Yang, Fan Li, Qijia Han, Zhu Ai, Minyi Wu, Qiuxing Chen, Siqi Qu, Lingxiang Liu, Haowen Yan, Guorong Zou, Fang Chen, Hao Wang, Zhiming Xiang · 2025
Read Bio-interpretable ensemble learning model for invasive pulmonary adenocarcinoma grade using CT and histopathology images by Zhihe Yang, Fan Li, Qijia Han, Zhu Ai, Minyi Wu, Qiuxing Chen, Siqi Qu, Lingxiang Liu, Haowen Yan, Guorong Zou, Fang Chen, Hao Wang, Zhiming Xiang (2025) 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-12824164.txt.
Description
Zhihe Yang and eleven colleagues build an AI grading model for a common lung tumor using scans and tissue images together, in a twenty twenty-five study. Grading this tumor by eye is hard because its forms vary so much. The study covered 988 patients from several centers who had surgery with confirmed invasive lung adenocarcinoma. Features from CT scans and slide images were fused into one ensemble model.
The model graded tumors well in both home and outside test sets. Paired gene data tied the image patterns to two gene groups and hundreds of pathways. Paths covered signaling, cell growth, DNA repair, and spread. The authors say joint image reading beats either source alone for grading.
Catalog record
| CREATOR | Zhihe Yang, Fan Li, Qijia Han, Zhu Ai, Minyi Wu, Qiuxing Chen, Siqi Qu, Lingxiang Liu, Haowen Yan, Guorong Zou, Fang Chen, Hao Wang, Zhiming Xiang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 67 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |