Development and validation of a radiomics-habitat model for preoperatively predicting poorly differentiated stage IA lung adenocarcinoma
Gang Xiang, Zi Yang, Xiang Wang, Jie Gao, Bin Yang, Qin-Ling Jiang, Yu Ding, Wen-Man Li, Guang-Yan Si · 2026
Read Development and validation of a radiomics-habitat model for preoperatively predicting poorly differentiated stage IA lung adenocarcinoma by Gang Xiang, Zi Yang, Xiang Wang, Jie Gao, Bin Yang, Qin-Ling Jiang, Yu Ding, Wen-Man Li, Guang-Yan Si (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-13559462.txt.
Description
Gang Xiang and co-workers build a CT-based model to predict poorly differentiated early lung cancer before surgery, in a 2026 paper. This aggressive form of stage IA lung adenocarcinoma spreads more readily and shortens survival, so knowing its grade in advance helps plan treatment. Their model mixes whole-tumor measures with sub-region patterns.
Scans from more than five hundred patients at two centers were split into training, test, and outside validation groups. Clustering divided each tumor into three habitats. Four learning algorithms were compared across whole-tumor, habitat, and combined models. Explanation analysis showed which features drove the predictions. The authors report a validated fusion model for clinical planning.
Catalog record
| CREATOR | Gang Xiang, Zi Yang, Xiang Wang, Jie Gao, Bin Yang, Qin-Ling Jiang, Yu Ding, Wen-Man Li, Guang-Yan Si |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |