Development and validation of a PET/CT radiomics and dual-task learning model for the prediction of pathological subtypes and EGFR mutation in non-small cell lung cancer
Fan Jiang, Nan-Feng Zhang, Yi Gao, Xin Chen, En-Tao Liu, Tian Mou · 2026
Read Development and validation of a PET/CT radiomics and dual-task learning model for the prediction of pathological subtypes and EGFR mutation in non-small cell lung cancer by Fan Jiang, Nan-Feng Zhang, Yi Gao, Xin Chen, En-Tao Liu, Tian Mou (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/PMC13351896.txt.
Description
Fan Jiang and co-authors build a 2026 imaging model that predicts tumour subtype and EGFR mutation status in non-small cell lung cancer from PET and CT scans. Tissue sampling has limits for tracking change and tumour variety. The retrospective study held 384 patients across three separate cohorts.
Features were drawn from CT regions and from PET measures of uneven metabolism. Their dual-modal, dual-task model fuses both scan types with cross-attention and learns the two predictions together. Extra tests checked which parts of the model mattered, and the design aims to keep decisions readable for clinicians. The goal is a non-invasive route to guide personal treatment choices.
Catalog record
| CREATOR | Fan Jiang, Nan-Feng Zhang, Yi Gao, Xin Chen, En-Tao Liu, Tian Mou |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 40060 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |