CT-based deep learning for survival stratification in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance: A multicenter study
Shuai Qie, Yasong Shi, Jingyun Li, Sicong Jia, Ming Liu · 2026
Read CT-based deep learning for survival stratification in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance: A multicenter study by Shuai Qie, Yasong Shi, Jingyun Li, Sicong Jia, Ming 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/pmc-13571145.txt.
Description
Shuai Qie and co-authors used deep learning on CT scans to sort lung cancer outlook in a 2026 study. The patients had EGFR-mutant lung adenocarcinoma whose targeted drugs had stopped working. After that point, care choices hinge on who will do well and who will not.
The team trained a deep model on scans taken before care, from 525 patients across centers. Its score split patients into risk groups for both time without growth and total survival. The score held up when clinic facts were added, in home and outside cohorts alike. Such a score could guide the next care step after resistance.
Catalog record
| CREATOR | Shuai Qie, Yasong Shi, Jingyun Li, Sicong Jia, Ming Liu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 53 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |