Development and validation of an interpretable medical deep foundation model for predicting occult lymph node metastasis in early-stage small cell lung cancer: a multicenter retrospective diagnostic study
Xu Jiang, Qin Peng, Jing Zhang, Alex Zwanenburg, Xin Peng, Chao Luo, Jialiang Ren, Lin Yang, Yajia Gu, Steffen Löck, Kunfeng Liu, Meng Li, Jianwei Wang · 2026
Read Development and validation of an interpretable medical deep foundation model for predicting occult lymph node metastasis in early-stage small cell lung cancer: a multicenter retrospective diagnostic study by Xu Jiang, Qin Peng, Jing Zhang, Alex Zwanenburg, Xin Peng, Chao Luo, Jialiang Ren, Lin Yang, Yajia Gu, Steffen Löck, Kunfeng Liu, Meng Li, Jianwei Wang (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/PMC13557867.txt.
Description
Xu Jiang and twelve colleagues trained in 2026 a deep foundation model to spot hidden lymph node spread in early small cell lung cancer from ordinary CT scans. About a third of patients who look node free before surgery prove to have occult spread afterwards, and that finding worsens survival and changes treatment. The multicentre study drew 416 surgically treated patients whose scans preceded operations at five hospitals.
Images from the largest centre trained and internally tested the model, while the other four centres formed an outside test set. Deep features pulled from a cancer-pretrained foundation model fed standard classifiers, and performance was compared with handcrafted image features, ordinary neural networks, and a clinical model. The foundation approach was built to be interpretable as well as accurate. The authors present it as decision support for staging and surgical planning.
Catalog record
| CREATOR | Xu Jiang, Qin Peng, Jing Zhang, Alex Zwanenburg, Xin Peng, Chao Luo, Jialiang Ren, Lin Yang, Yajia Gu, Steffen Löck, Kunfeng Liu, Meng Li, Jianwei Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 60740 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |