Interpretable machine learning integrating intra-/peritumoral CT radiomics and serum indicators for predicting poorly differentiated esophageal squamous cell carcinoma
Jun Chen, Jiqiang He, Xiaojiao Zhang, Xiaolin Tang, Ming Yang, Fei Wang · 2026
Read Interpretable machine learning integrating intra-/peritumoral CT radiomics and serum indicators for predicting poorly differentiated esophageal squamous cell carcinoma by Jun Chen, Jiqiang He, Xiaojiao Zhang, Xiaolin Tang, Ming Yang, Fei 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/pmc-13585512.txt.
Description
Jun Chen, Jiqiang He, Xiaojiao Zhang, Xiaolin Tang, Ming Yang, and Fei Wang build in a 2026 study a model to spot poorly grown throat cancer from scans. The cancer's cell grade shapes care, but biopsy is the usual way to learn it. Scan traits inside and around the tumor may carry that signal instead.
Records for 261 patients split into training and test groups for the build. Traits came from the tumor, a thin rim around it, and blood markers. Seven single models and one blended model were trained on the reduced trait set. The blend won on the test group, with a curve score of 0.799. Explanation tools show which traits drive each call, and benefit checks span a wide risk range.
Catalog record
| CREATOR | Jun Chen, Jiqiang He, Xiaojiao Zhang, Xiaolin Tang, Ming Yang, Fei Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |