Interpretable machine learning prognostication of gastroenteropancreatic neuroendocrine tumors across Chinese and United States cohorts
Yangwei Fan, Yuqian Yang, Ke Wang, Tian Zhang, Xuyuan Dong, Yu Shi, Meichen Wang, Enxiao Li, Yinying Wu · 2026
Read Interpretable machine learning prognostication of gastroenteropancreatic neuroendocrine tumors across Chinese and United States cohorts by Yangwei Fan, Yuqian Yang, Ke Wang, Tian Zhang, Xuyuan Dong, Yu Shi, Meichen Wang, Enxiao Li, Yinying Wu (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-13518172.txt.
Description
A peer-reviewed research article from Europe PMC (2026). BackgroundGastroenteropancreatic neuroendocrine tumors (GEP-NETs) display marked clinical heterogeneity, and conventional prognostic indicators such as TNM stage and Ki-67 index provide limited individualized risk discrimination. We aimed to develop and externally validate interpretable machine learning models for survival prediction.MethodsThis retrospective study enrolled 337 patients with histologically confirmed GEP-NETs, randomly partitioned into a training set (n = 236) and an independent test set (n = 101) with stratification by mortality.
Catalog record
| CREATOR | Yangwei Fan, Yuqian Yang, Ke Wang, Tian Zhang, Xuyuan Dong, Yu Shi, Meichen Wang, Enxiao Li, Yinying Wu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 43 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |