Machine Learning-Based Radiopatho-Clinical Model Integrating Ultrasound Radiomics and Kleiner Score for Prognosis Prediction in NAFLD-Related Hepatocellular Carcinoma
Chang-Lei Li, Zhen Jia, Zhi-Yuan Yao, Xiao-Tong Cui, Ao Sun, Ao-Yun Hao, Zhong-Yi Chen, Fang Chen, Jing-Yu Cao, Zu-Sen Wang · 2026
Read Machine Learning-Based Radiopatho-Clinical Model Integrating Ultrasound Radiomics and Kleiner Score for Prognosis Prediction in NAFLD-Related Hepatocellular Carcinoma by Chang-Lei Li, Zhen Jia, Zhi-Yuan Yao, Xiao-Tong Cui, Ao Sun, Ao-Yun Hao, Zhong-Yi Chen, Fang Chen, Jing-Yu Cao, Zu-Sen 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/PMC13289783.txt.
Description
Chang-Lei Li and a large team built a 2026 machine learning model to predict outlook after surgery for liver cancer tied to fatty liver disease. Fatty liver now drives more liver cancers, yet how background fat grading affects outlook was unclear. The model blends ultrasound image features, a pathology fat score called Kleiner, and clinical traits for risk sorting after curative surgery.
Records held 639 patients operated between 2010 and 2023 at the study center. Image features were shrunk to a signature, fat was graded on tissue, and clinical factors were screened by an algorithm. A total of 101 models were compared, and a random survival forest won overall. Time-based accuracy stayed strong through five years for survival and return.
Catalog record
| CREATOR | Chang-Lei Li, Zhen Jia, Zhi-Yuan Yao, Xiao-Tong Cui, Ao Sun, Ao-Yun Hao, Zhong-Yi Chen, Fang Chen, Jing-Yu Cao, Zu-Sen Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49163 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |