Interpretable prediction of macrotrabecular-massive HCC and recurrence-free survival by integrating MRI LI-RADS features with deep learning habitat radiomics
Mengwei Zhang, Qian Wu, Mo Zhu, Tao Zhang, Chunyan Gu, Wenhao Gu, Mingzhan Du, Hailin Shen, Chunhong Hu, Yan Yang, Ximing Wang, Yixing Yu · 2026
Read Interpretable prediction of macrotrabecular-massive HCC and recurrence-free survival by integrating MRI LI-RADS features with deep learning habitat radiomics by Mengwei Zhang, Qian Wu, Mo Zhu, Tao Zhang, Chunyan Gu, Wenhao Gu, Mingzhan Du, Hailin Shen, Chunhong Hu, Yan Yang, Ximing Wang, Yixing Yu (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-13554023.txt.
Description
Mengwei Zhang and co-authors build a prediction tool for a harsh liver cancer subtype in a 2026 study. The macrotrabecular-massive form of liver cancer tends to return early and end poorly, so spotting it before surgery helps.
The team studied over six hundred patients who had early stage cancer removed at many centers. Scan traits, blood tests, and deep learning scores from tumor zones were joined in one chart tool. The tool sorted patients well in training and test sets. Scores also split patients by how long they stayed free of return. Key drivers were scan habitat scores, deep scores, and two blood marks. Surgeons get a clear aid for risk talks.
Catalog record
| CREATOR | Mengwei Zhang, Qian Wu, Mo Zhu, Tao Zhang, Chunyan Gu, Wenhao Gu, Mingzhan Du, Hailin Shen, Chunhong Hu, Yan Yang, Ximing Wang, Yixing Yu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |