Beyond VI-RADS Uncertainty: Leveraging Spatiotemporal DCE-MRI to Predict Bladder Cancer Muscle Invasion
Minghui Song, Haonan Ren, Lijuan Wang, Yihang Zhou, Xing Tang, Huanjun Wang, Yan Guo, Yang Liu, Hongbing Lu, Xiaopan Xu · 2025
Read Beyond VI-RADS Uncertainty: Leveraging Spatiotemporal DCE-MRI to Predict Bladder Cancer Muscle Invasion by Minghui Song, Haonan Ren, Lijuan Wang, Yihang Zhou, Xing Tang, Huanjun Wang, Yan Guo, Yang Liu, Hongbing Lu, Xiaopan Xu (2025) 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-12729362.txt.
Description
Minghui Song and nine colleagues build a deep learning model to judge whether bladder cancer has entered the muscle wall, in a twenty twenty-five paper. A standard scan scoring system struggles most in its middle grades. Their model reads contrast MRI films across time, using how the tumor takes up dye. It trained on 184 patients whose scores sat in those tricky grades.
The model beat classic networks and the standard cutoff. It caught ninety percent of non-invasive cases and scored well overall. Heat maps showed it watched the right time-space patterns. The authors offer it as an aid where the standard score is unsure.
Catalog record
| CREATOR | Minghui Song, Haonan Ren, Lijuan Wang, Yihang Zhou, Xing Tang, Huanjun Wang, Yan Guo, Yang Liu, Hongbing Lu, Xiaopan Xu |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 36 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |