MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer
Cici Zhang, Minzhi Zhong, Zhiping Liang, Jing Zhou, Kejian Wang, Jun Bu · 2024
Read MRI-based radiomic and machine learning for prediction of lymphovascular invasion status in breast cancer by Cici Zhang, Minzhi Zhong, Zhiping Liang, Jing Zhou, Kejian Wang, Jun Bu (2024) 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-11603622.txt.
Description
Cici Zhang and co-authors test MRI radiomics with machine learning to predict vessel invasion in breast cancer, in a research paper from 2024. Invasion of lymph and blood vessels shapes treatment and prognosis, yet it is normally confirmed only after surgery. A pre-surgery image-based forecast could change planning for the better.
More than four hundred patients with known invasion status had breast MRI scans. Features were extracted from two scan types, then filtered to a compact signature. Eight learning algorithms were trained and validated on split data. One neighbor-based model led the field on accuracy measures. The study weighs which algorithm best turns image texture into a useful risk call.
Catalog record
| CREATOR | Cici Zhang, Minzhi Zhong, Zhiping Liang, Jing Zhou, Kejian Wang, Jun Bu |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 25 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |