The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer
Han Wang, Li Gong · 2025
Read The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer by Han Wang, Li Gong (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-12776218.txt.
Description
Han Wang and Li Gong report a 2025 study on breast cancer scans. Doctors need to know before surgery whether cancer has reached the armpit nodes. Current checks are either invasive or not exact. The team builds learning models from a contrast MRI scan type. Clinic and lab facts about each patient join the scan data. Records cover 605 patients, split into training and test sets.
Image features were cut down and picked by set rules. Several model types were trained and compared. Standard regression also picked which clinic facts belong in the final tool. The models sort patients into node stages set by a national cancer guide. Surgeons could use a good score to plan node surgery with less guesswork. The study is a look back at past records, so live clinic tests come next. Still, it shows scan-based learning can carry real signal about node spread.
Catalog record
| CREATOR | Han Wang, Li Gong |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |