The deep learning radiomics nomogram helps to evaluate the lymph node status in cervical adenocarcinoma/adenosquamous carcinoma
Mei Ling Xiao, Le Fu, Ting Qian, Yan Wei, Feng Hua Ma, Yong Ai Li, Jie Jun Cheng, Zhao Xia Qian, Guo Fu Zhang, Jin Wei Qiang · 2024
Read The deep learning radiomics nomogram helps to evaluate the lymph node status in cervical adenocarcinoma/adenosquamous carcinoma by Mei Ling Xiao, Le Fu, Ting Qian, Yan Wei, Feng Hua Ma, Yong Ai Li, Jie Jun Cheng, Zhao Xia Qian, Guo Fu Zhang, Jin Wei Qiang (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-11671353.txt.
Description
Mei Ling Xiao and co-authors build, in 2024, a scoring chart to predict lymph node spread in two cervical cancer types before surgery. Knowing whether nodes are involved shapes the choice between radiation and radical surgery. Their chart blends image texture features, deep learning features from three scan types, and clinical factors.
Data came from six hundred fifty-two patients across centers, split into training and two validation groups. The combined chart scored well in all three groups and beat charts using only one feature family. The authors present it as a preoperative aid for planning treatment in cervical adenocarcinoma and adenosquamous carcinoma.
Catalog record
| CREATOR | Mei Ling Xiao, Le Fu, Ting Qian, Yan Wei, Feng Hua Ma, Yong Ai Li, Jie Jun Cheng, Zhao Xia Qian, Guo Fu Zhang, Jin Wei Qiang |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 29 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |