Machine learning based on clinico-biological features integrated (18)F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

PURPOSE: To develop and validate a clinico-biological features and (18)F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carc...

Descripción completa

Detalles Bibliográficos
Autores principales: Ren, Caiyue, Zhang, Jianping, Qi, Ming, Zhang, Jiangang, Zhang, Yingjian, Song, Shaoli, Sun, Yun, Cheng, Jingyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113203/
https://www.ncbi.nlm.nih.gov/pubmed/33057772
http://dx.doi.org/10.1007/s00259-020-05065-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113203/
https://www.ncbi.nlm.nih.gov/pubmed/33057772
http://dx.doi.org/10.1007/s00259-020-05065-6

Ejemplares similares