Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

SIMPLE SUMMARY: This study developed CT-based radiomics signatures using the least absolute shrinkage and selection operator (LASSO), random forest (RF) and support vector machine (SVM) algorithms to predict the pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients w...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Chao, Chen, Haiyan, Zhang, Bicheng, Fang, Yimin, Sun, Wenzheng, Wu, Dang, Su, Zhuo, Shen, Li, Wei, Qichun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648149/
https://www.ncbi.nlm.nih.gov/pubmed/37958309
http://dx.doi.org/10.3390/cancers15215134

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10648149/
https://www.ncbi.nlm.nih.gov/pubmed/37958309
http://dx.doi.org/10.3390/cancers15215134

Ejemplares similares