Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features

Quantitative features extracted from biopsy digital pathology images can provide predictive information for neoadjuvant chemoradiotherapy (nCRT) in local advanced rectal cancer (LARC) Machine learning technologies are applied to build the digital‐pathology‐based pathology signature The pathology sig...

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Detalles Bibliográficos
Autores principales: Zhang, Fang, Yao, Su, Li, Zhi, Liang, Changhong, Zhao, Ke, Huang, Yanqi, Gao, Ying, Qu, Jinrong, Li, Zhenhui, Liu, Zaiyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Short Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7403709/
https://www.ncbi.nlm.nih.gov/pubmed/32594660
http://dx.doi.org/10.1002/ctm2.110
Descripción
Sumario:Quantitative features extracted from biopsy digital pathology images can provide predictive information for neoadjuvant chemoradiotherapy (nCRT) in local advanced rectal cancer (LARC) Machine learning technologies are applied to build the digital‐pathology‐based pathology signature The pathology signature is an independent predictor of treatment response to nCRT in LARC [Image: see text]

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