Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features
Fang Zhang, Su Yao, Zhi Li, Changhong Liang, Ke Zhao, Yanqi Huang, Ying Gao, Jinrong Qu, Zhenhui Li, Zaiyi Liu
Abstract
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.
Topics & Concepts
MedicineDigital pathologyColorectal cancerBiopsyNeoadjuvant therapyChemoradiotherapyDigital image analysisRadiologyPathologyCancerInternal medicineBreast cancerComputer scienceComputer visionRadiomics and Machine Learning in Medical ImagingColorectal Cancer Screening and DetectionAI in cancer detection