通过活检数字病理图像特征预测局部晚期直肠癌新辅助放化疗的治疗反应
Predicting treatment response to neoadjuvant chemoradiotherapy in local advanced rectal cancer by biopsy digital pathology image features.
作者信息
Zhang Fang, Yao Su, Li Zhi, Liang Changhong, Zhao Ke, Huang Yanqi, Gao Ying, Qu Jinrong, Li Zhenhui, Liu Zaiyi
机构信息
School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China.
Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
出版信息
Clin Transl Med. 2020 Jun;10(2):e110. doi: 10.1002/ctm2.110. Epub 2020 Jun 28.
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.
从活检数字病理图像中提取的定量特征可为局部晚期直肠癌（LARC）的新辅助放化疗（nCRT）提供预测信息。机器学习技术被应用于构建基于数字病理的病理特征。该病理特征是LARC中nCRT治疗反应的独立预测指标。
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