Journal
CLINICAL AND TRANSLATIONAL MEDICINE
Volume 10, Issue 2, Pages -Publisher
JOHN WILEY & SONS LTD
DOI: 10.1002/ctm2.110
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Funding
- National Key Research and Development Program of China [2017YFC1309100]
- National Science Fund for Distinguished Young Scholars, China [81925023]
- NationalNatural Science Foundation of China [81771912, 81671854, 81702322]
- National Science Foundation forYoung Scientists ofChina [81701782]
- KeyResearch and Development Programof Guangdong Province, China [2018B030339001]
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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
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