机构：[a]Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China[b]School ofMedicine, South China University of Technology, Guangzhou, China[c]Department of Radiology, Yunnan Cancer Hospital, The Third Affiliated Hospitalof Kunming Medical University, Kunming, China[d]Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy ofMedical Sciences, Guangzhou, China[e]Department of Pathology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences,Guangzhou, China[f]Department of Radiology, Zhuhai People’s Hospital, Zhuhai Hospital Affiliated with Jinan University, Zhuhai, China[g]School ofComputer Science Engineering, South China University of Technology, Guangzhou, China[h]Department of Pathology, Yunnan Cancer Hospital, TheThird Affiliated Hospital of Kunming Medical University, Kunming, China[i]Department of Radiology, Guangzhou First People’s Hospital, School ofMedicine, South China University of Technology, Guangzhou, China
Computerized image analysis for whole-slide images has been shown to improve efficiency, accuracy, and consistency in histopathology evaluations. We aimed to assess whether immunohistochemistry (IHC) image quantitative features can reflect the immune status and provide prognostic information for colorectal cancer patients. A fully automated pipeline was designed to extract histogram features from IHC digital images in a training set (N = 243). A Hist-Immune signature was generated with selected features using the LASSO Cox model. The results were validated using internal (N = 147) and external (N = 76) validation sets. The five-feature-based Hist-Immune signature was significantly associated with overall survival in training (HR 2.72, 95% CI 1.68-4.41, P < .001), internal (2.86, 1.28-6.39, 0.010), and external (2.30, 1.02-6.16, 0.044) validation sets. The full model constructed by integrating the Hist-Immune signature and clinicopathological factors had good discrimination ability (C-index 0.727, 95% CI 0.678-0.776), confirmed using internal (0.703, 0.621-0.784) and external (0.756, 0.653-0.859) validation sets. Our findings indicate that the Hist-Immune signature constructed based on the quantitative features could reflect the immune status of patients with colorectal cancer, which might advocate change in risk stratification and consequent precision medicine.
基金：
National Key Research and Development Program of China [2017YFC130910002]; National Science Fund for Distinguished Young ScholarsNational Natural Science Foundation of China (NSFC)National Science Fund for Distinguished Young Scholars [81925023]; National Natural Scientific Foundation of ChinaNational Natural Science Foundation of China (NSFC) [81601469, 81771912, 81671854, 82001986, 82072090]; Guangzhou Science and Technology Project of Health [20191A011002]
第一作者机构：[a]Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China[b]School ofMedicine, South China University of Technology, Guangzhou, China
共同第一作者：
通讯作者：
通讯机构：[a]Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China[i]Department of Radiology, Guangzhou First People’s Hospital, School ofMedicine, South China University of Technology, Guangzhou, China[*a]Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou 510080, China[*b]Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Guangzhou 510180, China
推荐引用方式(GB/T 7714)：
Ke Zhao,Zhenhui Li,Yong Li,et al.Hist-Immune signature: a prognostic factor in colorectal cancer using immunohistochemical slide image analysis[J].ONCOIMMUNOLOGY.2020,9(1):doi:10.1080/2162402X.2020.1841935.
APA：
Ke Zhao,Zhenhui Li,Yong Li,Su Yao,Yanqi Huang...&Zaiyi Liu.(2020).Hist-Immune signature: a prognostic factor in colorectal cancer using immunohistochemical slide image analysis.ONCOIMMUNOLOGY,9,(1)
MLA：
Ke Zhao,et al."Hist-Immune signature: a prognostic factor in colorectal cancer using immunohistochemical slide image analysis".ONCOIMMUNOLOGY 9..1(2020)
