机构：[1]The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China[2]Department of Radiology, GuangdongProvincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China[3]Guangdong Provincial Key Laboratory ofArtificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of MedicalSciences, Guangzhou 510080, China[4]Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan CancerHospital, Yunnan Cancer Center, Kunming 650118, China[5]Department of Radiology, Guangzhou First People’s Hospital, School of Medicine,South China University of Technology, Guangzhou 510180, China[6]Department of Radiology, the Affiliated Cancer Hospital of ZhengzhouUniversity & Henan Cancer Hospital, Zhengzhou 450003, China河南省肿瘤医院[7]Department of Radiology, the First Affiliated Hospital of Chongqing MedicalUniversity, Chongqing 400042, China
Objective: This study aimed to establish a method to predict the overall survival (OS) of patients with stage I-III colorectal cancer (CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification. Methods: We retrospectively identified 161 consecutive patients with stage I-III CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction. Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio (HR)=6.670; 95% confidence interval (95% CI): 3.433-12.956; P<0.001), external validation cohort 1 (HR=2.866; 95% CI: 1.646-4.990; P<0.001) and external validation cohort 2 (HR=3.342; 95% CI: 1.289-8.663; P=0.002). Incorporating the EcoRad signature into the prediction model presented a higher prediction ability (P<0.001) with respect to the C-index (0.813, 95% CI: 0.804-0.822 in the training cohort; 0.758, 95% CI: 0.751-0.765 in the external validation cohort 1; and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis (TNM) system, as well as a better calibration, improved reclassification and superior clinical usefulness. Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage I-III CRC patients.
基金：
National Key R&D
Program of China (No. 2021YFF1201003); the Key R&D
Program of Guangdong Province, China (No.
2021B0101420006); the National Science Fund for
Distinguished Young Scholars (No. 81925023 and
82071892); the National Natural Science Foundation of China (No. 81771912 and 82071892); and the National
Natural Science Foundation for Young Scientists of China
(No. 81701782 and 81901910).
第一作者机构：[1]The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China[2]Department of Radiology, GuangdongProvincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China[3]Guangdong Provincial Key Laboratory ofArtificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of MedicalSciences, Guangzhou 510080, China
共同第一作者：
通讯作者：
通讯机构：[1]The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China[2]Department of Radiology, GuangdongProvincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China[3]Guangdong Provincial Key Laboratory ofArtificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of MedicalSciences, Guangzhou 510080, China[*1]Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, Guangzhou 510080, China
推荐引用方式(GB/T 7714)：
Yanqi Huang,Lan He,Zhenhui Li,et al.Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I-III colorectal cancer[J].CHINESE JOURNAL OF CANCER RESEARCH.2022,34(1):doi:10.21147/j.issn.1000-9604.2022.01.04.
APA：
Yanqi Huang,Lan He,Zhenhui Li,Xin Chen,Chu Han...&Zaiyi Liu.(2022).Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I-III colorectal cancer.CHINESE JOURNAL OF CANCER RESEARCH,34,(1)
MLA：
Yanqi Huang,et al."Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I-III colorectal cancer".CHINESE JOURNAL OF CANCER RESEARCH 34..1(2022)
