Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I - III colorectal cancer

Yanqi Huang; Lan He; Zhenhui Li; Xin Chen; Chu Han; Ke Zhao; Yuan Zhang; Jinrong Qu; Yun Mao; Changhong Liang; Zaiyi Liu

doi:10.21147/j.issn.1000-9604.2022.01.04

Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I - III colorectal cancer

Chin J Cancer Res. 2022 Feb 28;34(1):40-52. doi: 10.21147/j.issn.1000-9604.2022.01.04.

Authors

Yanqi Huang^{1

2

3}, Lan He², Zhenhui Li^{2

4}, Xin Chen⁵, Chu Han², Ke Zhao², Yuan Zhang², Jinrong Qu⁶, Yun Mao⁷, Changhong Liang^{1

2}, Zaiyi Liu^{1

2

3}

Affiliations

¹ The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China.
² Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
³ Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China.
⁴ Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, 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 Zhengzhou University & Henan Cancer Hospital, Zhengzhou 450003, China.
⁷ Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China.

PMID: 35355935
PMCID: PMC8913257
DOI: 10.21147/j.issn.1000-9604.2022.01.04

Abstract

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.

Keywords: Radiomics; colorectal cancer; spatial heterogeneity; survival prediction; tumor ecosystem.