AUTHOR=Chu Yanpeng , Li Jie , Zeng Zhaoping , Huang Bin , Zhao Jiaojiao , Liu Qin , Wu Huaping , Fu Jiangping , Zhang Yin , Zhang Yefan , Cai Jianqiang , Zeng Fanxin 

TITLE=A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 10 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.575422

DOI=10.3389/fonc.2020.575422

ISSN=2234-943X

ABSTRACT=Introduction: Prognosis prediction is essential to improve therapeutic strategies and to achieve better clinical outcomes in colorectal cancer (CRC) patients. Radiomics based on high-throughput mining of quantitative medical imaging is an emerging field in recent years. However, the relationship among prognosis, radiomics features, and gene expression remains unknown. 
Methods: We retrospectively analyzed 141 patients diagnosed with CRC from January 2018 to October 2019 and randomly divided them into training (N=99) and testing (N=42) cohorts. Radiomics feature were extracted in venous phase image of preoperative computed tomography (CT) images. Gene expression were detected by RNA-sequencing on tumor tissues. The least absolute shrinkage and selection operator (LASSO) regression model was used for selecting imaging features and building radiomics model. Totally 45 CRC patients with immunohistochemical staining of CXCL8 diagnosed with CRC from January 2014 to October 2018 were included in the independent testing cohort. Combined radiomics model comprised of radiomics score, tumor stage, and CXCL8 derived radiomics model, and the clinical model was validated for prognosis prediction in prognostic-testing cohort (163 CRC patients from 2014-2018). 
Results: In our study, we identified that the CXCL8 as a hub gene in affecting prognosis, which mainly through regulating neutrophil migration pathway. The radiomics model in incorporated twelve radiomics features were screened by LASSO according to CXCL8 expression in the training cohort and showed good performance in testing and IHC-testing cohorts. Finally, the CXCL8 derived radiomics model combined with tumor stage performed high ability in predicting prognosis of CRC patients in prognostic testing cohort, with an area under curve (AUC) of 0.774 (95% confidence interval (CI): 0.674-0.874). Kaplan–Meier analysis of the overall survival probability in CRC patients stratified by combined model revealed that high risk patients have a poor prognosis compared with low risk patients (Log-rank P < 0.0001).
Conclusion: We demonstrated that the radiomic model reflected by CXCL8 combined with tumor stage information is a reliable and noninvasive approach to predict prognosis in CRC patients and has a potential ability in assisting clinical decision-making.