Data_Sheet_3_A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer.docx

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 (from study 1) diagnosed with CRC from February 2018 to October 2019 and randomly divided them into training (N = 99) and testing (N = 42) cohorts. Radiomics features in venous phase image were extracted from preoperative computed tomography (CT) images. Gene expression was 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 the radiomics model. A total of 45 CRC patients (study 2) with immunohistochemical (IHC) staining of CXCL8 diagnosed with CRC from January 2014 to October 2018 were included in the independent testing cohort. A clinical model was validated for prognosis prediction in prognostic testing cohort (163 CRC patients from 2014 to 2018, study 3). We performed a combined radiomics model that was composed of radiomics score, tumor stage, and CXCL8-derived radiomics model to make comparison with the clinical model.
Results: In our study, we identified the CXCL8 as a hub gene in affecting prognosis, which is mainly through regulating cytokine–cytokine receptor interaction and neutrophil migration pathway. The radiomics model incorporated 12 radiomics features 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 the prognosis of CRC patients in the prognostic testing cohort, with an area under the 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 radiomics model reflected by CXCL8 combined with tumor stage information is a reliable approach to predict the prognosis in CRC patients and has a potential ability in assisting clinical decision-making.

前言：结直肠癌（colorectal cancer, CRC）患者的预后预测对于优化治疗策略、获得更佳临床结局至关重要。基于高通量定量医学影像挖掘的放射组学（radiomics）是近年来新兴的研究领域。然而，预后、放射组学特征与基因表达三者之间的关联仍未明确。
方法：本研究回顾性分析了2018年2月至2019年10月期间收治的141例结直肠癌患者（研究1），并将其随机划分为训练集（N=99）与测试集（N=42）。从术前静脉期计算机断层扫描（computed tomography, CT）图像中提取静脉期放射组学特征。通过RNA测序（RNA-sequencing）对肿瘤组织的基因表达进行检测。采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）回归模型筛选影像特征并构建放射组学模型。另外纳入2014年1月至2018年10月期间确诊的45例CXCL8免疫组化（immunohistochemical, IHC）染色检测的结直肠癌患者（研究2）作为独立测试集。同时，针对2014年至2018年期间的163例结直肠癌患者（研究3）构建的临床模型在预后测试集中进行了预后预测验证。本研究构建了由放射组学评分、肿瘤分期以及基于CXCL8的放射组学模型组成的联合放射组学模型，并与临床模型进行对比。
结果：本研究鉴定出CXCL8为影响结直肠癌预后的核心基因，其主要通过调控细胞因子-细胞因子受体相互作用及中性粒细胞迁移通路发挥作用。根据训练集内CXCL8的表达水平，通过LASSO筛选得到12个放射组学特征并构建放射组学模型，该模型在测试集及免疫组化测试集中均表现出良好的预测性能。最终，结合肿瘤分期的基于CXCL8的放射组学模型在预后测试集中展现出优异的结直肠癌患者预后预测能力，曲线下面积（area under the curve, AUC）为0.774 [95%置信区间（confidence interval, CI）：0.674~0.874]。基于联合模型分层的结直肠癌患者总生存概率的Kaplan-Meier分析显示，高危组患者的预后显著差于低危组患者（Log-rank检验，P<0.0001）。
结论：本研究证实，以CXCL8为基础结合肿瘤分期信息构建的放射组学模型是一种可靠的结直肠癌患者预后预测方法，具备辅助临床决策的潜在应用价值。