Chu Yanpeng, Li Jie, Zeng Zhaoping, Huang Bin, Zhao Jiaojiao, Liu Qin, Wu Huaping, Fu Jiangping, Zhang Yin, Zhang Yefan, Cai Jianqiang, Zeng Fanxin
Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, China.
Department of Cardiology, Peking University First Hospital, Beijing, China.
Front Oncol. 2020 Oct 14;10:575422. doi: 10.3389/fonc.2020.575422. eCollection 2020.
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. We retrospectively analyzed 141 patients (from study 1) diagnosed with CRC from February 2018 to October 2019 and randomly divided them into training ( = 99) and testing ( = 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 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 -derived radiomics model to make comparison with the clinical model. In our study, we identified the 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 expression in the training cohort and showed good performance in testing and IHC testing cohorts. Finally, the -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 < 0.0001). We demonstrated that the radiomics model reflected by 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.
预后预测对于改善治疗策略和实现结直肠癌（CRC）患者更好的临床结局至关重要。基于定量医学影像高通量挖掘的放射组学是近年来新兴的领域。然而，预后、放射组学特征和基因表达之间的关系仍不清楚。我们回顾性分析了2018年2月至2019年10月诊断为CRC的141例患者（来自研究1），并将他们随机分为训练组（n = 99）和测试组（n = 42）。从术前计算机断层扫描（CT）图像中提取静脉期图像的放射组学特征。通过对肿瘤组织进行RNA测序检测基因表达。使用最小绝对收缩和选择算子（LASSO）回归模型选择影像特征并构建放射组学模型。2014年1月至2018年10月诊断为CRC且进行了KRAS免疫组织化学（IHC）染色的45例CRC患者（研究2）被纳入独立测试组。在预后测试组（2014年至2018年的163例CRC患者，研究3）中验证了一个临床模型用于预后预测。我们构建了一个由放射组学评分、肿瘤分期和KRAS衍生的放射组学模型组成的联合放射组学模型，与临床模型进行比较。在我们的研究中，我们确定KRAS是影响预后的枢纽基因，主要通过调节细胞因子-细胞因子受体相互作用和中性粒细胞迁移途径。放射组学模型纳入了根据训练组中KRAS表达经LASSO筛选的12个放射组学特征，在测试组和IHC测试组中表现良好。最后，KRAS衍生的放射组学模型与肿瘤分期相结合，在预后测试组中对CRC患者预后的预测能力较高，曲线下面积（AUC）为0.774 [95%置信区间（CI）：0.674 - 0.874]。对按联合模型分层的CRC患者总体生存概率进行的Kaplan - Meier分析显示，高危患者与低危患者相比预后较差（对数秩检验P < 0.0001）。我们证明，KRAS反映的放射组学模型与肿瘤分期信息相结合是预测CRC患者预后的可靠方法，并且在辅助临床决策方面具有潜在能力。
