Purpose

This study was designed to estimate the clinical significance of the contrast-enhanced computed tomography (CT) textural features for prediction of survival in colorectal cancer (CRC) patients receiving targeted therapy (bevacizumab and cetuximab).

Procedures

The LifeX software was used to extract the textural parameters of the tumor lesions in the contrast-enhanced CT. We used the least absolute shrinkage and selection operator (LASSO) Cox regression and random forest method to screen the non-redundant radiomic features and constructed the CT imaging score. Univariate and multivariate analyses through the Cox proportional hazards model were performed to assess the prognostic clinical factor. Based on the result of multivariate analysis and CT imaging score, combined nomogram model was constructed to predict the overall survival (OS) of patients. Decision curves analysis was employed to evaluate the performance of the combined model and clinical model.

Results

After comparative analysis of the area under curve of the receiver operating characteristic (ROC) curve, we chose the result of random forest model as CT imaging score. Considering the clinical practice and the result of analysis, age, surgery, and lactate dehydrogenase (LDH) level have been introduced into clinical model. Based on the result of analysis and the CT imaging score, we constructed the nomogram combined model. C-index and calibration curve verified the goodness of fit and discrimination of the combined model. Decision curve analysis (DCA) demonstrated that the combined model showed the better net benefit for a 3-year OS than clinical model.

Conclusions

In conclusion, the study provides preliminary evidences that several radiomic parameters of tumor lesions derived from CT images were prognostic factors and predictive markers for CRC patients who are candidates for targeted therapy (bevacizumab and cetuximab).

中文翻译：

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基于增强 CT 的纹理参数作为接受靶向治疗的结直肠癌患者生存的潜在预后因素

目的

本研究旨在评估对比增强计算机断层扫描 (CT) 纹理特征对预测接受靶向治疗（贝伐单抗和西妥昔单抗）的结直肠癌 (CRC) 患者的生存率的临床意义。

程序

LifeX软件用于提取增强CT中肿瘤病灶的纹理参数。我们使用最小绝对收缩和选择算子（LASSO）Cox回归和随机森林方法筛选非冗余放射组学特征并构建CT成像评分。通过 Cox 比例风险模型进行单变量和多变量分析以评估预后临床因素。基于多因素分析结果和CT影像评分，构建组合列线图模型预测患者的总生存期（OS）。决策曲线分析用于评估组合模型和临床模型的性能。

结果

在对受试者工作特征（ROC）曲线的曲线下面积进行对比分析后，我们选择随机森林模型的结果作为CT成像评分。考虑到临床实践和分析结果，将年龄、手术和乳酸脱氢酶（LDH）水平引入临床模型。基于分析结果和CT成像评分，我们构建了列线图组合模型。C指数和校准曲线验证了组合模型的拟合优度和判别性。决策曲线分析 (DCA) 表明，组合模型显示 3 年 OS 的净收益优于临床模型。

结论

总之，该研究提供了初步证据，即来自 CT 图像的肿瘤病变的几个放射组学参数是适合靶向治疗（贝伐单抗和西妥昔单抗）候选人的 CRC 患者的预后因素和预测标志物。