Background: Radiomics refers to the extraction of a large amount of image information from medical images, which can provide decision support for clinicians. In this study, we developed and validated a radiomics-based nomogram to predict the prognosis of colorectal cancer (CRC).

Methods: A total of 381 patients with colorectal cancer (primary cohort: n = 242; validation cohort: n = 139) were enrolled and radiomic features were extracted from the vein phase of preoperative computed tomography (CT). The radiomics score was generated by using the least absolute shrinkage and selection operator algorithm (LASSO). A nomogram was constructed by combining the radiomics score with clinicopathological risk factors for predicting the prognosis of CRC patients. The performance of the nomogram was evaluated by the calibration curve, receiver operating characteristic (ROC) curve and C-index statistics. Functional analysis and correlation analysis were used to explore the underlying association between radiomic feature and the gene-expression patterns.

Results: Five radiomic features were selected to calculate the radiomics score by using the LASSO regression model. The Kaplan-Meier analysis showed that radiomics score was significantly associated with disease-free survival (DFS) [primary cohort: hazard ratio (HR): 5.65, 95% CI: 2.26–14.13, P < 0.001; validation cohort: HR: 8.49, 95% CI: 2.05–35.17, P < 0.001]. Multivariable analysis confirmed the independent prognostic value of radiomics score (primary cohort: HR: 5.35, 95% CI: 2.14–13.39, P < 0.001; validation cohort: HR: 5.19, 95% CI: 1.22–22.00, P = 0.026). We incorporated radiomics signature with the TNM stage to build a nomogram, which performed better than TNM stage alone. The C-index of the nomogram achieved 0.74 (0.69–0.80) in the primary cohort and 0.82 (0.77–0.87) in the validation cohort. Functional analysis and correlation analysis found that the radiomic signatures were mainly associated with metabolism related pathways.

Conclusions: The radiomics score derived from the preoperative CT image was an independent prognostic factor and could be a complement to the current staging strategies of colorectal cancer.

背景：放射学是指从医学图像中提取大量图像信息，这可以为临床医生提供决策支持。在这项研究中，我们开发并验证了基于放射学的列线图，以预测结直肠癌（CRC）的预后。

方法： 总共381例结直肠癌患者（主要队列： ñ= 242; 验证队列：ñ（= 139）名患者，并从术前计算机断层扫描（CT）的静脉相中提取放射学特征。通过使用最小绝对收缩和选择算子算法（LASSO）生成放射学分数。通过将放射线评分与临床病理危险因素相结合来构建诺模图，以预测CRC患者的预后。通过校准曲线，接收器工作特性（ROC）曲线和C指数统计来评估列线图的性能。使用功能分析和相关分析来探索放射学特征与基因表达模式之间的潜在关联。

结果：通过使用LASSO回归模型，选择了五个放射​​线特征以计算放射线分数。Kaplan-Meier分析显示，放射线评分与无病生存率（DFS）显着相关[主要队列：危险比（HR）：5.65，95％CI：2.26-14.13，P<0.001; 验证队列：HR：8.49，95％CI：2.05–35.17，P<0.001]。多变量分析证实了放射学评分的独立预后价值（主要队列：HR：5.35，95％CI：2.14-13.39，P<0.001; 验证队列：HR：5.19，95％CI：1.22–22.00，P= 0.026）。我们将放射学签名与TNM阶段结合起来，以构建一个诺模图，其性能比单独的TNM阶段要好。在主要队列中，列线图的C指数达到0.74（0.69–0.80），在验证队列中，该指数达到0.82（0.77–0.87）。功能分析和相关分析发现，放射性标记主要与代谢相关途径有关。

结论： 术前CT图像得出的放射学评分是一个独立的预后因素，可以作为当前大肠癌分期策略的补充。