基于CT影像组学特征预测结直肠癌患者PD-L1表达水平的研究

Research on Predicting PD-L1 Expression Levels in Colorectal Cancer Patients Based on CT Radiomics Features

摘要: 目的：本研究基于CT增强动脉期图像的影像组学标签预测结直肠癌患者PD-L1表达情况。方法：回顾性收集2021年1月~2023年12月行腹部CT增强扫描并在检查后2周内经病理证实、行PD-L1表达水平检测的103例结直肠癌患者，经手术病理证实，PD-L1阳性69例，PD-L1阴性34例，以8:2的比例将患者分为训练组(n = 82)和测试组(n = 21)。CT动脉期图像手动勾画肿瘤感兴趣区(ROI)提取影像组学特征，进一步筛选特征用于构建预测模型。使用AUC评价预测模型的预测性能。DCA评估预测模型的临床价值。结果：从1835个特征中选择11个与PD-L1表达显著相关影像组学特征，建立与PD-L1表达水平显著相关的影像组学标签。训练组中AUC为0.910 (95% CI: 0.848~0.971)，敏感度90.7%，特异度71.4%。测试组AUC为0.867 (95% CI: 0.626~1.000)，敏感度86.7%，特异度83.3%。DCA显示基于CT影像组学标签预测结直肠癌PD-L1表达水平效能较优。结论：增强CT动脉期图像建立的影像组学标签有助于无创预测结直肠癌病灶PD-L1的表达。

Abstract: Objective: This study aims to predict PD-L1 expression in colorectal cancer patients based on radiomic labels derived from CT enhanced arterial phase images. Methods: A retrospective collection was conducted on 103 patients with colorectal cancer who underwent abdominal CT enhanced scans between January 2021 and December 2023, and whose PD-L1 expression levels were tested and pathologically confirmed within 2 weeks after the examination. Surgical pathology confirmed 69 cases of PD-L1 positive and 34 cases of PD-L1 negative. The patients were divided into a training group (n = 82) and a test group (n = 21) at a ratio of 8:2. Radiomic features were extracted from manually delineated regions of interest (ROI) in the tumor on CT arterial phase images, and further feature selection was performed to construct a predictive model. The prediction performance of the model was evaluated using the AUC, and its clinical value was assessed using DCA. Results: Eleven radiomic features significantly associated with PD-L1 expression were selected from 1835 features to establish a radiomic label strongly correlated with PD-L1 expression levels. In the training group, the AUC was 0.910 (95% CI: 0.848~0.971), with a sensitivity of 90.7% and a specificity of 71.4%. In the test group, the AUC was 0.867 (95% CI: 0.626~1.000), with a sensitivity of 86.7% and a specificity of 83.3%. DCA demonstrated that the CT-based radiomic label had good efficacy in predicting PD-L1 expression levels in colorectal cancer. Conclusion: The radiomic label established based on enhanced CT arterial phase images aids in non-invasive prediction of PD-L1 expression in colorectal cancer lesions.