Department of Radiology, Affiliated Hospital of Hebei University; Clinical Medical college, Hebei University, Baoding, Hebei Province, China.
Hebei Provincial Center for Disease Control and Prevention, Shijiazhuang City, Hebei Province, China.
J Xray Sci Technol. 2023;31(6):1333-1340. doi: 10.3233/XST-230189.
To explore the value of applying computed tomography (CT) radiomics based on different CT-enhanced phases to determine the immunotherapeutic efficacy of non-small cell lung cancer (NSCLC).
106 patients with NSCLC who underwent immunotherapy are randomly divided into training (74) and validation (32) groups. CT-enhanced arterial and venous phase images of patients before treatment are collected. Region-of-interest (ROI) is segmented on the CT-enhanced images, and the radiomic features are extracted. One-way analysis of variance and least absolute shrinkage and selection operator (LASSO) are used to screen the optimal radiomics features and analyze the association between radiomics features and immunotherapy efficacy. The area under receiver-operated characteristic curves (AUC) along with the sensitivity and specificity are computed to evaluate diagnostic effectiveness.
LASSO regression analysis screens and selects 6 and 8 optimal features in the arterial and venous phases images, respectively. Applying to the training group, AUCs based on CT-enhanced arterial and venous phase images are 0.867 (95% CI:0.82-0.94) and 0.880 (95% CI:0.86-0.91) with the sensitivities of 73.91% and 76.19%, and specificities of 66.67% and 72.19%, respectively, while in validation group, AUCs of the arterial and venous phase images are 0.732 (95% CI:0.71-0.78) and 0.832 (95% CI:0.78-0.91) with sensitivities of 75.00% and 76.00%, and specificities of 73.07% and 75.00%, respectively. There are no significant differences between AUC values computed from arterial phases and venous phases images in both training and validation groups (P < 0.05).
The optimally selected radiomics features computed from CT-enhanced different-phase images can provide new imaging marks to evaluate efficacy of the targeted therapy in NSCLC with a high diagnostic value.
探讨基于不同 CT 增强期的 CT 放射组学在确定非小细胞肺癌（NSCLC）免疫治疗疗效中的价值。
将 106 例接受免疫治疗的 NSCLC 患者随机分为训练组（74 例）和验证组（32 例）。采集患者治疗前的 CT 增强动脉期和静脉期图像。对 CT 增强图像进行感兴趣区（ROI）分割，提取放射组学特征。采用单因素方差分析和最小绝对值收缩和选择算子（LASSO）筛选最优放射组学特征，并分析放射组学特征与免疫治疗疗效的相关性。计算受试者工作特征曲线（ROC）下面积（AUC）及敏感度、特异度以评估诊断效能。
LASSO 回归分析筛选并选择动脉期和静脉期图像中的 6 个和 8 个最优特征。在训练组中，基于 CT 增强动脉期和静脉期图像的 AUC 分别为 0.867（95%CI：0.82-0.94）和 0.880（95%CI：0.86-0.91），敏感度分别为 73.91%和 76.19%，特异度分别为 66.67%和 72.19%；在验证组中，动脉期和静脉期图像的 AUC 分别为 0.732（95%CI：0.71-0.78）和 0.832（95%CI：0.78-0.91），敏感度分别为 75.00%和 76.00%，特异度分别为 73.07%和 75.00%。在训练组和验证组中，动脉期和静脉期图像计算的 AUC 值之间无统计学差异（P < 0.05）。
从 CT 增强不同期图像中计算出的最优放射组学特征可为 NSCLC 靶向治疗疗效评估提供新的影像学标志物，具有较高的诊断价值。
