Department of Respiratory Medicine, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
Department of Medical Imaging, Third Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150030, China.
Sci Rep. 2024 Sep 6;14(1):20788. doi: 10.1038/s41598-024-63415-y.
This study aimed to explore potential radiomics biomarkers in predicting the efficiency of chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Eligible patients were prospectively assigned to receive chemo-immunotherapy, and were divided into a primary cohort (n = 138) and an internal validation cohort (n = 58). Additionally, a separative dataset was used as an external validation cohort (n = 60). Radiomics signatures were extracted and selected from the primary tumor sites from chest CT images. A multivariate logistic regression analysis was conducted to identify the independent clinical predictors. Subsequently, a radiomics nomogram model for predicting the efficiency of chemo-immunotherapy was conducted by integrating the selected radiomics signatures and the independent clinical predictors. The receiver operating characteristic (ROC) curves demonstrated that the radiomics model, the clinical model, and the radiomics nomogram model achieved areas under the curve (AUCs) of 0.85 (95% confidence interval [CI] 0.78-0.92), 0.76 (95% CI 0.68-0.84), and 0.89 (95% CI 0.84-0.94), respectively, in the primary cohort. In the internal validation cohort, the corresponding AUCs were 0.93 (95% CI 0.86-1.00), 0.79 (95% CI 0.68-0.91), and 0.96 (95% CI 0.90-1.00) respectively. Moreover, in the external validation cohort, the AUCs were 0.84 (95% CI 0.72-0.96), 0.75 (95% CI 0.62-0.87), and 0.86 (95% CI 0.75-0.96), respectively. In conclusion, the radiomics nomogram provides a convenient model for predicting the effect of chemo-immunotherapy in advanced NSCLC patients.
本研究旨在探索预测晚期非小细胞肺癌（NSCLC）患者化疗免疫治疗效果的潜在放射组学生物标志物。符合条件的患者前瞻性地接受化疗免疫治疗，并分为主要队列（n=138）和内部验证队列（n=58）。此外，还使用分离数据集作为外部验证队列（n=60）。从胸部 CT 图像的原发肿瘤部位提取并选择放射组学特征。通过多变量逻辑回归分析确定独立的临床预测因素。随后，通过整合选定的放射组学特征和独立的临床预测因素，构建了预测化疗免疫治疗效果的放射组学列线图模型。受试者工作特征（ROC）曲线表明，放射组学模型、临床模型和放射组学列线图模型在主要队列中的曲线下面积（AUC）分别为 0.85（95%置信区间 [CI] 0.78-0.92）、0.76（95% CI 0.68-0.84）和 0.89（95% CI 0.84-0.94）。在内部验证队列中，相应的 AUC 分别为 0.93（95% CI 0.86-1.00）、0.79（95% CI 0.68-0.91）和 0.96（95% CI 0.90-1.00）。此外，在外部验证队列中，AUC 分别为 0.84（95% CI 0.72-0.96）、0.75（95% CI 0.62-0.87）和 0.86（95% CI 0.75-0.96）。总之，放射组学列线图为预测晚期 NSCLC 患者化疗免疫治疗效果提供了一种方便的模型。
