Xie Dong, Yu Jinna, He Cong, Jiang Han, Qiu Yonggang, Fu Linfeng, Kong Lingting, Xu Hongwei
Department of Radiology, Shaoxing Second Hospital Medical Community General Hospital, Shaoxing, China.
Department of Medical Oncology, Shaoxing Second Hospital Medical Community General Hospital, Shaoxing, China.
Front Med (Lausanne). 2025 Apr 3;12:1541376. doi: 10.3389/fmed.2025.1541376. eCollection 2025.
To identify imaging biomarkers of primary tumors and lymph nodes in patients with stage III-IV non-small cell lung cancer (NSCLC) and assess their predictive ability for treatment response (response vs. non-response) to immune checkpoint inhibitors (ICIs) after 6 months.
Retrospective analysis of 83 NSCLC patients treated with ICIs. Quantitative imaging features of the maximum primary lung tumors and lymph nodes on contrast-enhanced CT imaging were extracted at baseline (time point 0, TP0) and after 2-3 cycles of immunotherapy (time point 1, TP1). Delta-radiomics features (delta-RFs) were defined as the net changes in radiomics features (RFs) between TP0 and TP1. Interobserver interclass coefficient (ICC) and Pearson correlation analyses were applied for feature selection, and logistic regression (LR) was used to build a model for predicting treatment response.
Four and five important delta-RFs were selected to construct the nodal and tumor models, respectively. Δ Tumor diameter was used for constructing the clinical prediction model. The predictive efficacy of the nodal model for the treatment response status was higher than that of the tumor and clinical models. In the training set, the AUC values for the three models were 0.96 (95% CI = 0.90-1.00), 0.86 (95% CI = 0.76-0.95), and 0.82 (95% CI = 0.71-0.93), respectively. In the validation set, the AUC values were 0.94 (95% CI = 0.85-1.00), 0.77 (95% CI = 0.56-0.98), and 0.74 (95% CI = 0.48-1.00), respectively.
The nodal model based on delta-RFs performed well in distinguishing responders from non-responders and could identify patients more likely to benefit from immunotherapy. Finally, the nodal model exhibited a higher classification performance than the tumor model.
识别Ⅲ-Ⅳ期非小细胞肺癌（NSCLC）患者原发肿瘤和淋巴结的影像生物标志物，并评估其对免疫检查点抑制剂（ICI）治疗6个月后治疗反应（反应与无反应）的预测能力。
对83例接受ICI治疗的NSCLC患者进行回顾性分析。在基线（时间点0，TP0）和免疫治疗2-3个周期后（时间点1，TP1），提取对比增强CT成像上最大原发肺肿瘤和淋巴结的定量影像特征。将差异放射组学特征（delta-RFs）定义为TP0和TP1之间放射组学特征（RFs）的净变化。采用观察者间组内相关系数（ICC）和Pearson相关性分析进行特征选择，并使用逻辑回归（LR）建立预测治疗反应的模型。
分别选择4个和5个重要的delta-RFs构建淋巴结和肿瘤模型。Δ肿瘤直径用于构建临床预测模型。淋巴结模型对治疗反应状态的预测效能高于肿瘤模型和临床模型。在训练集中，三个模型的AUC值分别为0.96（95%CI = 0.90-1.00）、0.86（95%CI = 0.76-0.95）和0.82（95%CI = 0.71-0.93）。在验证集中，AUC值分别为0.94（95%CI = 0.85-1.00）、0.77（95%CI = 0.56-0.98）和0.74（95%CI = 0.48-1.00）。
基于delta-RFs的淋巴结模型在区分反应者和无反应者方面表现良好，能够识别更可能从免疫治疗中获益的患者。最后，淋巴结模型表现出比肿瘤模型更高的分类性能。
