Wang Yun, Lyu Deng, Yu Dan, Hu Su, Ma Yanqing, Huang Wenjun, Duan Shaofeng, Zhou Taohu, Tu Wenting, Zhou Xiuxiu, Xiao Yi, Fan Li, Liu Shiyuan
Department of Radiology, Second Affiliated Hospital of Navy Medical University, Shanghai, China.
Department of Radiology, Hangzhou Lin'an District First People's Hospital, Hangzhou, China.
J Thorac Dis. 2024 Aug 31;16(8):5122-5137. doi: 10.21037/jtd-24-243. Epub 2024 Aug 28.
Preoperative accurate judgment of the degree of invasiveness in subpleural ground-glass lung adenocarcinoma (LUAD) with a consolidation-to-tumor ratio (CTR) ≤50% is very important for the choice of surgical timing and planning. This study aims to investigate the performance of intratumoral and peritumoral radiomics combined with computed tomography (CT) features for predicting the invasiveness of LUAD presenting as a subpleural ground-glass nodule (GGN) with a CTR ≤50%.
A total of 247 patients with LUAD from our hospital were randomly divided into two groups, i.e., the training cohort (n=173) and the internal validation cohort (n=74) (7:3 ratio). Furthermore, 47 patients from three other hospitals were collected as the external validation cohort. In the training cohort, the differences in clinical-radiological features were compared using univariate and multivariate analyses. The gross tumor volume (GTV) and gross peritumoral tumor volume (GPTV5, GPTV10, and GPTV15) radiomics models were constructed based on intratumoral and peritumoral (5, 10, and 15 mm) radiomics features. Additionally, the radscore of the best radiomics model and clinical risk factors were used to construct a combined model and the predictive efficacy of the model was evaluated in the validation cohorts. Finally, the receiver operating characteristics (ROC) curve and area under the curve (AUC) value were used to evaluate the discriminative ability of the model.
Tumor size and CTR were independent risk factors for predicting the invasiveness of LUAD. The GPTV10 model outperformed the other radiomics models, with AUC values of 0.910, 0.870, and 0.887 in the three cohorts. The AUC values of the combined model were 0.912, 0.874, and 0.892.
A nomogram based on GPTV10-radscore, tumor size, and CTR exhibited high predictive efficiency for predicting the invasiveness of LUAD.
术前准确判断实性成分与肿瘤体积比（CTR）≤50%的亚实性磨玻璃型肺腺癌（LUAD）的侵袭程度，对于手术时机的选择和规划非常重要。本研究旨在探讨瘤内及瘤周影像组学联合计算机断层扫描（CT）特征对预测表现为亚实性磨玻璃结节（GGN）且CTR≤50%的LUAD侵袭性的效能。
将我院247例LUAD患者随机分为两组，即训练队列（n = 173）和内部验证队列（n = 74）（比例为7:3）。此外，收集其他三家医院的47例患者作为外部验证队列。在训练队列中，采用单因素和多因素分析比较临床放射学特征的差异。基于瘤内及瘤周（5、10和15 mm）影像组学特征构建总体积（GTV）和瘤周总体积（GPTV5、GPTV10和GPTV15）影像组学模型。此外，将最佳影像组学模型的radscore与临床危险因素用于构建联合模型，并在验证队列中评估该模型的预测效能。最后，采用受试者工作特征（ROC）曲线和曲线下面积（AUC）值评估模型的鉴别能力。
肿瘤大小和CTR是预测LUAD侵袭性的独立危险因素。GPTV10模型优于其他影像组学模型，在三个队列中的AUC值分别为0.910、0.870和0.887。联合模型的AUC值分别为0.912、0.874和0.892。
基于GPTV10-radscore、肿瘤大小和CTR的列线图在预测LUAD侵袭性方面具有较高的预测效率。
