Miao Lei, Qiu Tian, Li Yan, Li Jianwei, Jiang Xu, Liu Mengwen, Zhang Xue, Jiang Jiuming, Zhang Huanhuan, Wang Yanmei, Li Xiao, Ying Jianming, Li Meng
Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Transl Lung Cancer Res. 2024 Jun 30;13(6):1232-1246. doi: 10.21037/tlcr-24-56. Epub 2024 Jun 11.
Pulmonary sarcomatoid carcinoma (PSC) is a rare, highly malignant type of non-small cell lung cancer (NSCLC) with a poor prognosis. Targeted drugs for exon 14 (ex14) skipping mutation can have considerable clinical benefits. This study aimed to predict ex14 skipping mutation in PSC patients by whole-tumour texture analysis combined with clinical and conventional contrast-enhanced computed tomography (CECT) features.
This retrospective study included 56 patients with PSC diagnosed by pathology. All patients underwent CECT before surgery or other treatment, and both targeted DNA- and RNA-based next-generation sequencing (NGS) were used to detect ex14 skipping mutation status. The patients were divided into two groups: ex14 skipping mutation and nonmutation groups. Overall, 1,316 texture features of the whole tumour were extracted. We also collected 12 clinical and 20 conventional CECT features. After dimensionality reduction and selection, predictive models were established by multivariate logistic regression analysis. Models were evaluated using the area under the curve (AUC), and the clinical utility of the model was assessed by decision curve analysis.
ex14 skipping mutation was detected in 17.9% of PSCs. Mutations were found more frequently in those (I) who had smaller long- or short-axis diameters (P=0.02, P=0.01); (II) who had lower T stages (I, II) (P=0.02); and (III) with pseudocapsular or annular enhancement (P=0.03). The combined model based on the conventional and texture models yielded the best performance in predicting ex14 skipping mutation with the highest AUC (0.89). The conventional and texture models also had good performance (AUC =0.83 conventional; =0.88 texture).
Whole-tumour texture analysis combined with clinical and conventional CECT features may serve as a noninvasive tool to predict the ex14 skipping mutation status in PSC.
肺肉瘤样癌（PSC）是一种罕见的、高恶性的非小细胞肺癌（NSCLC）类型，预后较差。针对外显子14（ex14）跳跃突变的靶向药物可带来显著的临床益处。本研究旨在通过全肿瘤纹理分析结合临床及传统对比增强计算机断层扫描（CECT）特征，预测PSC患者的ex14跳跃突变情况。
本回顾性研究纳入了56例经病理诊断为PSC的患者。所有患者在手术或其他治疗前均接受了CECT检查，并采用基于DNA和RNA的靶向二代测序（NGS）检测ex14跳跃突变状态。患者被分为两组：ex14跳跃突变组和非突变组。共提取了全肿瘤的1316个纹理特征。我们还收集了12个临床特征和20个传统CECT特征。经过降维和筛选后，通过多因素逻辑回归分析建立预测模型。使用曲线下面积（AUC）评估模型，并通过决策曲线分析评估模型的临床实用性。
17.9%的PSC患者检测到ex14跳跃突变。在以下患者中突变更为常见：（I）长轴或短轴直径较小者（P = 0.02，P = 0.01）；（II）T分期较低（I、II期）者（P = 0.02）；以及（III）具有假包膜或环形强化者（P = 0.03）。基于传统模型和纹理模型的联合模型在预测ex14跳跃突变方面表现最佳，AUC最高（0.89）。传统模型和纹理模型也具有良好的性能（传统模型AUC = 0.83；纹理模型AUC = 0.88）。
全肿瘤纹理分析结合临床及传统CECT特征可作为一种无创工具，用于预测PSC患者的ex14跳跃突变状态。
