通过全肿瘤纹理分析结合临床和常规对比增强计算机断层扫描特征预测肺肉瘤样癌中的MET 14外显子跳变。

IF 4 2区医学 Q2 ONCOLOGY

Translational lung cancer research Pub Date : 2024-06-30 Epub Date: 2024-06-11 DOI:10.21037/tlcr-24-56

Lei Miao, Tian Qiu, Yan Li, Jianwei Li, Xu Jiang, Mengwen Liu, Xue Zhang, Jiuming Jiang, Huanhuan Zhang, Yanmei Wang, Xiao Li, Jianming Ying, Meng Li

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All patients underwent CECT before surgery or other treatment, and both targeted DNA- and RNA-based next-generation sequencing (NGS) were used to detect METex14 skipping mutation status. The patients were divided into two groups: METex14 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.Results: METex14 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). 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引用次数: 0

摘要

背景：肺肉瘤样癌（PSC）是一种罕见、高度恶性的非小细胞肺癌（NSCLC），预后较差。针对MET外显子14（METex14）跳过突变的靶向药物可产生可观的临床疗效。本研究旨在通过全肿瘤纹理分析，结合临床和常规对比增强计算机断层扫描（CECT）特征，预测PSC患者的METex14跳越突变：这项回顾性研究纳入了56例经病理诊断的PSC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的下一代测序（NGS）检测METex14跳变突变状态。患者被分为两组：METex14 跳越突变组和非突变组。总共提取了 1316 个肿瘤纹理特征。我们还收集了 12 个临床特征和 20 个常规 CECT 特征。经过降维和筛选，我们通过多变量逻辑回归分析建立了预测模型。使用曲线下面积（AUC）对模型进行评估，并通过决策曲线分析评估模型的临床实用性：结果：在17.9%的PSCs中检测到了METex14跳越突变。在以下情况中更常发现突变：（I）长轴或短轴直径较小（P=0.02，P=0.01）；（II）T分期较低（I，II）（P=0.02）；（III）假囊性或环状强化（P=0.03）。基于传统模型和纹理模型的组合模型在预测 METex14 跳变方面表现最佳，AUC 最高（0.89）。传统模型和纹理模型也有很好的表现（传统模型的AUC=0.83；纹理模型的AUC=0.88）：结论：全瘤纹理分析与临床和常规CECT特征相结合，可作为预测PSC患者METex14跳跃突变状态的无创工具。

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Predicting MET exon 14 skipping mutation in pulmonary sarcomatoid carcinoma by whole-tumour texture analysis combined with clinical and conventional contrast-enhanced computed tomography features.

Background: Pulmonary sarcomatoid carcinoma (PSC) is a rare, highly malignant type of non-small cell lung cancer (NSCLC) with a poor prognosis. Targeted drugs for MET exon 14 (METex14) skipping mutation can have considerable clinical benefits. This study aimed to predict METex14 skipping mutation in PSC patients by whole-tumour texture analysis combined with clinical and conventional contrast-enhanced computed tomography (CECT) features.

Methods: 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 METex14 skipping mutation status. The patients were divided into two groups: METex14 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.

Results: METex14 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 METex14 skipping mutation with the highest AUC (0.89). The conventional and texture models also had good performance (AUC =0.83 conventional; =0.88 texture).

Conclusions: Whole-tumour texture analysis combined with clinical and conventional CECT features may serve as a noninvasive tool to predict the METex14 skipping mutation status in PSC.

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来源期刊

Translational lung cancer research Medicine-Oncology

CiteScore

7.20

自引率

2.50%

发文量

137

期刊介绍： Translational Lung Cancer Research(TLCR, Transl Lung Cancer Res, Print ISSN 2218-6751; Online ISSN 2226-4477) is an international, peer-reviewed, open-access journal, which was founded in March 2012. TLCR is indexed by PubMed/PubMed Central and the Chemical Abstracts Service (CAS) Databases. It is published quarterly the first year, and published bimonthly since February 2013. It provides practical up-to-date information on prevention, early detection, diagnosis, and treatment of lung cancer. Specific areas of its interest include, but not limited to, multimodality therapy, markers, imaging, tumor biology, pathology, chemoprevention, and technical advances related to lung cancer.