Subregion-specific 18F-FDG PET-CT radiomics for the pre-treatment prediction of EGFR mutation status in solid lung adenocarcinoma.

IF 2 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING American journal of nuclear medicine and molecular imaging Pub Date : 2024-04-25 eCollection Date: 2024-01-01 DOI:10.62347/DDRR4923
Yun Wang, Guang Yang, Xinyi Gao, Linfa Li, Hongzhou Zhu, Heqing Yi
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Abstract

This study aimed to assess the efficacy of fluor-18 fluorodeoxyglucose (18F-FDG) PET/CT using sub-regional-based radiomics in predicting epidermal growth factor receptor (EGFR) mutation status in pretreatment patients with solid lung adenocarcinoma. A retrospective analysis included 269 patients (134 EGFR+ and 135 EGFR-) who underwent pretreatment 18F-FDG PET/CT scans and EGFR mutation testing. The most metabolically active intratumoral sub-region was identified, and radiomics features from whole tumors or sub-regional regions were used to build classification models. The dataset was split into a 7:3 ratio for training and independent testing. Feature subsets were determined by Pearson correlation and the Kruskal Wallis test and radiomics classifiers were built with support vector machines or logistic regressions. Evaluation metrics, including accuracy, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were employed for different classifiers. Results indicated that the sub-region-based classifier outperformed the whole-tumor classifier in terms of accuracy (73.8% vs. 66.2%), AUC (0.768 vs. 0.632), specificity (65.0% vs. 50.0%), PPV (70.2% vs. 62.2%), and NPV (78.8% vs. 74.0%). The clinical classifier exhibited an accuracy of 75.0%, AUC of 0.768, sensitivity of 72.5%, specificity of 77.5%, PPV of 76.3%, and NPV of 73.8%. The combined classifier, incorporating sub-region analysis and clinical parameters, demonstrated further improvement with an accuracy of 77.5%, AUC of 0.807, sensitivity of 77.5%, specificity of 77.5%, and NPV of 77.5%. The study suggests that sub-region-based 18F-FDG PET/CT radiomics enhances EGFR mutation prediction in solid lung adenocarcinoma, providing a practical and cost-efficient alternative to invasive EGFR testing.

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用于治疗前预测实性肺腺癌表皮生长因子受体突变状态的亚区域特异性 18F-FDG PET-CT 放射组学。
本研究旨在评估基于亚区域放射组学的氟-18-脱氧葡萄糖(18F-FDG)PET/CT在预测实体肺腺癌患者治疗前表皮生长因子受体(EGFR)突变状态方面的疗效。一项回顾性分析纳入了269名接受治疗前18F-FDG PET/CT扫描和表皮生长因子受体突变检测的患者(134名表皮生长因子受体+患者和135名表皮生长因子受体-患者)。确定了瘤内代谢最活跃的亚区,并利用整个肿瘤或亚区的放射组学特征建立了分类模型。数据集按 7:3 的比例进行训练和独立测试。通过皮尔逊相关性和 Kruskal Wallis 检验确定特征子集,并使用支持向量机或逻辑回归建立放射组学分类器。对不同的分类器采用了包括准确率、曲线下面积(AUC)、灵敏度、特异性、阳性预测值(PPV)和阴性预测值(NPV)在内的评价指标。结果表明,基于亚区域的分类器在准确率(73.8% 对 66.2%)、AUC(0.768 对 0.632)、特异性(65.0% 对 50.0%)、PPV(70.2% 对 62.2%)和 NPV(78.8% 对 74.0%)方面均优于全肿瘤分类器。临床分类器的准确率为 75.0%,AUC 为 0.768,灵敏度为 72.5%,特异性为 77.5%,PPV 为 76.3%,NPV 为 73.8%。综合分类器结合了亚区域分析和临床参数,准确率进一步提高,达到 77.5%,AUC 为 0.807,灵敏度为 77.5%,特异性为 77.5%,NPV 为 77.5%。该研究表明,基于亚区域的18F-FDG PET/CT放射组学提高了实体肺腺癌的表皮生长因子受体突变预测能力,为侵入性表皮生长因子受体检测提供了一种实用且具有成本效益的替代方法。
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来源期刊
American journal of nuclear medicine and molecular imaging
American journal of nuclear medicine and molecular imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
自引率
4.00%
发文量
4
期刊介绍: The scope of AJNMMI encompasses all areas of molecular imaging, including but not limited to: positron emission tomography (PET), single-photon emission computed tomography (SPECT), molecular magnetic resonance imaging, magnetic resonance spectroscopy, optical bioluminescence, optical fluorescence, targeted ultrasound, photoacoustic imaging, etc. AJNMMI welcomes original and review articles on both clinical investigation and preclinical research. Occasionally, special topic issues, short communications, editorials, and invited perspectives will also be published. Manuscripts, including figures and tables, must be original and not under consideration by another journal.
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