基于 PET/CT 数据和列线图的临床实用放射组学-临床联合模型预测肺腺癌中 EGFR 突变。
A clinically practical radiomics-clinical combined model based on PET/CT data and nomogram predicts EGFR mutation in lung adenocarcinoma.
机构信息
Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, No. 241 West Huaihai Road, Shanghai, 200030, China.
Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China.
出版信息
Eur Radiol. 2021 Aug;31(8):6259-6268. doi: 10.1007/s00330-020-07676-x. Epub 2021 Feb 5.
OBJECTIVES
This study aims to develop a clinically practical model to predict EGFR mutation in lung adenocarcinoma patients according to radiomics signatures based on PET/CT and clinical risk factors.
METHODS
This retrospective study included 583 lung adenocarcinoma patients, including 295 (50.60%) patients with EGFR mutation and 288 (49.40%) patients without EGFR mutation. The clinical risk factors associated with lung adenocarcinoma were collected at the same time. We developed PET/CT, CT, and PET radiomics models for the prediction of EGFR mutation using multivariate logistic regression analysis, respectively. We also constructed a combined PET/CT radiomics-clinical model by nomogram analysis. The diagnostic performance and clinical net benefit of this risk-scoring model were examined via receiver operating characteristic (ROC) curve analysis while the clinical usefulness of this model was evaluated by decision curve analysis (DCA).
RESULTS
The ROC analysis showed predictive performance for the PET/CT radiomics model (AUC = 0.76), better than the PET model (AUC = 0.71, Delong test: Z = 3.03, p value = 0.002) and the CT model (AUC = 0.74, Delong test: Z = 1.66, p value = 0.098). Also, the PET/CT radiomics-clinical combined model has a better performance (AUC = 0.84) to predict EGFR mutation than the PET/CT radiomics model (AUC = 0.76, Delong test: D = 2.70, df = 790.81, p value < 0.001) or the clinical model (AUC = 0.81, Delong test: Z = 3.46, p value < 0.001).
CONCLUSIONS
We demonstrated that the combined PET/CT radiomics-clinical model has an advantage to predict EGFR mutation in lung adenocarcinoma.
KEY POINTS
• Radiomics from lung tumor increase the efficiency of the prediction for EGFR mutation in clinical lung adenocarcinoma on PET/CT. • A radiomic nomogram was developed to predict EGFR mutation. • Combining PET/CT radiomics-clinical model has an advantage to predict EGFR mutation.
目的
本研究旨在根据基于 PET/CT 和临床危险因素的影像学特征，开发一种用于预测肺腺癌患者 EGFR 突变的临床实用模型。
方法
本回顾性研究纳入了 583 例肺腺癌患者，其中 295 例（50.60%）患者 EGFR 突变，288 例（49.40%）患者 EGFR 不突变。同时收集与肺腺癌相关的临床危险因素。我们使用多变量逻辑回归分析分别为预测 EGFR 突变建立了 PET/CT、CT 和 PET 影像学模型。我们还通过列线图分析构建了一个联合 PET/CT 影像学-临床模型。通过接受者操作特征（ROC）曲线分析来评估该风险评分模型的诊断性能和临床净获益，通过决策曲线分析（DCA）来评估该模型的临床实用性。
结果
ROC 分析显示，PET/CT 影像学模型具有预测性能（AUC = 0.76），优于 PET 模型（AUC = 0.71，Delong 检验：Z = 3.03，p 值 = 0.002）和 CT 模型（AUC = 0.74，Delong 检验：Z = 1.66，p 值 = 0.098）。此外，PET/CT 影像学-临床联合模型在预测 EGFR 突变方面的表现优于 PET/CT 影像学模型（AUC = 0.84，Delong 检验：D = 2.70，df = 790.81，p 值 < 0.001）或临床模型（AUC = 0.81，Delong 检验：Z = 3.46，p 值 < 0.001）。
结论
我们证明了联合 PET/CT 影像学-临床模型在预测肺腺癌 EGFR 突变方面具有优势。
关键点
肺肿瘤的影像学特征提高了在临床肺腺癌中基于 PET/CT 的 EGFR 突变预测效率。
开发了一种基于影像学特征的预测 EGFR 突变的列线图。
联合 PET/CT 影像学-临床模型在预测 EGFR 突变方面具有优势。
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