Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer.

Jay Kumar Raghavan Nair, Umar Abid Saeed, Connor C McDougall, Ali Sabri, Bojan Kovacina, B V S Raidu, Riaz Ahmed Khokhar, Stephan Probst, Vera Hirsh, Jeffrey Chankowsky, Léon C Van Kempen, Jana Taylor
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引用次数: 38

Abstract

Background: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and 18F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor (EGFR) mutations.

Methods: Fifty patients diagnosed with NSCLC between 2011 and 2015 and with known EGFR mutation status were retrospectively identified. Texture features extracted from pretreatment CT and FDG PET-CT images by manual contouring of the primary tumor were used to develop multivariate logistic regression (LR) models to predict EGFR mutations in exon 19 and exon 20.

Results: An LR model evaluating FDG PET-texture features was able to differentiate EGFR mutant from wild type with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.87, 0.76, 0.66, and 0.71, respectively. The model derived from CT texture features had an AUC, sensitivity, specificity, and accuracy of 0.83, 0.84, 0.73, and 0.78, respectively. FDG PET-texture features that could discriminate between mutations in EGFR exon 19 and 21 demonstrated AUC, sensitivity, specificity, and accuracy of 0.86, 0.84, 0.73, and 0.78, respectively. Based on CT texture features, the AUC, sensitivity, specificity, and accuracy were 0.75, 0.81, 0.69, and 0.75, respectively.

Conclusion: Non-small cell lung cancer texture analysis using FGD-PET and CT images can identify tumors with mutations in EGFR. Imaging signatures could be valuable for pretreatment assessment and prognosis in precision therapy.

使用机器学习技术预测非小细胞肺癌中EGFR突变的放射基因组模型。
背景:本研究的目的是根据具有和不具有表皮生长因子受体(EGFR)突变的非小细胞肺癌(NSCLC)的计算机断层扫描(CT)和18F-FDG PET-CT (FDG PET-CT)图像的纹理特征建立放射基因组学模型。方法:回顾性分析2011年至2015年间确诊为非小细胞肺癌且已知EGFR突变状态的50例患者。通过对原发肿瘤进行手工轮廓处理,从预处理CT和FDG PET-CT图像中提取纹理特征,用于建立多元逻辑回归(LR)模型,预测外显子19和外显子20的EGFR突变。结果:评估FDG pet纹理特征的LR模型能够区分EGFR突变型和野生型,其曲线下面积(AUC)、敏感性、特异性和准确性分别为0.87、0.76、0.66和0.71。基于CT纹理特征的模型AUC、灵敏度、特异度和准确度分别为0.83、0.84、0.73和0.78。FDG pet结构特征可以区分EGFR外显子19和21的突变,AUC、敏感性、特异性和准确性分别为0.86、0.84、0.73和0.78。基于CT纹理特征的AUC、灵敏度、特异度和准确度分别为0.75、0.81、0.69和0.75。结论:利用FGD-PET和CT图像对非小细胞肺癌进行结构分析,可以鉴别出EGFR突变的肿瘤。影像特征对精准治疗的前处理评估和预后有重要价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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