The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma
The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma
复制标题
放射组学列线图无创预测肺腺癌表皮生长因子受体突变状态和亚型的潜力
DOI：
10.3389/fonc.2019.01485
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发表时间：
2020-01-09
影响因子：
4.7
通讯作者：
Li, Ming
中科院分区：
文献类型：
--
作者：
Zhao, Wei;Wu, Yuzhi;Li, Ming
目的：高达50%的亚洲NSCLC患者存在EGFR基因突变，表明选择合格患者进行EGFR-TKI治疗具有临床重要性。该研究的目的是开发和验证基于放射组学的列线图，整合放射组学，CT特征和临床特征，以非侵入性地预测EGFR突变状态和亚型。材料和方法：我们纳入了637例肺腺癌患者，他们在本研究中进行了EGFR突变分析。将整个数据集随机分为训练数据集（n = 322）和验证数据集（n = 315）。EGFR突变型病变（外显子19和外显子21中的EGFR突变）的子数据集用于探索放射组学特征预测EGFR突变亚型的能力。提取475个放射组学特征，并在训练数据集中使用最小绝对收缩和选择算子（LASSO）回归构建放射组学评分（R评分）。在训练数据集中开发了一个基于放射学的诺模图，结合了临床特征、CT特征和R评分，并在验证数据集中进行了评价。结果如下：构建的R-评分在预测EGFR突变状态和亚型方面取得了有希望的性能，在两个验证数据集中AUC分别为0.694和0.708。此外，构建的基于放射组学的列线图在预测EGFR突变状态和亚型方面优于单独的R评分、临床、CT特征，在两个验证数据集中AUC分别为0.734和0.757。结论：基于放射组学的诺模图结合临床特征、CT特征和放射组学特征，可以无创、高效地预测EGFR突变状态，从而实现精准医疗的最终目的。该方法是预测EGFR突变亚型的一种可能的有前景的策略，为临床治疗方案提供支持。
Purpose: Up to 50% of Asian patients with NSCLC have EGFR gene mutations, indicating that selecting eligible patients for EGFR-TKIs treatments is clinically important. The aim of the study is to develop and validate radiomics-based nomograms, integrating radiomics, CT features and clinical characteristics, to non-invasively predict EGFR mutation status and subtypes. Materials and Methods: We included 637 patients with lung adenocarcinomas, who performed the EGFR mutations analysis in the current study. The whole dataset was randomly split into a training dataset (n = 322) and validation dataset (n = 315). A sub-dataset of EGFR-mutant lesions (EGFR mutation in exon 19 and in exon 21) was used to explore the capability of radiomic features for predicting EGFR mutation subtypes. Four hundred seventy-five radiomic features were extracted and a radiomics sore (R-score) was constructed by using the least absolute shrinkage and selection operator (LASSO) regression in the training dataset. A radiomics-based nomogram, incorporating clinical characteristics, CT features and R-score was developed in the training dataset and evaluated in the validation dataset. Results: The constructed R-scores achieved promising performance on predicting EGFR mutation status and subtypes, with AUCs of 0.694 and 0.708 in two validation datasets, respectively. Moreover, the constructed radiomics-based nomograms excelled the R-scores, clinical, CT features alone in terms of predicting EGFR mutation status and subtypes, with AUCs of 0.734 and 0.757 in two validation datasets, respectively. Conclusions: Radiomics-based nomogram, incorporating clinical characteristics, CT features and radiomic features, can non-invasively and efficiently predict the EGFR mutation status and thus potentially fulfill the ultimate purpose of precision medicine. The methodology is a possible promising strategy to predict EGFR mutation subtypes, providing the support of clinical treatment scenario.