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基于影像组学的非小细胞肺癌表皮生长因子受体突变亚型预测。

Radiomics for the prediction of EGFR mutation subtypes in non-small cell lung cancer.

机构信息

School of Medical Informatics, China Medical University, Shenyang, Liaoning, 110122, China.

Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, 110004, China.

出版信息

Med Phys. 2019 Oct;46(10):4545-4552. doi: 10.1002/mp.13747. Epub 2019 Aug 20.

DOI:10.1002/mp.13747
PMID:31376283
Abstract

PURPOSE

This retrospective study was designed to investigate the ability of radiomics to predict the mutation status of epidermal growth factor receptor (EGFR) subtypes (19Del and L858R) in patients with non-small cell lung cancer (NSCLC).

METHODS

In total, 312 patients with NSCLC were included, and 580 radiomic features were extracted from the computed tomography images of each patient. In the training set, univariate analysis was performed on the clinical and radiomic features; logistic regression models were established using a 5-fold cross validation strategy for the prediction of EGFR subtypes 19Del and L858R. Subsequently, the predictive ability of the joint models was evaluated using the test set.

RESULTS

The results revealed that the radiomic features specific for EGFR 19Del and L858R were Gabor's MTRVariance, Gabor's PTREntropy, and sphericity. Additionally, the respective areas under the receiver operating characteristic curves of the EGFR 19Del and L858R joint models were 0.7925 and 0.7750 for the test set.

CONCLUSIONS

Our study demonstrated the potential for radiomics to predict EGFR 19Del and L858R. Epidermal growth factor receptor 19Del and L858R exhibited distinct imaging phenotypes, which may help to guide the selection of more accurate and personalized treatment programs for patients with NSCLC.

摘要

目的

本回顾性研究旨在探讨放射组学预测非小细胞肺癌(NSCLC)患者表皮生长因子受体(EGFR)亚型(19 号外显子缺失和 L858R 突变)突变状态的能力。

方法

共纳入 312 例 NSCLC 患者,从每位患者的 CT 图像中提取 580 个放射组学特征。在训练集中,对临床和放射组学特征进行单因素分析;采用 5 折交叉验证策略建立逻辑回归模型,用于预测 EGFR 亚型 19 号外显子缺失和 L858R。随后,使用测试集评估联合模型的预测能力。

结果

结果表明,EGFR 19 号外显子缺失和 L858R 特有的放射组学特征为 Gabor 的 MTRVariance、Gabor 的 PTREntropy 和球形度。此外,EGFR 19 号外显子缺失和 L858R 联合模型在测试集的受试者工作特征曲线下面积分别为 0.7925 和 0.7750。

结论

本研究表明放射组学具有预测 EGFR 19 号外显子缺失和 L858R 的潜力。EGFR 19 号外显子缺失和 L858R 表现出不同的影像学表型,这可能有助于指导 NSCLC 患者更准确和个体化的治疗方案选择。

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