The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma

Frontiers in Oncology

Liang Jin¹  Wei Zhao¹  Pan Gao¹  Mingyu Tan¹  Weiling Ma¹  Yanqing Hua¹  Yingli Sun¹  Cheng Li¹  Yuzhi Wu²  Jun Liu²  Ming Li⁴  Ya'nan Xu⁵ 

[1] Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China;Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China;Diagnosis and Treatment Center of Small Lung Nodules of Huadong Hospital, Shanghai, China;Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China;School of Biomedical Engineering, Capital Medical University, Beijing, China;

关键词: EGFR;    radiomics;    nomogram;    lung adenocarcinomas;    CT;   

DOI  :  10.3389/fonc.2019.01485

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown