Abstract
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.
Highlights
Lung cancer is the leading cause cancer-related death both in male and female [1]
Associations between clinical, CT features and epidermal growth factor receptor (EGFR) mutation status and subtypes were presented in Tables 2, 3
The incidence of harboring EGFR mutation was significantly higher in female than male in two datasets (P = 0.002, P = 0.013, respectively)
Summary
Lung cancer is the leading cause cancer-related death both in male and female [1]. Non-small cell lung cancer (NSCLC) accounts for more than 80% of lung cancers, of which lung adenocarcinoma is the most common histological subtype [2]. In patients with NSCLC, the most commonly found EGFR mutations are deletions in exon 19 (45%) and in exon 21 (L858R in 40%) in patients with EGFR mutations [2]. Both mutations are associated with sensitivity to the small molecule TKIs as well as erlotinib, gefitinib, afatinib, and osimertinib [2], with different survival outcomes in response to both EGFR-TKIs and chemotherapy [6]. Identifying EGFR mutation subtypes, especially those responsive to TKI treatment, seems to be more critically and scientifically important than just predicting EGFR mutation status
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