Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas

Abstract

Background This study retrospectively evaluated the capability of computed-tomography (CT) based radiomic features to predict EGFR mutation status in surgically-resected peripheral lung adenocarcinomas in an Asian cohort of patients. Materials and Methods 298 patients with surgically resected peripheral lung adenocarcinomas were investigated in this institutional review board-approved retrospective study with waived consent. 219 quantitative 3D features were extracted from segmented volumes of each tumor, and 59 of these which were considered as independent features were included in the analysis. Clinical and pathological information were obtained from the institutional database. Results Mutant EGFR was significantly associated with female gender (p=0.0005); never smoker status (p<0.0001), lepidic predominant adenocarcinomas (p=0.017), and low or intermediate pathologic grade (p=0.0002). Statistically significant differences were found in 11 radiomic features between EGFR mutant and wild type groups on univariate analysis. Mutant EGFR status could be predicted by a set of five radiomic features that fall in three broad groups: CT attenuation energy, tumor main direction and texture defined by wavelets and Laws (AUC 0.647). Multiple logistic regression model showed that adding radiomic features to a clinical model resulted in a significant improvement of predicting power, as the AUC increased from 0.667 to 0.709 (p<0.0001). Conclusions CT based radiomic features of peripheral lung adenocarcinomas can capture useful information regarding tumor phenotype, and the model we built can be useful to predict the presence of EGFR mutations in peripheral lung adenocarcinoma in Asian patients when mutational profiling is not available or possible.

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

“…Few studies have investigated associations between the tumor imaging phenotype and the underlying molecular landscape (24–31,33). These studies generally had small sample sizes, used subjective observer-dependent imaging descriptors, and did not perform robust external validation.…”

Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy

“…18 F-FDG PET/CT imaging data have also been adopted to predict clinical endpoints, such as overall/disease-free survival [15], local/distant recurrence [16], and distant metastasis [17]. Radiomic features are associated with epidermal growth factor receptor mutation status in lung adenocarcinomas [18], and the biological basis of radiomic features have also been explored in the view of molecular pathways in lung cancer [19]. Moreover, the combination of radiomic features and genetic biomarkers could boost the prediction performance for predicting tumor recurrence in stage I NSCLC patients [20].…”

Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition‐based radiomic features

“…Several studies have suggested the use of DWT‐based decomposed images for the calculation of radiomic features . Aerts et al.…”