Predicting EGFR mutation status in lung adenocarcinoma presenting as ground-glass opacity: utilizing radiomics model in clinical translation
Man Tat Alexander Ng,
Posted 30 May 2021
medRxiv DOI: 10.1101/2021.05.27.21257956
Posted 30 May 2021
Objectives: This study aimed to establish a noninvasive radiomics model based on computed tomography (CT), with favorable sensitivity and specificity to predict EGFR mutation status in GGO-featured lung adenocarcinoma that subsequently guiding the administration of targeted therapy. Methods: Clinical-pathological information and preoperative CT-images of 636 lung adenocarcinoma patients (464, 100, and 72 in the training, internal, and external validation sets, respectively) that underwent GGO lesions resection were included. A total of 1476 radiomic features were extracted with gradient boosting decision tree (GBDT). Results: The established radiomics model containing 252 selected features showed an encouraging discrimination performance of EGFR mutation status (mutant or wild-type), and the predictive ability was superior to that of the clinical model (AUC: 0.901 vs. 0.674, 0.813 vs. 0.730, and 0.801 vs. 0.746 the training, internal, and external validation sets, respectively). The combined radiomics plus clinical model showed no additional benefit over the radiomics model in predicting EGFR status (AUC: 0.909 vs. 0.901, 0.803 vs. 0.813, 0.808 vs. 0.801, respectively, in three cohorts). Uniquely, this model was validated in a cohort of lung adenocarcinoma patients who undertaken adjuvant EGFR-TKIs and harbored unresected GGOs, leading to a significantly improved potency of EGFR-TKIs (response rate: 33.9% vs. 62.5%, P =0.04; before- and after-prediction, respectively). Conclusion: This presented radiomics model can be served as a noninvasive and time-saving approach for predicting the EGFR mutation status in lung adenocarcinoma presenting as GGO.
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