Predicting EGFR mutation status in lung cancer:Proposal for a scoring model using imaging and demographic characteristics

Objective: To determine if a combination of CT and demographic features can predict EGFR mutation status in bronchogenic carcinoma. Methods: We reviewed demographic and CT features for patients with molecular profiling for resected non-small cell lung carcinoma. Using multivariate logistic regression, we identified features predictive of EGFR mutation. Prognostic factors identified from the logistic regression model were then used to build a more practical scoring system. Results: A scoring system awarding 5 points for no or minimal smoking history, 3 points for tumours with ground glass component, 3 points for airbronchograms, 2 points for absence of preoperative evidence of nodal enlargement or metastases and 1 point for doubling time of more than a year, resulted in an AUROC of 0.861. A total score of at least 8 yielded a specificity of 95 %. On multivariate analysis sex was not found to be predictor of EGFR status. Conclusions: A weighted scoring system combining imaging and demographic data holds promise as a predictor of EGFR status. Further studies are necessary to determine reproducibility in other patient groups. A predictive score may help determine which patients would benefit from molecular profiling and may help inform treatment decisions when molecular profiling is not possible. Key points: • EGFR mutation-targeted chemotherapy for bronchogenic carcinoma has a high success rate. • Mutation testing is not possible in all patients. • EGFR associations include subsolid density, slow tumour growth and minimal/no smoking history. • Demographic or imaging features alone are weak predictors of EGFR status. • A scoring system, using imaging and demographic features, is more predictive.