Abstract
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.
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Acknowledgements
The scientific guarantor of this publication is Daria Manos. The authors of this manuscript declare relationships with the following companies: Dr. Z. Xu and Dr D. Bethune: advisory board for Pfizer. The other authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. The tumour bank was supported by The QE II Hospital Foundation, Cancer Care Nova Scotia and the Dalhousie Medical Research Foundation. Molecular testing was partially supported by Pfizer Canada, Roche Canada and Boehringer Ingelheim Canada. The authors state that this work has not received any other funding. One of the authors (M. Abdolell) has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was obtained from all patients in this study. Methodology: Retrospective and performed at one institution This study was initially presented at ECR 2015 in Vienna and won the best onsite oral presentation award in Thoracic oncology.
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Sabri, A., Batool, M., Xu, Z. et al. Predicting EGFR mutation status in lung cancer:Proposal for a scoring model using imaging and demographic characteristics. Eur Radiol 26, 4141–4147 (2016). https://doi.org/10.1007/s00330-016-4252-3
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DOI: https://doi.org/10.1007/s00330-016-4252-3