Ali Sabri1,2, Madiha Batool3, Zhaolin Xu4, Drew Bethune5, Mohamed Abdolell3, Daria Manos3. 1. Department of Diagnostic Radiology, Dalhousie University, Victoria Building, room 307, 1276 South Park Street, PO BOX 9000, Halifax, Nova Scotia, B3H 2Y9, Canada. sabri.ali@gmail.com. 2. Halifax Infirmary, Room 3510, 1796 Summer Street, Halifax, NS, Canada. sabri.ali@gmail.com. 3. Department of Diagnostic Radiology, Dalhousie University, Victoria Building, room 307, 1276 South Park Street, PO BOX 9000, Halifax, Nova Scotia, B3H 2Y9, Canada. 4. Department of Pathology, Dalhousie University, Halifax, Nova Scotia, Canada. 5. Department of Surgery, Dalhousie University, QEII Health Sciences center, Halifax, Nova Scotia, Canada.
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.
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.
Entities:
Keywords:
Adenocarcinoma; EGFR; Gene mutation; Lung cancer; Scoring model
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