Abstract
BACKGROUND
The BRAF V600E mutation is present in approximately 50% of patients with melanoma and is an important prerequisite for a response to targeted therapies such as BRAF inhibitors. In the majority of patients, the BRAF mutational status is based on the analysis of tissue samples from the extracranial primary tumor only. Since the extracranial and intracranial BRAF mutational status may be discrepant, the additional information on the BRAF mutational status of melanoma brain metastases would be of clinical value, e.g., for the prediction of response to targeted therapies. Here, we evaluated the potential of MRI radiomics for the determination of the intracranial BRAF mutational status in patients with melanoma brain metastases.
PATIENTS AND METHODS
Fifty-nine patients with melanoma brain metastases from two university hospitals (group 1, 45 patients; group 2, 14 patients) were operated with subsequent genetic analysis of the intracranial BRAF mutational status. All patients underwent structural MRI preoperatively. Areas of contrast enhancement were manually segmented and analyzed. Group 1 was used for model training and validation, group 2 for model testing. After image preprocessing and radiomics feature extraction, a test-retest analysis was performed to identify robust features prior to feature selection. Finally, the best performing radiomics model was applied to the test data. Diagnostic performances were evaluated using receiver operating characteristic (ROC) analyses.
RESULTS
Twenty-two patients (49%) in group 1, and 6 patients (43%) in group 2 had an intrametastatic BRAF V600E mutation. Using the best performing six parameter radiomics signature, a linear support vector machine classifier yielded an area under the ROC curve (AUC) of 0.92 (sensitivity, 83%; specificity, 88%) in the test data.
CONCLUSION
The developed radiomics classifier allows a non-invasive prediction of the intracranial BRAF V600E mutational status in patients with melanoma brain metastases and may be of value for treatment decisions.
Genomic Biomarkers of Meningioma: A Focused Review