Meissner, Anna-Katharina and Gutsche, Robin and Galldiks, Norbert and Kocher, Martin and Juenger, Stephanie T. and Eich, Marie-Lisa and Montesinos-Rongen, Manuel and Brunn, Anna and Deckert, Martina and Wendl, Christina and Dietmaier, Wolfgang and Goldbrunner, Roland and Ruge, Maximilian and Mauch, Cornelia and Schmidt, Nils-Ole and Proescholdt, Martin and Grau, Stefan and Lohmann, Philipp (2022) Radiomics for the noninvasive prediction of the BRAF mutation status in patients with melanoma brain metastases. NEURO-ONCOLOGY, 24 (8). pp. 1331-1340. ISSN 1522-8517, 1523-5866
Full text not available from this repository. (Request a copy)Abstract
Background The BRAF V600E mutation is present in approximately 50% of patients with melanoma brain metastases and an important prerequisite for response to targeted therapies, particularly BRAF inhibitors. As heterogeneity in terms of BRAF mutation status may occur in melanoma patients, a wild-type extracranial primary tumor does not necessarily rule out a targetable mutation in brain metastases using BRAF inhibitors. We evaluated the potential of MRI radiomics for a noninvasive prediction of the intracranial BRAF mutation status. Methods Fifty-nine patients with melanoma brain metastases from two university brain tumor centers (group 1, 45 patients; group 2, 14 patients) underwent tumor resection with subsequent genetic analysis of the intracranial BRAF mutation status. Preoperative contrast-enhanced MRI was manually segmented and analyzed. Group 1 was used for model training and validation, group 2 for model testing. After 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 of 45 patients (49%) in group 1, and 8 of 14 patients (57%) in group 2 had an intracranial BRAF V600E mutation. A linear support vector machine classifier using a six-parameter radiomics signature yielded an area under the ROC curve of 0.92 (sensitivity, 83%; specificity, 88%) in the test data. Conclusions The developed radiomics classifier allows a noninvasive prediction of the intracranial BRAF V600E mutation status in patients with melanoma brain metastases with high diagnostic performance.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | GUIDELINES; DIAGNOSIS; artificial intelligence (AI); brain tumors; machine learning; MRI; radiogenomics |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Neurochirurgie Medicine > Lehrstuhl für Pathologie Medicine > Lehrstuhl für Röntgendiagnostik Medicine > Zentrum für Neuroradiologie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 20 Feb 2024 07:29 |
| Last Modified: | 20 Feb 2024 07:29 |
| URI: | https://pred.uni-regensburg.de/id/eprint/57478 |
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