Literature DB >> 34935978

Radiomics for the noninvasive prediction of the BRAF mutation status in patients with melanoma brain metastases.

Anna-Katharina Meißner1, Robin Gutsche2,3, Norbert Galldiks2,4,5, Martin Kocher2,6, Stephanie T Jünger1, Marie-Lisa Eich7, Manuel Montesinos-Rongen8, Anna Brunn8, Martina Deckert5,8, Christina Wendl9, Wolfgang Dietmaier10, Roland Goldbrunner1,5, Maximilian I Ruge5,6, Cornelia Mauch5,11, Nils-Ole Schmidt12, Martin Proescholdt12, Stefan Grau1,5, Philipp Lohmann2,6.   

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.
© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  MRI; artificial intelligence (AI); brain tumors; machine learning; radiogenomics

Mesh:

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Year:  2022        PMID: 34935978      PMCID: PMC9340614          DOI: 10.1093/neuonc/noab294

Source DB:  PubMed          Journal:  Neuro Oncol        ISSN: 1522-8517            Impact factor:   13.029


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