Virtual biopsy using MRI radiomics for prediction of BRAF status in melanoma brain metastasis

• Published in: Scientific reports 2020-04, Vol.10 (1), p.6623-6623, Article 6623
• Main Authors: Shofty, Ben, Artzi, Moran, Shtrozberg, Shai, Fanizzi, Claudia, DiMeco, Francesco, Haim, Oz, Peleg Hason, Shira, Ram, Zvi, Bashat, Dafna Ben, Grossman, Rachel
• Format: Article
• Language: English
• Subjects: 59/57; 631/378/1689/1690; 692/699/67/2321; Biopsy; Brain cancer; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - secondary; Discordance; Female; Humanities and Social Sciences; Humans; Learning algorithms; Machine learning; Magnetic Resonance Imaging; Male; Melanoma; Melanoma - pathology; Metastases; Metastasis; Middle Aged; Morbidity; multidisciplinary; Mutation; Neuroimaging; Proto-Oncogene Proteins B-raf - genetics; Radiomics; ROC Curve; Science; Science (multidisciplinary); Toxicity
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-63821-y

Brain metastases are common in patients with advanced melanoma and constitute a major cause of morbidity and mortality. Between 40% and 60% of melanomas harbor BRAF mutations. Selective BRAF inhibitor therapy has yielded improvement in clinical outcome; however, genetic discordance between the primary lesion and the metastatic tumor has been shown to occur. Currently, the only way to characterize the genetic landscape of a brain metastasis is by tissue sampling, which carries risks and potential complications. The aim of this study was to investigate the use of radiomics analysis for non-invasive identification of BRAF mutation in patients with melanoma brain metastases, based on conventional magnetic resonance imaging (MRI) data. We applied a machine-learning method, based on MRI radiomics features for noninvasive characterization of the BRAF status of brain metastases from melanoma (BMM) and applied it to BMM patients from two tertiary neuro-oncological centers. All patients underwent surgical resection for BMM, and their BRAF mutation status was determined as part of their oncological work-up. Their routine preoperative MRI study was used for radiomics-based analysis in which 195 features were extracted and classified according to their BRAF status via a support vector machine. The BRAF status of 53 study patients, with 54 brain metastases (25 positive, 29 negative for BRAF mutation) was predicted with mean accuracy = 0.79 ± 0.13, mean precision = 0.77 ± 0.14, mean sensitivity = 0.72 ± 0.20, mean specificity = 0.83 ± 0.11 and with a 0.78 area under the receiver operating characteristic curve for positive BRAF mutation prediction. Radiomics-based noninvasive genetic characterization is feasible and should be further verified using large prospective cohorts.