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Patients with  BRAF  mutated ( BRAF -mt) metastatic melanoma benefit significantly from treatment with BRAF inhibitors. Currently, the  BRAF  status is determined on archival tumor tissue or on fresh tumor tissue from an invasive biopsy. The aim of this study was to evaluate whether radiomics can predict the  BRAF  status in a non-invasive manner. Patients with melanoma lung metastases, known  BRAF  status, and a pretreatment computed tomography scan were included. After semi-automatic annotation of the lung lesions (maximum two per patient), 540 radiomics features were extracted. A chest radiologist scored all segmented lung lesions according to the Lung Image Database Consortium (LIDC) criteria. Univariate analysis was performed to assess the predictive value of each feature for  BRAF  mutation status. A combination of various machine learning methods was used to develop  BRAF  decision models based on the radiomics features and LIDC criteria. A total of 169 lung lesions from 103 patients (51  BRAF -mt; 52  BRAF  wild type) were included. There were no features with a significant discriminative value in the univariate analysis. Models based on radiomics features and LIDC criteria both performed as poorly as guessing. Hence, the  BRAF  mutation status in melanoma lung metastases cannot be predicted using radiomics features or visually scored LIDC criteria.

journal of personalized medicine

The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning