Print Email Facebook Twitter The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning Title The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning Author Angus, Lindsay (Erasmus MC) Starmans, M.P.A. (Erasmus MC) Rajicic, Ana (Erasmus MC) Odink, Arlette E. (Erasmus MC) Jalving, Mathilde (University Medical Center Groningen) Niessen, W.J. (TU Delft ImPhys/Medical Imaging; TU Delft ImPhys/Computational Imaging; Erasmus MC) Visser, Jacob J. (Erasmus MC) Sleijfer, Stefan (Erasmus MC) Klein, Stefan (Erasmus MC) van der Veldt, Astrid A.M. (Erasmus MC) Date 2021 Abstract 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. Subject Lung neoplasm/metastasesMachine learningMelanomaProto-oncogene proteins B-rafTomographyX-ray computed To reference this document use: http://resolver.tudelft.nl/uuid:7f0c1dae-9f6b-41bd-a7c2-ae6efc53e1ff DOI https://doi.org/10.3390/jpm11040257 Source Journal of Personalized Medicine, 11 (4) Part of collection Institutional Repository Document type journal article Rights © 2021 Lindsay Angus, M.P.A. Starmans, Ana Rajicic, Arlette E. Odink, Mathilde Jalving, W.J. Niessen, Jacob J. Visser, Stefan Sleijfer, Stefan Klein, Astrid A.M. van der Veldt Files PDF jpm_11_00257.pdf 5.33 MB Close viewer /islandora/object/uuid:7f0c1dae-9f6b-41bd-a7c2-ae6efc53e1ff/datastream/OBJ/view