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

Lindsay Angus, Martijn P.A. Starmans, Ana Rajicic, Arlette E. Odink, Mathilde Jalving, Wiro J. Niessen, Jacob J. Visser, Stefan Sleijfer, Stefan Klein, Astrid A.M. van der Veldt

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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.

Original languageEnglish
Article number257
Number of pages12
JournalJournal of Personalized Medicine
Volume11
Issue number4
DOIs
Publication statusPublished - 2021

Keywords

  • Lung neoplasm/metastases
  • Machine learning
  • Melanoma
  • Proto-oncogene proteins B-raf
  • Tomography
  • X-ray computed

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