The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning
| Autor: | Ana Rajicic, Astrid A M van der Veldt, Arlette E. Odink, Lindsay Angus, Stefan Sleijfer, Stefan Klein, Wiro J. Niessen, Martijn P. A. Starmans, Mathilde Jalving, Jacob J. Visser |
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| Přispěvatelé: | Targeted Gynaecologic Oncology (TARGON), Medical Oncology, Radiology & Nuclear Medicine, Erasmus MC other |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
endocrine system diseases
Medicine (miscellaneous) lcsh:Medicine Computed tomography X-ray computed Machine learning computer.software_genre Article 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Radiomics Biopsy Medicine skin and connective tissue diseases neoplasms Melanoma Tomography Univariate analysis Lung medicine.diagnostic_test business.industry lcsh:R medicine.disease digestive system diseases enzymes and coenzymes (carbohydrates) medicine.anatomical_structure Lung neoplasm/metastases Feature (computer vision) Proto-oncogene proteins B-raf 030220 oncology & carcinogenesis Mutation (genetic algorithm) Artificial intelligence business computer |
| Zdroj: | Journal of Personalized Medicine, 11(4) Journal of Personalized Medicine Volume 11 Issue 4 Journal of Personalized Medicine, Vol 11, Iss 257, p 257 (2021) Journal of personalized medicine, 11(4):257. MDPI AG Journal of Personalized Medicine, 11(4):257. Multidisciplinary Digital Publishing Institute (MDPI) |
| ISSN: | 2075-4426 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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