Quality Control and Telemedicine for BRAF V600E Mutations in Papillary Thyroid Carcinomas: Image Analysis and Classification and Regression Trees

摘要

The assessment of BRAF V600E mutations is important for prognosis and treatment of Papillary Thyroid Carcinomas (PTC), the standard methods for their identification are molecular biology techniques. In this study, the potential of image morphometry applied to cell nuclei and sequentially the use of a Classification And Regression Tree (CART) is investigated, in order to: identify morphometric features useful to characterize BRAF mutations, and to eventually produce an algorithm identifying BRAF mutation status. The 140 studied cases had histological confirmation and known BRAF mutation status identified via real-time PCR. The analysis revealed that nuclear features contributing to BRAF mutation status identification via the CART model are related mostly to nuclear color. According to the results there is evidence that BRAF V600E mutations can be identified by measurable image features. Therefore, the proposed method is useful for quality control of BRAF V600E mutations on cytological slides, can serve as alternative to PCR method and may be used for remote assessment.