Abstract
The survival of melanoma patients greatly depends on a timely diagnosis followed by the definition of the most suitable treatment. In the last decade, the number of available therapies for melanoma has increased. However, most patients still respond differently to each of them, resulting in an increased health and financial burden. Therefore, it is critical to identify new mechanisms that allow the design of more personalized treatment protocols. Nowadays, it is a standard procedure to screen melanoma patients for BRAF mutations, through a biopsy followed by a PCR analysis. This process takes a considerable amount of time and exhibits different levels of sensitivity. Thus, there is a need to not only accelerate this process, but also automatize it. In this work we propose a new mechanism based on Deep Learning (DL) to predict the BRAF status from dermoscopy image biomarkers. Our preliminary results show that this is a promising venue of research, outperforming previous medical studies.
M. R. Verdelho and S. Gonçalves—Equal contribution.
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17 April 2023
A correction has been published.
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The complete results are presented in the Support Material document.
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Acknowledgments
This work was supported by the FCT project and multi-year funding [CEECIND/ 00326/2017] and LARSyS - FCT Plurianual funding 2020–2023; and by a Google Research Award’21.
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Verdelho, M.R. et al. (2022). Predictive Biomarkers in Melanoma: Detection of BRAF Mutation Using Dermoscopy. In: Kakileti, S.T., et al. Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery. MIABID AIIIMA 2022 2022. Lecture Notes in Computer Science, vol 13602. Springer, Cham. https://doi.org/10.1007/978-3-031-19660-7_17
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