2026-04-17T23:06:22Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3354462026-02-07T12:14:54Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Rey Barroso, Laura author Peña Gutiérrez, Sara author Yáñez Alvarado, Carlos René author Burgos Fernández, Francisco Javier author Vilaseca Ricart, Meritxell author Royo Royo, Santiago author 2021-01-02 The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, threedimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met. This research was supported by the Ministerio de Economía, Industria y Competitividad (MINECO), the Agencia Estatal de Investigación (AEI) and the European Regional Development Fund (FEDER) under the grants DPI2017-89414-R and FIS2017-89850-R. L.R-B. thanks the Spanish Government for the predoctoral FPI grant she received, grant number DPI2017-89414-R. S. P-G. thanks AGAUR for the FI grant she received, grant number 2020FI_B2 00068. C.Y. thanks CONACYT Mexico for the grant he received, grant number 472102 Peer Reviewed Postprint (published version) Àrees temàtiques de la UPC::Ciències de la salut Àrees temàtiques de la UPC::Ciències de la visió Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Skin--Cancer Interferometry Machine learning Cancer--Diagnosis Skin cancer Melanoma Multispectral imaging 3D topography Optical feed-back interferometry Confocal microscopy Optical coherence tomography Polarimetry Machine learning Pell -- Càncer Interferometria Aprenentatge automàtic Càncer -- Diagnòstic Optical technologies for the improvement of skin cancer diagnosis: a review