A Clinical Perspective on the Automated Analysis of Reflectance Confocal Microscopy in Dermatology

Background and objectivesNon-invasive optical imaging has the potential to provide a diagnosis without the need for biopsy. One such technology is reflectance confocal microscopy (RCM), which uses low power, near-infrared laser light to enable real-time in vivo visualization of superficial human skin from the epidermis down to the papillary dermis. Although RCM has great potential as a diagnostic tool, there is a need for the development of reliable image analysis programs, as acquired grayscale images can be difficult and time-consuming to visually assess. The purpose of this review is to provide a clinical perspective on the current state of artificial intelligence (AI) for the analysis and diagnostic utility of RCM imaging.Study design/materials and methodsA systematic PubMed search was conducted with additional relevant literature obtained from reference lists.ResultsAlgorithms used for skin stratification, classification of pigmented lesions, and the quantification of photoaging were reviewed. Image segmentation, statistical methods, and machine learning techniques are among the most common methods used to analyze RCM image stacks. The poor visual contrast within RCM images and difficulty navigating image stacks were mediated by machine learning algorithms, which allowed the identification of specific skin layers.ConclusionsAI analysis of RCM images has the potential to increase the clinical utility of this emerging technology. A number of different techniques have been utilized but further refinements are necessary to allow consistent accurate assessments for diagnosis. The automated detection of skin cancers requires more development, but future applications are truly boundless, and it is compelling to envision the role that AI will have in the practice of dermatology. Lasers Surg. Med. © 2020 Wiley Periodicals LLC

Vav2 pharmaco-mimetic mice reveal the therapeutic value and caveats of the catalytic inactivation of a Rho exchange factor

The current paradigm holds that the inhibition of Rho guanosine nucleotide exchange factors (GEFs), the enzymes that stimulate Rho GTPases, can be a valuable therapeutic strategy to treat Rho-dependent tumors. However, formal validation of this idea using in vivo models is still missing. In this context, it is worth remembering that many Rho GEFs can mediate both catalysis-dependent and independent responses, thus raising the possibility that the inhibition of their catalytic activities might not be sufficient per se to block tumorigenic processes. On the other hand, the inhibition of these enzymes can trigger collateral side effects that could preclude the practical implementation of anti-GEF therapies. To address those issues, we have generated mouse models to mimic the effect of the systemic application of an inhibitor for the catalytic activity of the Rho GEF Vav2 at the organismal level. Our results indicate that lowering the catalytic activity of Vav2 below specific thresholds is sufficient to block skin tumor initiation, promotion, and progression. They also reveal that the negative side effects typically induced by the loss of Vav2 can be bypassed depending on the overall level of Vav2 inhibition achieved in vivo. These data underscore the pros and cons of anti-Rho GEF therapies for cancer treatment. They also support the idea that Vav2 could represent a viable drug target.XRB is supported by grants from Worldwide Cancer Research (14-1248), the Castilla-León Government (CSI252P18, CLC-2017-01), the Spanish Ministry of Science and Innovation (MSI) (RTI2018-096481-B-I00), and the Spanish Association against Cancer (GC16173472GARC). XRB’s institution is supported by the Programa de Apoyo a Planes Estratégicos de Investigación de Estructuras de Investigación de Excelencia of the Castilla-León autonomous government (CLC-2017-01). SF, SR-F, and LFL-M contracts have been supported by funding from the MSI (SF, BES-2010-031386; SR-F, BES-2013-063573), the Spanish Ministry of Universities (LFL-M, FPU13/02923), and the CLC-2017-01 grant (SR-F and LFL-M). JR-V has been supported by the CIBERONC and, currently, by the Spanish Association against Cancer. Both Spanish and Castilla-León government-associated funding is partially supported by the European Regional Development Fund.Peer reviewe