Pyoderma gangrenosum arising during treatment of psoriasis with adalimumab: Effectiveness of ustekinumab

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Ensemble Method of Convolutional Neural Networks with Directed Acyclic Graph Using Dermoscopic Images: Melanoma Detection Application

International audienceThe early detection of melanoma is the most efficient way to reduce its mortality rate. Dermatologists achieve this task with the help of dermoscopy, a non-invasive tool allowing the visualization of patterns of skin lesions. Computer-aided diagnosis (CAD) systems developed on dermoscopic images are needed to assist dermatologists. These systems rely mainly on multiclass classification approaches. However, the multiclass classification of skin lesions by an automated system remains a challenging task. Decomposing a multiclass problem into a binary problem can reduce the complexity of the initial problem and increase the overall performance. This paper proposes a CAD system to classify dermoscopic images into three diagnosis classes: melanoma, nevi, and seborrheic keratosis. We introduce a novel ensemble scheme of convolutional neural networks (CNNs), inspired by decomposition and ensemble methods, to improve the performance of the CAD system. Unlike conventional ensemble methods, we use a directed acyclic graph to aggregate binary CNNs for the melanoma detection task. On the ISIC 2018 public dataset, our method achieves the best balanced accuracy (76.6%) among multiclass CNNs, an ensemble of multiclass CNNs with classical aggregation methods, and other related works. Our results reveal that the directed acyclic graph is a meaningful approach to develop a reliable and robust automated diagnosis system for the multiclass classification of dermoscopic images

Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions

International audienceEarly detection of melanoma remains a daily challenge due to the increasing number of cases and the lack of dermatologists. Thus, AI-assisted diagnosis is considered as a possible solution for this issue. Despite the great advances brought by deep learning and especially convolutionalneural networks (CNNs), computer-aided diagnosis (CAD) systems are still not used in clinical practice. This may be explained by the dermatologist’s fear of being misled by a false negative and the assimilation of CNNs to a “black box”, making their decision process difficult to understandby a non-expert. Decision theory, especially game theory, is a potential solution as it focuses on identifying the best decision option that maximizes the decision-maker’s expected utility. This study presents a new framework for automated melanoma diagnosis. Pursuing the goal of improvingthe performance of existing systems, our approach also attempts to bring more transparency in the decision process. The proposed framework includes a multi-class CNN and six binary CNNs assimilated to players. The players’ strategies is to first cluster the pigmented lesions (melanoma,nevus, and benign keratosis), using the introduced method of evaluating the confidence of the predictions, into confidence level (confident, medium, uncertain). Then, a subset of players has the strategy to refine the diagnosis for difficult lesions with medium and uncertain prediction. We usedEfficientNetB5 as the backbone of our networks and evaluated our approach on the public ISIC dataset consisting of 8917 lesions: melanoma (1113), nevi (6705) and benign keratosis (1099). The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.93 for melanoma, 0.96 for nevus and 0.97 for benign keratosis. Furthermore, our approach outperformed existing methods in this task, improving the balanced accuracy (BACC) of the best compared method from 77% to 86%. These results suggest that our framework provides an effective and explainable decision-making strategy. This approach could help dermatologists in their clinical practice for patients with atypical and difficult-to-diagnose pigmented lesions. We also believe that our system could serve as a didactic tool for less experienced dermatologists

Détection automatique du mélanome : comparaison d’un algorithme fondé sur la caractérisation de l’aspect désordonné de lésions mélanocytaires mimant la pratique des dermatologues, avec une approche par CNN (Convolutional Neural Network)

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In vivo characterisation of healthy human skin with a novel, non-invasive imaging technique : line-field confocal optical coherence tomography

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Diagnosis of congenital pigmented macules in infants with reflectance confocal microscopy and machine learning

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Identification of an Immature Subset of PMN-MDSC Correlated to Response to Checkpoint Inhibitor Therapy in Patients with Metastatic Melanoma.

PMN-MDSCs support tumor progression and resistance to ICI therapy through their suppressive functions but their heterogeneity limits their use as biomarkers in cancer. Our aim was to investigate the phenotypic and functional subsets of PMN-MDSCs to identify biomarkers of response to ICI therapy. We isolated low-density CD15 PMNs from patients with metastatic melanoma and assessed their immune-suppressive capacities. Expression of CD10 and CD16 was used to identify mature and immature subsets and correlate them to inhibition of T cell proliferation or direct cytotoxicity. Frequencies of the PMN-MDSCs subsets were next correlated to the radiological response of 36 patients receiving ICI therapy. Mature activated cells constituted the major population of PMN-MDSCs. They were found in a higher proportion in the pre-treatment blood of patients non responders to ICI. A subset of immature cells characterized by intermediate levels of CD10 and CD16, the absence of expression of SIRPα and a strong direct cytotoxicity to T cells was increased in patients responding to ICI. The paradoxical expansion of such cells during ICI therapy suggests a role of PMNs in the inflammatory events associated to efficient ICI therapy and the usefulness of their monitoring in patients care

Role of community pharmacists in skin cancer screening: A descriptive study of skin cancer risk factors prevalence and photoprotection habits in Barcelona, Catalonia, Spain

Background: Skin cancer incidence is increasing alarmingly, despite current efforts trying to improve its early detection. Community pharmacists have proven success in implementing screening protocols for a number of diseases because of their skills and easy access. Objective: To evaluate the prevalence of skin cancer risk factors and the photoprotection habits with a questionnaire in community pharmacy users. Methods: A research group consisting of pharmacists and dermatologists conducted a descriptive cross-sectional study to assess photoprotection habits and skin cancer risk factors by using a validated questionnaire in 218 community pharmacies in Barcelona from May 23rd to June 13th 2016. All participants received health education on photoprotection and skin cancer prevention. Patients with ≥1 skin cancer risk factor were referred to their physician, as they needed further screening of skin cancer. Results: A total of 5,530 participants were evaluated. Of those, only 20.2% participants had received a total body skin examination for skin cancer screening in the past by a physician and 57.1% reported using a SPF 50+ sunscreen. 53.9% participants presented ≥1 skin cancer risk factor: 11.8% participants reported having skin cancer familial history and 6.2% reported skin cancer personal history; pharmacists found ≥10 melanocytic nevi in 43.8% participants and chronically sun-damaged skin in 21.4%. Lesions suspicious for melanoma were reported in 10.9% of the participants and urgent dermatological evaluation was recommended. Conclusions: Pharmacists can detect people with skin cancer risk factors amongst their users. This intervention can be considered in multidisciplinary strategies of skin cancer screening

Deep Learning for Basal Cell Carcinoma Detection for Reflectance Confocal Microscopy

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