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Computer-assisted diagnosis techniques (dermoscopy and spectroscopy-based) for diagnosing skin cancer in adults

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      Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.

      Estimates of the worldwide incidence and mortality from 27 major cancers and for all cancers combined for 2012 are now available in the GLOBOCAN series of the International Agency for Research on Cancer. We review the sources and methods used in compiling the national cancer incidence and mortality estimates, and briefly describe the key results by cancer site and in 20 large "areas" of the world. Overall, there were 14.1 million new cases and 8.2 million deaths in 2012. The most commonly diagnosed cancers were lung (1.82 million), breast (1.67 million), and colorectal (1.36 million); the most common causes of cancer death were lung cancer (1.6 million deaths), liver cancer (745,000 deaths), and stomach cancer (723,000 deaths). © 2014 UICC.
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        QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

        In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.
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          Dermatologist-level classification of skin cancer with deep neural networks

          Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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            Author and article information

            Affiliations
            [1 ]University of Birmingham; Institute of Applied Health Research; Edgbaston Campus Birmingham UK B15 2TT
            [2 ]University Hospitals Birmingham NHS Foundation Trust and University of Birmingham; NIHR Birmingham Biomedical Research Centre; Birmingham UK
            [3 ]Churchill Hospital; Department of Dermatology; Old Road Headington Oxford UK OX3 7LE
            [4 ]The University of Nottingham; c/o Cochrane Skin Group; Nottingham UK
            [5 ]Barts Health NHS Trust; Department of Dermatology; Whitechapel London UK E11BB
            [6 ]City Hospital; Birmingham Skin Centre; Dudley Rd Birmingham UK B18 7QH
            [7 ]Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust; Dermatology; Hills Road Cambridge UK CB2 0QQ
            [8 ]Cardiff and Vale University Health Board; CEDAR Healthcare Technology Research Centre; Cardiff Medicentre, University Hospital of Wales, Heath Park Campus Cardiff Wales UK CF144UJ
            [9 ]University of Oxford; Kennedy Institute of Rheumatology; Oxford UK
            [10 ]Institute of Cancer Research and The Royal Marsden NHS Foundation Trust; Joint Department of Physics; 15 Cotswold Road Sutton UK SM2 5NG
            [11 ]University of Nottingham; Centre of Evidence Based Dermatology; Queen's Medical Centre Derby Road Nottingham UK NG7 2UH
            Journal
            Cochrane Database of Systematic Reviews
            Wiley
            14651858
            December 04 2018
            10.1002/14651858.CD013186
            (Editor)
            © 2018
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