Background. The incidence of melanoma in situ (MIS) is increasing faster compared to invasive melanoma. Despite varying international practice, a minimum of 5 mm surgical excision margin is currently recommended in the UK. There is no clear guidance on the minimum histological peripheral clearance margins. Aim. This study compares the histological peripheral clearance margins of MIS using wide local excision (WLE) to the rate of recurrence and progression to invasive disease. Methods. A retrospective single-center review was performed over a 5-year period. Inclusion criteria consisted of MIS diagnosis, ≥16 years of age, and treatment with WLE with curative intent. Those patients with a recurrence of a previous MIS or with a reported focus of invasion/regression were also included. Clinicopathological data and follow-up were recorded. Results. 167 MIS were identified in 155 patients, 80% of which were lentigo maligna subtype. Of patients with completely excised MIS on histology (>0 mm), 9% had recurrence with a median time to recurrence of 36 months. Three (1.8%) cases recurred as invasive disease. Age, MIS site, MIS subtype, and histological evidence of foci of invasion/regression did not predict recurrence nor progression to invasive disease ( p > 0.05 ). The recurrence rate of MIS with a histological excision margin ≤3.0 mm was 13% compared to 3% in those with histology margins of >3.0 mm ( p = 0.049 ). Conclusion. A histological peripheral clearance of at least 3.0 mm is advocated to achieve lower recurrence rates. The follow-up duration should be reviewed due to the median recurrence occurring at 36 months in our cohort. Cumulative work on MIS needs to be collated and completed in a large multicenter study with a long follow-up period.
Background Artificial intelligence (AI) is an innovative field with potential for improving burn care. This article provides an updated review on machine learning in burn care and discusses future challenges and the role of healthcare professionals in the successful implementation of AI technologies. Methods A systematic search was carried out on MEDLINE, Embase and PubMed databases for English-language articles studying machine learning in burns. Articles were reviewed quantitatively and qualitatively for clinical applications, key features, algorithms, outcomes and validation methods. Results A total of 46 observational studies were included for review. Assessment of burn depth (n = 26), support vector machines (n = 19) and 10-fold cross-validation (n = 11) were the most common application, algorithm and validation tool used, respectively. Conclusion AI should be incorporated into clinical practice as an adjunct to the experienced burns provider once direct comparative analysis to current gold standards outlining its benefits and risks have been studied. Future considerations must include the development of a burn-specific common framework. Authors should use common validation tools to allow for effective comparisons. Level I/II evidence is required to produce robust proof about clinical and economic impacts.
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