Open AccessMachine-learning algorithm to predict multidisciplinary team treatment recommendations in the management of basal cell carcinoma
Author(s) -
Thomas Andrew,
Nathan Hamnett,
Iain Roy,
J. Garioch,
Jenny Nobes,
Marc Moncrieff
Publication year - 2021
Publication title -
british journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.833
H-Index - 236
eISSN - 1532-1827
pISSN - 0007-0920
DOI - 10.1038/s41416-021-01506-7
Subject(s) - medicine , skin cancer , context (archaeology) , basal cell carcinoma , radiation therapy , nose , audit , mohs surgery , radiology , surgery , algorithm , machine learning , cancer , medical physics , pathology , basal cell , computer science , paleontology , management , economics , biology
Basal cell carcinoma (BCC) is the most common human cancer. Facial BCCs most commonly occur on the nose and the management of these lesions is particularly complex, given the functional and complex implications of treatment. Multidisciplinary team (MDT) meetings are routinely held to integrate expertise from dermatologists, surgeons, oncologists, radiologists, pathologists and allied health professionals. The aim of this research was to develop a supervised machine-learning algorithm to predict MDT recommendations for nasal BCC to potentially reduce MDT caseload, provide automatic decision support and permit data audit in a health service context.