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.
The study population included all consecutive patients who were discussed at skin cancer-specialised MDT (SSMDT) with a diagnosis of nasal BCC between January 1, 2015 and December 31, 2015. We conducted analyses for gender, age, anatomical location, histological subtype, tumour size, tumour recurrence, anticoagulation, pacemaker, immunosuppressants and therapeutic modalities (Mohs surgery, conventional excision or radiotherapy). We used S-statistic computing language to develop a supervised machine-learning algorithm.
We found that 37.5% of patients could be reliably predicted to be triaged to Mohs micrographic surgery (MMS), based on tumour location and age. Similarly, the choice of conventional treatment (surgical excision or radiotherapy) by the MDT could be reliably predicted based on the patient’s age, tumour phenotype and lesion size. Accordingly, the algorithm reliably predicted the MDT decision outcome of 45.1% of nasal BCCs.
Our study suggests that the machine-learning approach is a potentially useful tool for predicting MDT decisions for MMS vs conventional surgery or radiotherapy for a significant group of patients. We suggest that utilising this algorithm gives the MDT more time to consider more complex patients, where multiple factors, including recurrence, financial costs and cosmetic outcome, contribute to the final decision, but cannot be reliably predicted to determine that outcome. This approach has the potential to reduce the burden and improve the efficiency of the specialist skin MDT and, in turn, improve patient care, reduce waiting times and reduce the financial burden. Such an algorithm would need to be updated regularly to take into account any changes in patient referral patterns, treatment options or local clinical expertise.
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Data cannot be shared publicly due to the violation of patient confidentiality and the absence of informed consent for public data sharing. Data are available to researchers who meet the criteria for access to confidential NHS data; requests should be made to T Andrew, Department of Plastic and Reconstructive Surgery, Norfolk and Norwich University Hospital (email@example.com).
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None of the authors has a financial interest in any of the products, devices or drugs mentioned in this paper.
Ethics approval and consent to participate
Patient electronic records, including the specialised skin MDT forms, were obtained with the approval of the Clinical Audit and Improvement Department at the Norfolk and Norwich University Hospital (approval code: lPLAS_20-21_A08). The study was performed in accordance with the Declaration of Helsinki.
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The authors declare no competing interests.
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Andrew, T.W., Hamnett, N., Roy, I. et al. Machine-learning algorithm to predict multidisciplinary team treatment recommendations in the management of basal cell carcinoma. Br J Cancer 126, 562–568 (2022). https://doi.org/10.1038/s41416-021-01506-7
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