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Clinical Studies

Machine-learning algorithm to predict multidisciplinary team treatment recommendations in the management of basal cell carcinoma



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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Fig. 1: Outcome of decision-tree model for establishing a classification system for basal cell carcinoma, risk-stratifying cases and predicting clinical treatment.
Fig. 2: Simplified basal cell carcinoma management algorithm based on machine-learning models.

Data availability

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 (


  1. Bichakjian C, Armstrong A, Baum C, Bordeaux J, Brown M, Busam K, et al. Guidelines of care for the management of basal cell carcinoma. J Am Acad Dermatol. 2018.

  2. Cancer Research UK. Non-melanoma skin cancer incidence statistics. 2018. Accessed10 Oct 2020.

  3. Rubin AI, Chen EH, Ratner D. Basal-cell carcinoma. N Engl J Med. 2005.

  4. Taylor C, Munro AJ, Glynne-Jones R, Griffith C, Trevatt P, Richards M, et al. Multidisciplinary team working in cancer: What is the evidence? BMJ. 2010.

  5. NHS England and NHS Improvement. Streamlining multi-disciplinary team meetings. 2020. Accessed 13 Jan 2021.

  6. Soukup T, Morbi A, Lamb BW, Gandamihardja T, Hogben K, Noyes K, et al. A measure of case complexity for streamlining workflow in multidisciplinary tumor boards: mixed methods development and early validation of the MeDiC tool. Cancer Med. 2020.

  7. NICE. Improving outcomes for people with skin tumours including melanoma: evidence update October 2011. NHS Evidence. National Institute for Health and Care Excellence; 2011. Accessed 13 Jan 2021.

  8. Soukup T, Lamb BW, Morbi A, Shah N, Bali A, Asher V, et al. A multicentre cross-sectional observational study of cancer multidisciplinary teams: analysis of team decision making. Cancer Med. 2020.

  9. Therneau T, Atkinson B, Ripley B. rpart: recursive partitioning. version 4.1-1. 2013.

  10. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter S, Blau H, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017.

  11. Van Loo E, Mosterd K, Krekels GAM, Roozeboom M, Ostertag J, Dirksen C, et al. Surgical excision versus Mohs’ micrographic surgery for basal cell carcinoma of the face: a randomised clinical trial with 10 year follow-up. Eur J Cancer. 2014.

  12. Kesson EM, Allardice GM, George WD, Burns HJG, Morrison DS. Effects of multidisciplinary team working on breast cancer survival: retrospective, comparative, interventional cohort study of 13 722 women. BMJ. 2012.

  13. Soukup T, Gandamihardja TAK, McInerney S, Green JSA, Sevdalis N. Do multidisciplinary cancer care teams suffer decision-making fatigue: an observational, longitudinal team improvement study. BMJ Open. 2019.

  14. Camarero-Mulas C, Delgado Jiménez Y, Sanmartín-Jiménez O, Garces J, Rodriguez-Prieto M, Alonso-Alonso T, et al. Mohs micrographic surgery in the elderly: comparison of tumours, surgery and first-year follow-up in patients younger and older than 80 years old in REGESMOHS. J Eur Acad Dermatology Venereol. 2018.

  15. Charles AJ, Otley CC, Pond GR. Prognostic factors for life expectancy in nonagenarians with nonmelanoma skin cancer: implications for selecting surgical candidates. J Am Acad Dermatol. 2002.

  16. Hoorens I, Batteauw A, Van Maele G, Lapiere K, Boone B, Ongenae K. Mohs micrographic surgery for basal cell carcinoma: evaluation of the indication criteria and predictive factors for extensive subclinical spread. Br J Dermatol. 2016.

  17. Mosterd K, Krekels GA, Nieman FH, Ostertag J, Essers B, Dirksen C, et al. Surgical excision versus Mohs’ micrographic surgery for primary and recurrent basal-cell carcinoma of the face: a prospective randomised controlled trial with 5-years’ follow-up. Lancet Oncol. 2008.

  18. Masood A, Al-Jumaily AA. Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int J Biomed Imaging. 2013.

  19. Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. Accuracy of computer-aided diagnosis of melanoma: a meta-analysis. JAMA Dermatol. 2019.

  20. Binder M, Kittler H, Seeber A, Steiner A, Pehamberger H, Wolff K. Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network. Melanoma Res. 1998.

  21. Kalderstam J, Edén P, Ohlsson M. Finding risk groups by optimizing artificial neural networks on the area under the survival curve using genetic algorithms. PLoS ONE. 2015.

  22. Lin FPY, Pokorny A, Teng C, Dear R, Epstein RJ. Computational prediction of multidisciplinary team decision-making for adjuvant breast cancer drug therapies: a machine learning approach. BMC Cancer. 2016.

  23. Somashekhar SP, Sepúlveda MJ, Puglielli S, Norden A, Shortliffe E, Rohit Kumar C, et al. Watson for oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Ann Oncol. 2018.

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None of the authors has a financial interest in any of the products, devices or drugs mentioned in this paper.

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Authors and Affiliations



TWA: designed the study, gathered and analysed data and wrote the paper. NH: gathered data. IR: gathered data. JG: designed the study and analysed the data. JN: designed the study and analysed the data. MDM: designed the study, analysed the data and wrote the paper.

Corresponding author

Correspondence to Tom W. Andrew.

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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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No individual person’s data in any form are included in this publication.

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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).

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