Machine-learning algorithm to predict multidisciplinary team treatment recommendations in the management of basal cell carcinoma.
Aged
Aged, 80 and over
Algorithms
Carcinoma, Basal Cell
/ pathology
Case-Control Studies
Combined Modality Therapy
Decision Support Systems, Clinical
Disease Management
Female
Humans
Male
Middle Aged
Neoplasm Staging
Nose Neoplasms
/ pathology
Patient Care Team
Supervised Machine Learning
Treatment Outcome
Tumor Burden
Journal
British journal of cancer
ISSN: 1532-1827
Titre abrégé: Br J Cancer
Pays: England
ID NLM: 0370635
Informations de publication
Date de publication:
03 2022
03 2022
Historique:
received:
10
11
2020
accepted:
20
07
2021
revised:
21
06
2021
pubmed:
3
9
2021
medline:
11
3
2022
entrez:
2
9
2021
Statut:
ppublish
Résumé
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. lPLAS_20-21_A08.
Sections du résumé
BACKGROUND
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.
METHODS
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.
RESULTS
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.
CONCLUSIONS
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.
CLINICAL TRIAL REGISTRATION
lPLAS_20-21_A08.
Identifiants
pubmed: 34471257
doi: 10.1038/s41416-021-01506-7
pii: 10.1038/s41416-021-01506-7
pmc: PMC8854628
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
562-568Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature Limited.
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