ORIGINAL ARTICLE   Free access

Italian Journal of Dermatology and Venereology 2023 October;158(5):388-94

DOI: 10.23736/S2784-8671.23.07631-4

Copyright © 2023 EDIZIONI MINERVA MEDICA

language: English

Machine learning identification of patient clusters for cutaneous melanoma

Leo J. THOMPSON ^{1, 2}, Al’ona FURMANCHUK ³, Walter LISZEWSKI ¹, Abel KHO ^{2, 3}, Dennis P. WEST ¹, David M. LIEBOVITZ ^{2, 3}, Beatrice NARDONE ^{1, 2, 3} ✉, Pedram GERAMI ¹

¹ Department of Dermatology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; ² Institute for Augmented Intelligence in Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; ³ Department of Medicine (General Internal Medicine), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

BACKGROUND: Cutaneous melanoma is a cancer arising in melanocyte skin cells and is the deadliest form of skin cancer worldwide. Although some risk factors are known, accurate prediction of disease progression and probability for metastasis are difficult to ascertain, given the complexity of the disease and the absence of reliable predictive markers. Since early detection and treatment are essential to enhance survival, this study utilizing machine learning (ML) aims to further delineate additional risk factors associated with cutaneous melanoma.
METHODS: A Bayesian Gaussian Mixture ML model was created with data from 2056 patients diagnosed with cutaneous melanoma and then used to group the patients into six Clusters based on a Silhouette Score analysis. A t-distributed stochastic neighbor embedding (t-SNE) model was used to visualize the six Clusters.
RESULTS: Statistical analysis revealed that Cluster 4 showed a significantly higher rate of metastatic disease, as well as higher Breslow depth at diagnosis, compared to the other five Clusters. Compared to the other five Clusters, patients represented in Cluster 4 also had lower healthcare utilization, fewer dermatology clinic visits, fewer primary care providers, and less frequent colonoscopies and mammograms, and were more likely to smoke and less likely to have a prior diagnosis of basal cell carcinoma.
CONCLUSIONS: This study uncovers gaps in healthcare utilization of services among patient groups with cutaneous melanoma as well as possible implications for management of disease progression. Data-driven analyses emphasize the importance of routine clinic visits to dermatologists and/or primary care physicians (PCPs) for early detection and management of cutaneous melanoma. The findings from this study demonstrate that unsupervised ML methodology may serve to define the best candidate patients to benefit from enhanced dermatology/primary care which, in turn, is expected to improve outcomes for cutaneous melanoma.

KEY WORDS: Melanoma; Risk factors; Machine learning