aceMachine-learning-based Phenomapping of Patients with Keratinocyte Carcinoma: Data-driven Subgrouping by Disease Burden, Comorbidities and Socioeconomic Status
Abstract: Keratinocyte carcinoma (KC) places a considerable and growing burden on healthcare systems. Given the KC population's heterogeneity, tailored clinical pathways are needed to accommodate diverse management needs. This study applied machine learning (ML)-based phenomapping to identify distinct real-world subgroups within a national KC population using demographic and medical history variables. The study included KC patients treated in publicly-funded, office-based dermatology practices and registered in the Danish…
Military Dermatology by U.S. Army. Office of The Surgeon General. Borden Institute. Public domain
On 2026-08-13, a peer-reviewed study reported machine-learning-based phenomapping of 106,490 keratinocyte carcinoma patients from the Danish Skin Cancer Registry (2014-2022). The model derived seven clusters ranging from young, well-educated, high-income, medically noncomplex females with low-risk BCCs to highly comorbid patients with more SCCs and immunosuppressive drug exposure.
The finding matters because keratinocyte carcinoma places a considerable and growing burden on healthcare systems and the population is heterogeneous. By stratifying patients by disease burden, comorbidities and socioeconomic status, the approach provides a basis for tailored clinical pathways with differentiated resource needs, though the source does not report prospective implementation or outcomes of such pathways.
- Study included 106,490 KC patients treated in publicly-funded, office-based dermatology practices and registered in the Danish Skin Cancer Registry from 2014-2022.
- One cluster (22.2%; n=23,590) was young, well educated high-income medically noncomplex females with low-risk basal cell carcinomas.
- Four clusters (55.2%; n=58,736) had greater dermatological disease burden including facial BCCs, multiple BCCs, more squamous cell carcinomas, and history of skin cancer or actinic keratosis treatment.
- Two clusters (22.7%; n=24,164) were highly comorbid patients with more SCCs, high number of KCs on head and neck and high immunosuppressive drug exposure.
ML-based phenomapping of 106,490 Danish KC patients identified 7 distinct subgroups differentiated by disease burden, comorbidities and socioeconomic status, providing a basis for tailored clinical pathways.
The rundown
The analysis used demographic and medical history variables from the Danish Skin Cancer Registry covering 2014-2022 to perform data-driven subgrouping. The population was drawn from publicly-funded, office-based dermatology practices, reflecting real-world care.
The seven clusters separate along age, sex, education, income, comorbidity, immunosuppressive exposure, tumor type (BCC vs SCC), location, multiplicity, and prior actinic keratosis-related treatment, illustrating heterogeneity beyond single risk factors.
Sources
- Peer-reviewedActa Dermato-Venereologica2026-08-13
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