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TRUVACE RECORD VERSION
record: TRV-2026-0768
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-15T06:21:53.240599Z
status: published
lens: g_space
sector: health
headline: Machine-learning-based Phenomapping of Patients with Keratinocyte Carcinoma: Data-driven Subgrouping by Disease Burden, Comorbidities and Socioeconomic Status
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: ML-derived phenomapping of a national KC population identified distinct, clinically relevant KC subgroups | providing a basis for tailored clinical pathways with differentiated resource needs | In 106,490 KC patients, 7 ML-derived clusters were identified
problem_reading: (none)
problem_evidence: (none)
quick_read: 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.
limitation: 
tag: Evidence-backed gain
key_points: 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.
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_reviewed | Acta Dermato-Venereologica | https://doi.org/10.2340/actadv.v106.10.2340/adv-2026-0843 | 2026-08-13
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