ArticleThe British journal of dermatology2020
Predicting keratinocyte carcinoma in patients with actinic keratosis: development and internal validation of a multivariable risk-prediction model.
Article in The British journal of dermatology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Predicting skin cancer risk from facial images with an explainable artificial intelligence (XAI) based approach: a proof-of-concept study.EClinicalMedicine · 2024Article
- Evaluating the Efficacy and Safety of 4% 5-Fluorouracil Cream in Patients with Actinic Keratosis: An Expert Opinion.Acta dermato-venereologica · 2023Review
- Where Artificial Intelligence Can Take Us in the Management and Understanding of Cancerization Fields.Cancers · 2023Article
- Patterns and cost of care according to keratinocyte cancer risk stratification in a volunteer population screening clinic: Real-world data from the TRoPICS study.The Australasian journal of dermatology · 2023Article
- Review
- Article
- Response.Chest · 2020Article
- Developing a risk prediction model for keratinocyte carcinoma in patients with actinic keratosis.The British journal of dermatology · 2020Article
- Assessment of Cutaneous Squamous Cell Carcinoma (cSCC) In situ Incidence and the Risk of Developing Invasive cSCC in Patients With Prior cSCC In situ vs the General Population in the Netherlands, 1989-2017.JAMA dermatology · 2020Article
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5 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients with actinic keratosis (AK) are at increased risk for developing keratinocyte carcinoma (KC) but predictive factors and their risk rates are unknown.
objectivesTo develop and internally validate a prediction model to calculate the absolute risk of a first KC in patients with AK.
methodsThe risk-prediction model was based on the prospective population-based Rotterdam Study cohort. We hereto analysed the data of participants with at least one AK lesion at cohort baseline using a multivariable Cox proportional hazards model and included 13 a priori defined candidate predictor variables considering phenotypic, genetic and lifestyle risk factors. KCs were identified by linkage of the data with the Dutch Pathology Registry.
resultsOf the 1169 AK participants at baseline, 176 (15·1%) developed a KC after a median follow-up of 1·8 years. The final model with significant predictors was obtained after backward stepwise selection and comprised the presence of four to nine AKs [hazard ratio (HR) 1·68, 95% confidence interval (CI) 1·17-2·42], 10 or more AKs (HR 2·44, 95% CI 1·65-3·61), AK localization on the upper extremities (HR 0·75, 95% CI 0·52-1·08) or elsewhere except the head (HR 1·40, 95% CI 0·98-2·01) and coffee consumption (HR 0·92, 95% CI 0·84-1·01). Evaluation of the discriminative ability of the model showed a bootstrap validated concordance index (c-index) of 0·60.
conclusionsWe showed that the risk of KC in patients with AK can be calculated with the use of four easily assessable predictor variables. Given the c-index, extension of the model with additional, currently unknown predictor variables is desirable. Linked Comment: Kim et al. Br J Dermatol 2020; 183:415-416.
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