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Clinical utility of skin cancer and melanoma risk scores for population screening: TRoPICS study
Author(s) -
Shetty A.,
Janda M.,
Fry K.,
Brown S.,
Yau B.,
Schuckmann L. Von,
Thomas S.,
Rayner J.E.,
Spelman L.,
Wagner G.,
Jenkins H.,
Lun K.,
Parbery J.,
Soyer H.P.,
Neale R.E.,
Green A.C.,
Whiteman D.C.,
Olsen C.M.,
Khosrotehrani K.
Publication year - 2021
Publication title -
journal of the european academy of dermatology and venereology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.655
H-Index - 107
eISSN - 1468-3083
pISSN - 0926-9959
DOI - 10.1111/jdv.17062
Subject(s) - medicine , skin cancer , basal cell carcinoma , melanoma , cancer , dermatology , population , basal cell , risk assessment , oncology , environmental health , computer security , cancer research , computer science
Background Screening for skin cancer can be cost‐effective if focused on high‐risk groups. Risk prediction tools have been developed for keratinocyte cancers and melanoma to optimize advice and management. However, few have been validated in a clinical setting over the past few years. Objectives To assess the clinical utility of risk assessment tools to identify individuals with prevalent skin cancers in a volunteer‐based screening clinic. Methods Participants were adults presenting for a skin check at a volunteer‐based skin cancer screening facility. We used previously published tools, based on questionnaire responses, to predict melanoma and keratinocyte cancers [KCs; basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)] and classified each participant into one of five risk categories. Participants subsequently underwent a full skin examination by a dermatologist. All suspicious lesions were biopsied, and all cancers were histopathologically confirmed. Results Of 789 people who presented to the clinic, 507 (64%) consented to the study. Twenty‐two BCCs, 19 SCCs and eight melanomas were diagnosed. The proportion of keratinocyte cancers diagnosed increased according to risk category from <1% in the lowest to 24% in the highest risk category ( P < 0.001). Subtype analysis revealed similar proportionate increases in BCC or SCC prevalence according to risk category. However, a similar proportion of melanoma cases were detected in the low‐risk and high‐risk groups. Conclusion The risk prediction model for keratinocyte cancers can reliably identify individuals with a significant skin cancer burden prior to a skin examination in the community setting. The prediction tool for melanoma needs to be tested in a larger sample exposed to a wider range of environmental risk factors.