Development of a Prediction Model for Patients at Risk of Incidental Skin Cancer: A Multicentre Prospective Study

Authors

  • Álvaro Iglesias-Puzas Department of Dermatology, Hospital Universitario Clínico San Carlos, ES-28040 Madrid, Spain
  • Alberto Conde-Taboada
  • Beatriz Aranegui-Arteaga
  • Eduardo López-Bran

DOI:

https://doi.org/10.2340/00015555-3862

Keywords:

early diagnosis, incidental findings, projections and predictions, skin cancer

Abstract

In the absence of guidelines recommending routine total-body skin examination, patient concern remains the main factor in seeking consultation regarding suspicion of skin cancer. This study explores gaps in patients’ understanding of malignant skin lesions, through the factors associated with incidental skin cancer. Included patients had a confirmed histological diagnosis of basal cell carci­noma, squamous cell carcinoma or melanoma. Tumour characteristics, patient demographics and other risk factors related to the development of skin cancer were obtained from each participant. The main measure was incidental skin cancer detection, using both binary logistic regression and Chi-squared Automatic Interaction Detection (CHAID) algorithm. Of the total tumours, 26.6% were detected incidentally. The following variables: male sex, living alone, long-axis diameter, tumour location, symptoms and time of disease evolution were independent predictors of incidental skin cancer. According to the CHAID algorithm, the most significant risk factor for incidental skin cancer was the absence of symptoms at diagnosis.

Downloads

Download data is not yet available.

References

Duarte AF, Sousa-Pinto B, Freitas A, Delgado L, Costa-Pereira A, Correia O. Skin cancer healthcare impact: a nation-wide assessment of an administrative database. Cancer Epidemiol 2018; 56: 154–160.

Lopez AT, Carvajal RD, Geskin L. Secondary prevention strategies for nonmelanoma skin cancer. Oncology (Williston Park) 2018; 32: 195–200.

Linos E, Katz KA, Colditz GA. Skin cancer – the importance of prevention. JAMA Intern Med 2016; 176: 1435–1436.

Al-Dujaili Z, Henry M, Dorizas AS, Sadick NS. Skin cancer concerns particular to women. Int J Women’s Dermatol 2017; 3: S49–S51.

Argenziano G, Zalaudek I, Hofmann-Wellenhof R, Bakos RM, Bergman W, Blum A, et al. Total body skin examination for skin cancer screening in patients with focused symptoms. J Am Acad Dermatol 2012; 66: 212–219.

Lamerson CL, Eaton K, Sax JL, Kashani-Sabet M. Comparing melanoma invasiveness in dermatologist- versus patient-detected lesions: a retrospective chart review. J Skin Cancer 2012; 2012: 187963.

Graells J, Ojeda RM. Ability of non-melanoma skin cancer patients to self detect second tumours. J Eur Acad Dermatol Venereol 2009; 23: 180–181.

Koh HK, Miller DR, Geller AC, Clapp RW, Mercer MB, Lew RA. Who discovers melanoma? Patterns from a population-based survey. J Am Acad Dermatol 1992; 26: 914–919.

Walter FM, Humphrys E, Tso S, Johnson M, Cohn S. Patient understanding of moles and skin cancer, and factors influencing presentation in primary care: a qualitative study. BMC Fam Pract 2010; 11: 62.

von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet 2007; 370: 1453–1457.

Vila-Payeras A, Domínguez C, Solà A, Nadal C, Taberner R. Incidental skin cancer detection in a hospital department: a prospective study. Actas Dermosifiliogr 2020; 111: 496–502.

Connaboy C, Eagle SR, Johnson CD, Flanagan SD, Mi Q, Nindl BC. Using machine learning to predict lower-extremity injury in US special forces. Med Sci Sports Exerc 2019; 51: 1073–1079.

Atieh MA, Pang JK, Lian K, Wong S, Tawse-Smith A, Ma S, et al. Predicting peri-implant disease: chi-square automatic interaction detection (CHAID) decision tree analysis of risk indicators. J Periodontol 2019; 90: 834–846.

Díaz-Pérez FM, García-González CG, Fyall A. The use of the CHAID algorithm for determining tourism segmentation: a purposeful outcome. Heliyon 2020; 6: e0425.

Hanson JL, Kingsley-Loso JL, Grey KR, Raju SI, Parks PR, Bershow AL, et al. Incidental melanomas detected in veterans referred to dermatology. J Am Acad Dermatol 2016; 74: 462–469.

Avilés-Izquierdo JA, Molina-López I, Rodríguez-Lomba E, Marquez-Rodas I, Suarez-Fernandez R, Lazaro-Ochaita P. Who detects melanoma? Impact of detection patterns on characteristics and prognosis of patients with melanoma. J Am Acad Dermatol 2016; 75: 967–974.

Janda M, Baade PD, Youl PH, Aitken JF, Whiteman DC, Gordon L, et al. The skin awareness study: promoting thorough skin self-examination for skin cancer among men 50 years or older. Contemp Clin Trials 2010; 31: 119–130.

Kingsley-Loso JL, Grey KR, Hanson JL, Raju SI, Parks PR, Bershow AL, et al. Incidental lesions found in veterans referred to dermatology: the value of a dermatologic examination. J Am Acad Dermatol 2015; 72: 651–655.

Stoecker W V., Rader RK, Rabinovitz HS, Oliviero M, Calcara DA, Malters JM, et al. Patient concern as a predictor of cutaneous malignancy. Br J Dermatol 2016; 174: 222–224.

Hartman RI, Xue Y, Singer S, Markossian TW, Joyce C, Mostaghimi A. Modelling the value of risk-stratified skin cancer screening of asymptomatic patients by dermatologists. Br J Dermatol 2020; 183: 509–515.

Aldridge RB, Naysmith L, Ooi ET, Murray CS, Rees JL. The importance of a full clinical examination: assessment of index lesions referred to a skin cancer clinic without a total body skin examination would miss one in three melanomas. Acta Derm Venereol 2013; 93: 689–692.

Additional Files

Published

2021-07-13

How to Cite

Iglesias-Puzas, Álvaro, Conde-Taboada, A., Aranegui-Arteaga, B., & López-Bran, E. (2021). Development of a Prediction Model for Patients at Risk of Incidental Skin Cancer: A Multicentre Prospective Study. Acta Dermato-Venereologica, 101(7), adv00498. https://doi.org/10.2340/00015555-3862