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

Area of Science:

• Dermatology
• Oncology
• Medical Diagnostics

Background:

• Routine total-body skin examinations are not standardly recommended.
• Patient-initiated concern is the primary driver for seeking medical advice on suspected skin cancer.
• Understanding factors contributing to incidental skin cancer detection is crucial for improving early diagnosis.

Purpose of the Study:

• To investigate the factors associated with the incidental detection of skin cancers.
• To identify gaps in patient understanding regarding malignant skin lesions.
• To analyze predictors of incidental skin cancer discovery in patients with confirmed diagnoses.

Main Methods:

• Study included patients with histologically confirmed basal cell carcinoma, squamous cell carcinoma, or melanoma.
• Data collected included tumor characteristics, patient demographics, and risk factors.
• Incidental skin cancer detection was analyzed using binary logistic regression and Chi-squared Automatic Interaction Detection (CHAID).

Main Results:

• Approximately 26.6% of all tumors were detected incidentally.
• Independent predictors for incidental skin cancer included male sex, living alone, tumor long-axis diameter, location, symptoms, and disease evolution time.
• The CHAID algorithm identified the absence of symptoms at diagnosis as the most significant risk factor for incidental detection.

Conclusions:

• Several demographic and clinical factors are associated with incidental skin cancer detection.
• The lack of symptoms at diagnosis is a key indicator for incidental findings.
• Findings underscore the need for improved patient education and potentially revised screening strategies for skin cancer.