Multimodal Imaging of Basal Cell Carcinoma: A Usability Study

Background:

Basal cell carcinoma (BCC) is the most common type of skin cancer, accounting for approximately 80% of all keratinocyte carcinomas. Although BCC rarely spreads to other parts of the body, it can cause considerable damage to surrounding tissue if left untreated, and both its diagnosis and management place a substantial economic burden on healthcare systems.

The current gold standard for diagnosing BCC is a biopsy with histopathological examination, in which a small sample of skin is removed and examined under a microscope by a pathologist. While highly accurate, this procedure is invasive, causes discomfort, leaves a scar, and requires laboratory processing time before a diagnosis can be confirmed. In clinical practice, dermoscopy (a non-invasive handheld magnification tool) is widely used as a first-line technique to examine surface and near-surface skin structures and guide clinical decision-making. However, dermoscopy does not provide information about tumor depth, subcutaneous invasion, or detailed tissue architecture at the cellular level.

Over the past decade, new non-invasive imaging technologies have emerged that allow detailed visualization of skin lesions at varying depths and resolutions, without the need for tissue removal. These include optical coherence tomography (OCT), line-field confocal optical coherence tomography (LC-OCT), and high-frequency ultrasound (HFUS). Each of these modalities offers complementary information about the skin and underlying tissue. In recent years, artificial intelligence (AI) software modules have been integrated into these imaging systems to assist clinicians in interpreting images and detecting BCC-specific features automatically in real time.

Despite growing evidence that these imaging modalities can detect and characterize BCC with high accuracy, their clinical usability has not been systematically evaluated in a prospective, real-world clinical setting comparing all three modalities head-to-head. This knowledge gap highlights the need for dedicated usability studies to generate essential real-world data that can inform clinical implementation.