Abstract
Precision prevention of advanced melanoma is fast becoming a realistic prospect, with personalized, holistic risk stratification allowing patients to be directed to an appropriate level of surveillance, ranging from skin self-examinations to regular total body photography with sequential digital dermoscopic imaging. This approach aims to address both underdiagnosis (a missed or delayed melanoma diagnosis) and overdiagnosis (the diagnosis and treatment of indolent lesions that would not have caused a problem). Holistic risk stratification considers several types of melanoma risk factors: clinical phenotype, comprehensive imaging-based phenotype, familial and polygenic risks. Artificial intelligence computer-aided diagnostics combines these risk factors to produce a personalized risk score, and can also assist in assessing the digital and molecular markers of individual lesions. However, to ensure uptake and efficient use of AI systems, researchers will need to carefully consider how best to incorporate privacy and standardization requirements, and above all address consumer trust concerns.
Highlights
Clinician-led skin examinations with dermoscopy are the mainstay of melanoma detection, with unaided (“naked-eye”) examinations alone considered insufficient [1]
High-risk patients are managed with total body photography and sequential digital dermoscopic imaging, [3] allowing clinicians to monitor for changes in melanocytic naevi over time; this is useful for patients with many atypical naevi [4]
Underdiagnosis, a missed or delayed melanoma diagnosis, leading to untreated or improperly treated disease, is a familiar problem to clinicians. This is undesirable in melanoma, where a correct early diagnosis often allows successful treatment with a simple excision, while advanced melanoma treatment is expensive and associated with a poorer prognosis and undesirable side effects of treatment [6, 7]
Summary
Clinician-led skin examinations with dermoscopy are the mainstay of melanoma detection, with unaided (“naked-eye”) examinations alone considered insufficient [1]. High and ultra-high risk patients (those who have multiple risk factors or have already had one or more melanomas, respectively) could potentially benefit from more intensive surveillance by clinicians using total body imaging and sequential digital dermoscopy that can detect early changes of emerging melanomas, especially in patients with multiple and/or atypical naevi, where a diagnosis without photographic documentation may be difficult For these patients, it may soon become possible to use a combination of molecular and digital biomarkers, collected through noninvasive or minimally-invasive techniques, to assess individual lesions for their likelihood to be a true melanoma or an aggressive melanoma. Deep image-based phenotyping is the concept of creating an automated and objective assessment of phenotypic melanoma risk factors directly from total body imaging Such measures include constitutional and facultative skin color, naevus phenotype, freckling phenotype and UV damage phenotype; these sub-clinical factors are known melanoma risk indicators [19]. There are dermatology-specific issues such as nudity in total body photography and difficulty in de-identifying data for secondary use due to the patient being visually identifiable that are not addressed in existing health privacy frameworks [68]
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