Simple Summary
Squamous cell carcinoma (SCC) is most often preceded by a lesion called actinic keratosis (AK) and is largely due to ultra-violet radiation exposure. Usually, these cancers appear in areas that have been ‘damaged’ by the sun, otherwise known as ‘cancerization fields’, where sub-clinical (invisible to the naked eye), precursor (AK) and cancerous (SCC) lesions coexist. For clinicians, differentiating between the three is not always easy. To facilitate these diagnoses, we dispose now of non-invasive skin imaging techniques that are comparable to a virtual biopsy. The very recent introduction of artificial intelligence could enable us to broaden the application of these technologies when applied to cancerization fields, predicting the risk of malignant transformation of precancerous lesions, guiding treatments and better understanding the mechanisms behind.
Abstract
Squamous cell carcinoma and its precursor lesion actinic keratosis are often found together in areas of skin chronically exposed to sun, otherwise called cancerisation fields. The clinical assessment of cancerisation fields and the correct diagnosis of lesions within these fields is usually challenging for dermatologists. The recent adoption of skin cancer diagnostic imaging techniques, particularly LC-OCT, helps clinicians in guiding treatment decisions of cancerization fields in a non-invasive way. The combination of artificial intelligence and non-invasive skin imaging opens up many possibilities as AI can perform tasks impossible for humans in a reasonable amount of time. In this text we review past examples of the application of AI to dermatological images for actinic keratosis/squamous cell carcinoma diagnosis, and we discuss about the prospects of the application of AI for the characterization and management of cancerization fields.