What lies beneath actinic keratosis? Looking beyond visible lesions to optimize patient outcomes
Bibliographic record
Abstract
This supplement presents a summary of the key topics discussed at two satellite symposia presented at the 23rd World Congress of Dermatology Meeting which took place in June 2015 in Vancouver, Canada, and at the 24th European Association of Dermatology and Venereology in Copenhagen, Denmark, held during October 2015. These meetings provided important opportunities to explore the need for treating the field in actinic keratosis (AK) and the role of AK treatment modalities and management styles in meeting patient needs. Intraepithelial UV-induced damage, known as field cancerization or field disease, which can eventually transform into AK, is the precursor of most cutaneous invasive squamous cell carcinomas (iSCCs).1 Traditionally, AK is classified using a three-tiered system depending on whether the focal atypia of basal keratinocytes involves the lower third of the epidermis (AK I), the lower two-thirds (AK II) or the full epidermal thickness (AK III). Until recently, it was widely accepted that progression form AK to iSCC could only occur after an almost complete transformation of the epidermis, following a ‘classical pathway’ of stepwise progression. Thus, AK I lesions were typically considered ‘low risk’.2 However, recent evidence suggests that AK I lesions are the most common precursors of iSCC (emerging through a ‘differentiated pathway’) suggesting that ‘low risk’ AK lesions cannot be easily identified.3 Dr Maria Fernandez Figueras discusses the implications of this finding in clinical practice. Mutations in the p53 tumor suppression gene caused by sunlight have been shown to occur not only in visible AK lesions but also in other skin that has been exposed to sunlight (known as the field).4, 5 Therefore, in the context of AK, field disease is an area of subclinical genetic changes in the periphery of visible AKs;6 indeed, new imaging techniques have confirmed that the disease often affects the entire field that has been exposed to sun.7, 8 Professor Eggert Stockfleth stresses the importance of treating the whole field with the goal of eliminating not just the visible lesions, but also the subclinical AKs, thereby minimizing the risk of recurrence, and potentially preventing progression to iSCC.9, 10 Dr Gary Goldenberg continues with this theme, focussing on the efficacy, safety and tolerability of current treatment options available for AK, paying particular attention to patient-applied field therapies and their suitability depending on specific disease characteristics and patient needs. Finally, Professor Rino Cerio discusses barriers to the effective treatment of AK, providing strategies to overcome these barriers, with a focus on patient education (including sun protection, using treatments correctly and self-checks) and timely follow-ups, delivered within the framework of patient-centered care. I hope that these discussions will assist you in making the best choices in close partnership with your patients, leading to improved treatment adherence, reduced subclinical damage, improved rates of sustained clearance of AKs and ultimately the potential for reduced risk of progression to iSCC. Dr Lear reports receiving funds from LEO Pharma during the development of the manuscript. This supplement was funded by LEO Pharma. It is a publication based on the content presented at the LEO Pharma supported satellite symposia to WCD (Canada) and JEADV (Denmark) in 2015. The publication presents views of the author(s) and not necessarily LEO Pharma. Lucid Group, funded by LEO Pharma, is gratefully acknowledged for editorial support in the preparation of this publication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".