Faculty Bibliography

Importance/UNASSIGNED:There is a paucity of evidence to guide physicians regarding prevention strategies for cutaneous squamous cell carcinoma (CSCC) in solid organ transplant recipients (SOTRs). Objective/UNASSIGNED:To examine the development and results of a Delphi process initiated to identify consensus-based medical management recommendations for prevention of CSCC in SOTRs. Evidence Review/UNASSIGNED:Dermatologists with more than 5 years' experience treating SOTRs were invited to participate. A novel actinic damage and skin cancer index (AD-SCI), consisting of 6 ordinal stages corresponding to an increasing burden of actinic damage and CSCC, was used to guide survey design. Three sequential web-based surveys were administered from January 1, 2019, to December 31, 2020. Pursuant to Delphi principles, respondents thoroughly reviewed all peer responses between rounds. Supplemental questions were also asked to better understand panelists' rationale for their responses. Findings/UNASSIGNED:The Delphi panel comprised 48 dermatologists. Respondents represented 13 countries, with 27 (56%) from the US. Twenty-nine respondents (60%) were Mohs surgeons. Consensus was reached with 80% or higher concordance among respondents when presented with a statement, question, or management strategy pertaining to prevention of CSCC in SOTRs. A near-consensus category of 70% to less than 80% concordance was also defined. The AD-SCI stage-based recommendations were established if consensus or near-consensus was achieved. The panel was able to make recommendations for 5 of 6 AD-SCI stages. Key recommendations include the following: cryotherapy for scattered actinic keratosis (AK); field therapy for AK when grouped in 1 anatomical area, unless AKs are thick in which case field therapy and cryotherapy were recommended; combination lesion directed and field therapy with fluorouracil for field cancerized skin; and initiation of acitretin therapy and discussion of immunosuppression reduction or modification for patients who develop multiple skin cancers at a high rate (10 CSCCs per year) or develop high-risk CSCC (defined by a tumor with approximately ≥20% risk of nodal metastasis). No consensus recommendation was achieved for SOTRs with a first low risk CSCC. Conclusions and Relevance/UNASSIGNED:Physicians may consider implementation of panel recommendations for prevention of CSCC in SOTRs while awaiting high-level-of-evidence data. Additional clinical trials are needed in areas where consensus was not reached.

Standard histopathology is currently the gold standard for assessment of margin status in Mohs surgical removal of skin cancer. Ex vivo confocal microscopy (XVM) is potentially faster, less costly and inherently 3D/digital compared to standard histopathology. Despite these advantages, XVM use is not widespread due, in part, to the need for pathologists to retrain to interpret XVM images. We developed artificial intelligence (AI)-driven XVM pathology by implementing algorithms that render intuitive XVM pathology images identical to standard histopathology and produce automated tumor positivity maps. XVM images have fluorescence labeling of cellular and nuclear biology on the background of endogenous (unstained) reflectance contrast as a grounding counter-contrast. XVM images of 26 surgical excision specimens discarded after Mohs micrographic surgery were used to develop an XVM data pipeline with 4 stages: flattening, colorizing, enhancement and automated diagnosis. The first two stages were novel, deterministic image processing algorithms, and the second two were AI algorithms. Diagnostic sensitivity and specificity were calculated for basal cell carcinoma detection as proof of principal for the XVM image processing pipeline. The resulting diagnostic readouts mimicked the appearance of histopathology and found tumor positivity that required first collapsing the confocal stack to a 2D image optimized for cellular fluorescence contrast, then a dark field-to-bright field colorizing transformation, then either an AI image transformation for visual inspection or an AI diagnostic binary image segmentation of tumor obtaining a diagnostic sensitivity and specificity of 88% and 91% respectively. These results show that video-assisted micrographic XVM pathology could feasibly aid margin status determination in micrographic surgery of skin cancer.

It is well known that solid-organ transplant recipients (SOTRs) have a 65- to 100-fold increase in the risk of developing skin cancer, namely, nonmelanoma skin cancers (NMSCs) such as cutaneous squamous cell carcinoma (cSCC) and basal cell carcinoma (BCC). In addition, these patients are also at increased risk for development of melanoma as well as other less common cutaneous malignancies (Merkel's cell carcinoma, Kaposi's sarcoma). SOTRs with NMSC (namely cSCC) are also at significantly increased risk of poor clinical outcomes including local recurrence, nodal and distant metastasis, and disease-specific death relative to patients who are not immunosuppressed. Increased surveillance and monitoring in patients at risk of aggressive disease and poor outcomes who are on immunosuppression is essential in patients with lung transplants given the high degree of immunosuppression. Increased awareness of risks, treatments, and management allows for improved outcomes in these patients. This article will provide an overview of the risk factors for the development of cutaneous malignancies in organ transplant recipients as well as a detailed discussion of various immunosuppressant and prophylactic medications used in this patient population that contribute to the risk of developing cutaneous malignancies, with an emphasis on NMSC (cSCC and BCC) in lung transplant recipients. Finally, this article includes a discussion on the clinical and dermatologic management of this high-risk immunosuppressed population including a review of topical and systemic agents for field therapy of actinic damage and chemoprevention of keratinocyte carcinomas. In addition, indications for additional treatment and preventive measures such as adjuvant radiation treatment after surgical management of cutaneous malignancies and potential modification of immunosuppressive medication regimens are discussed.

Merkel cell carcinoma (MCC) is an aggressive and rare cutaneous cancer of the mechanoreceptor unit of the skin with a neuroendocrine origin. MCC incidence has been on the rise over the past two decades. Risk factors include old age, chronic UV exposure, and immunosuppression. Although MCC is a cutaneous malignancy that is often misdiagnosed as a benign nodule at the time of diagnosis, it has an aggressive disease course due to its high recurrence and metastatic potential. The PD-1/PD-L1 checkpoint blockade has recently shown promising results in the management of advanced MCC. Avelumab and pembrolizumab are considered the new standard of care for metastatic MCC. Despite advances in the field, studies are needed to elucidate the role of immunotherapy for patients who are resistant to treatment. Most ongoing clinical trials aim to assess the efficacy of checkpoint inhibitor combination therapies. This article reviews the most current literature on the surgical and medical management of MCC.

SIGNIFICANCE/CONCLUSIONS:Melanoma is a deadly cancer that physicians struggle to diagnose early because they lack the knowledge to differentiate benign from malignant lesions. Deep machine learning approaches to image analysis offer promise but lack the transparency to be widely adopted as stand-alone diagnostics. AIM/OBJECTIVE:We aimed to create a transparent machine learning technology (i.e., not deep learning) to discriminate melanomas from nevi in dermoscopy images and an interface for sensory cue integration. APPROACH/METHODS:Imaging biomarker cues (IBCs) fed ensemble machine learning classifier (Eclass) training while raw images fed deep learning classifier training. We compared the areas under the diagnostic receiver operator curves. RESULTS:Our interpretable machine learning algorithm outperformed the leading deep-learning approach 75% of the time. The user interface displayed only the diagnostic imaging biomarkers as IBCs. CONCLUSIONS:From a translational perspective, Eclass is better than convolutional machine learning diagnosis in that physicians can embrace it faster than black box outputs. Imaging biomarkers cues may be used during sensory cue integration in clinical screening. Our method may be applied to other image-based diagnostic analyses, including pathology and radiology.