Extramammary Paget Disease. Part I. Epidemiology, Pathogenesis, Clinical Features, and Diagnosis.

Extramammary Paget disease (EMPD) is a rare skin cancer of apocrine-rich skin that mimics common inflammatory and infectious dermatoses, leading to delays in diagnosis and increased patient morbidity. Better clinical recognition of this entity, multidisciplinary patient assessment, and deeper understanding of the underlying pathophysiology are essential to improve patient care and disease outcomes. It is important to distinguish primary intraepithelial/micro-invasive EMPD from invasive EMPD or cases with adenocarcinoma arising within EMPD. This 2-part continuing medical education series provides a complete picture of EMPD. Part 1 of this continuing medical education series reviews the epidemiology, oncogenesis, clinical and histopathologic presentation, workup, and prognosis of this rare cancer.

Jet-injection assisted photodynamic therapy for superficial and nodular basal cell carcinoma: A pilot study.

BACKGROUND: Photodynamic therapy (PDT) with topical δ-Aminolevulinic acid (ALA) has efficacy in treating basal cell carcinoma (BCC) but is limited by incomplete penetration of ALA into the deeper dermis. This prospective open-label pilot trial investigated the safety and efficacy of photosensitizer jet injection for PDT (JI-PDT) for BCC treatment. It was performed with 15 patients (n = 15) with histologically confirmed, untreated, low-risk nodular BCCs at a single institution. METHODS: For the intervention, JI-PDT patients (n = 11) received two sessions of jet-injected ALA with PDT separated by four to 6 weeks. To further understand treatment technique, another group of patients (n = 4) received jet-injected ALA followed by tumor excision and fluorescence microscopy (JI-E). Treatment tolerability was assessed by local skin responses (LSR) score at five distinct time intervals. Fluorescence microscopy assessed protoporphyrin IX penetration depth and biodistribution within the tumor. At the primary endpoint, tumor clearance was evaluated via visual inspection, dermoscopy and reflectance confocal microscopy. Postinjection and postillumination pain levels, and patient satisfaction, were scored on a 0-10 scale. RESULTS: Fifteen participants with mean age of 58.3, who were 15/15 White, non-Hispanic enrolled. The median composite LSR score immediately after JI-PDT was 5 (interquartile range [IQR] = 3) which decreased to 0.5 (IQR = 1) at primary endpoint (p < 0.01). Immunofluorescence of excised BCC tumors with jet-injected ALA showed photosensitizer penetration into papillary and reticular dermis. Of the 13 JI-PDT tumors, 11 had tumor clearance confirmed, 1 recurred, and 1 was lost to follow-up. 1/11 patients experienced a serious adverse event of cellulitis. 70% of patients had local scarring at 3 months. Patients reported an average pain level of 5.6 (standard deviation [SD] = 2.3) during jet injection and 3.7 (SD = 1.8) during light illumination. CONCLUSIONS: Jet injection of ALA for PDT treatment of nodular low-risk BCC is tolerable and feasible and may represent a novel modality to improve PDT.

Extramammary Paget Disease. Part II. Evidence-based Approach to Management.

Extramammary Paget disease is a rare cutaneous malignancy that most commonly affects the genitals, perianal area, and axilla of elderly patients. Delays in care often lead to high levels of disease burden for patients. Thus, evidence-based recommendations are paramount in mitigating morbidity and mortality for this unique patient population. This 2-part continuing medical education series provides a complete picture of extramammary Paget disease. Part 2 of this continuing medical education series focuses on the complex management of extramammary Paget disease including surgical and non-invasive therapies, as well as novel approaches for advanced disease.

Clinical Utility of Melanoma Sentinel Lymph Node Biopsy Nomograms.

BACKGROUND: For patients with melanoma, the decision to perform sentinel lymph node biopsy (SLNB) is based on the estimated risk of lymph node metastasis. We assessed 3 melanoma SLNB risk-prediction models' statistical performance and their ability to improve clinical decision making (clinical utility) on a cohort of melanoma SLNB cases. STUDY DESIGN: Melanoma patients undergoing SLNB at a single center from 2003 to 2021 were identified. The predicted probabilities of sentinel lymph node positivity using the Melanoma Institute of Australia, Memorial Sloan Kettering Cancer Center (MSK), and Friedman nomograms were calculated. Receiver operating characteristic and calibration curves were generated. Clinical utility was assessed via decision curve analysis, calculating the net SLNBs that could have been avoided had a given model guided selection at different risk thresholds. RESULTS: Of 2,464 melanoma cases that underwent SLNB, 567 (23.0%) had a positive sentinel lymph node. The areas under the receiver operating characteristic curves for the Melanoma Institute of Australia, MSK, and Friedman models were 0.726 (95% CI, 0.702 to 0.750), 0.720 (95% CI, 0.697 to 0.744), and 0.721 (95% CI, 0.699 to 0.744), respectively. For all models, calibration was best at predicted positivity rates below 30%. The MSK model underpredicted risk. At a 10% risk threshold, only the Friedman model would correctly avoid a net of 6.2 SLNBs per 100 patients. The other models did not reduce net avoidable SLNBs at risk thresholds of ≤10%. CONCLUSIONS: The tested nomograms had comparable performance in our cohort. The only model that achieved clinical utility at risk thresholds of ≤10% was the Friedman model.

Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study).

The use of artificial intelligence (AI) has the potential to improve the assessment of lesions suspicious of melanoma, but few clinical studies have been conducted. We validated the accuracy of an open-source, non-commercial AI algorithm for melanoma diagnosis and assessed its potential impact on dermatologist decision-making. We conducted a prospective, observational clinical study to assess the diagnostic accuracy of the AI algorithm (ADAE) in predicting melanoma from dermoscopy skin lesion images. The primary aim was to assess the reliability of ADAE's sensitivity at a predefined threshold of 95%. Patients who had consented for a skin biopsy to exclude melanoma were eligible. Dermatologists also estimated the probability of melanoma and indicated management choices before and after real-time exposure to ADAE scores. All lesions underwent biopsy. Four hundred thirty-five participants were enrolled and contributed 603 lesions (95 melanomas). Participants had a mean age of 59 years, 54% were female, and 96% were White individuals. At the predetermined 95% sensitivity threshold, ADAE had a sensitivity of 96.8% (95% CI: 91.1-98.9%) and specificity of 37.4% (95% CI: 33.3-41.7%). The dermatologists' ability to assess melanoma risk significantly improved after ADAE exposure (AUC 0.7798 vs. 0.8161, p = 0.042). Post-ADAE dermatologist decisions also had equivalent or higher net benefit compared to biopsying all lesions. We validated the accuracy of an open-source melanoma AI algorithm and showed its theoretical potential for improving dermatology experts' ability to evaluate lesions suspicious of melanoma. Larger randomized trials are needed to fully evaluate the potential of adopting this AI algorithm into clinical workflows.

Are the MIA and MSKCC nomograms useful in selecting patients with melanoma for sentinel lymph node biopsy?

BACKGROUND AND METHODS: The Melanoma Institute of Australia (MIA) and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms were developed to help guide sentinel lymph node biopsy (SLNB) decisions. Although statistically validated, whether these prediction models provide clinical benefit at National Comprehensive Cancer Network guideline-endorsed thresholds is unknown. We conducted a net benefit analysis to quantify the clinical utility of these nomograms at risk thresholds of 5%-10% compared to the alternative strategy of biopsying all patients. External validation data for MIA and MSKCC nomograms were extracted from respective published studies. RESULTS: The MIA nomogram provided added net benefit at a risk threshold of 9% but net harm at 5%-8% and 10%. The MSKCC nomogram provided added net benefit at risk thresholds of 5% and 9%-10% but net harm at 6%-8%. When present, the magnitude of net benefit was small (1-3 net avoidable biopsies per 100 patients). CONCLUSION: Neither model consistently provided added net benefit compared to performing SLNB for all patients. DISCUSSION: Based on published data, use of the MIA or MSKCC nomograms as decision-making tools for SLNB at risk thresholds of 5%-10% does not clearly provide clinical benefit to patients.

Clinical applications of artificial intelligence in urologic oncology.

PURPOSE OF REVIEW: This review aims to shed light on recent applications of artificial intelligence in urologic oncology. RECENT FINDINGS: Artificial intelligence algorithms harness the wealth of patient data to assist in diagnosing, staging, treating, and monitoring genitourinary malignancies. Successful applications of artificial intelligence in urologic oncology include interpreting diagnostic imaging, pathology, and genomic annotations. Many of these algorithms, however, lack external validity and can only provide predictions based on one type of dataset. SUMMARY: Future applications of artificial intelligence will need to incorporate several forms of data in order to truly make headway in urologic oncology. Researchers must actively ensure future artificial intelligence developments encompass the entire prospective patient population.