Recent incidence patterns of cutaneous T-cell lymphoma (CTCL) in the US are not well described. We sought to describe recent incidence trends by tumor subtype, sex, age, race and ethnicity, socioeconomic status (SES), and geography.Methods | Incidence data were derived from 18 populationbased registries of the Surveillance, Epidemiology, and End Results (SEER) Program for 2000 to 2018. We included histologically confirmed cases of first primary CTCL with malignant behavior and primary site of involvement skin (C44.0-C44.9). Patient characteristics included sex, age, race and ethnicity, and geographic region (metropolitan vs nonmetropolitan counties). 1 Area-level SES information was available from 2000 to 2016 and was categorized into quintile; Q1 lowest, Q5 highest. 2 We used the 2000 US standard population to calculate age-adjusted annual incidence rates (IR) per million people. Annual percent change (APC) was calculated by using the weighted least squares method. Statistical calculations used SEER*Stat statistical software (version 8.3.9.2) in January 2022. The Stanford institutional review board deemed this study exempt from review and waived the requirement for patient informed consent because only deidentified data were used.Results | We identified 14 942 new cases of CTCL from 2000 to 2018. Table 1 shows the number of cases, IR, and APC of IR of CTCL by International Statistical Classification of Diseases and Related Health Problems for Oncology (ICD-O) diagnosis. Mycosis fungoides (MF) was the most common diagnosis, followed by primary CTCL (PCTCL) and primary cutaneous ana-
Pyoderma gangrenosum is often associated with a systemic disease. Cocaine-induced pyoderma gangrenosum, most probably caused by levamisole, has been described recently and typically presents as multiple, large cribriform ulcers. Peri-nuclear antineutrophil cytoplasmic antibody is the most common serological finding. A strong counseling for cocaine cessation, combined with wound care and immunosuppressive therapy, is the mainstay of treatment. We present two cases of cocaine-induced pyoderma gangrenosum and correlate their findings with the typical clinical, histological and serological presentation.
Despite the proliferation and clinical deployment of artificial intelligence (AI)-based medical software devices, most remain black boxes that are uninterpretable to key stakeholders including patients, physicians, and even the developers of the devices. Here, we present a general model auditing framework that combines insights from medical experts with a highly expressive form of explainable AI that leverages generative models, to understand the reasoning processes of AI devices. We then apply this framework to generate the first thorough, medically interpretable picture of the reasoning processes of machine-learning–based medical image AI. In our synergistic framework, a generative model first renders "counterfactual" medical images, which in essence visually represent the reasoning process of a medical AI device, and then physicians translate these counterfactual images to medically meaningful features. As our use case, we audit five high-profile AI devices in dermatology, an area of particular interest since dermatology AI devices are beginning to achieve deployment globally. We reveal how dermatology AI devices rely both on features used by human dermatologists, such as lesional pigmentation patterns, as well as multiple, previously unreported, potentially undesirable features, such as background skin texture and image color balance. Our study also sets a precedent for the rigorous application of explainable AI to understand AI in any specialized domain and provides a means for practitioners, clinicians, and regulators to uncloak AI's powerful but previously enigmatic reasoning processes in a medically understandable way.
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