Calcipotriene and Betamethasone Dipropionate PAD-Cream Demonstrates Greater Treatment Efficacy in Patients with Moderate-to-Severe Psoriasis Compared to Topical Suspension/Gel: A Subgroup Analysis of Two Phase 3 Studies.

INTRODUCTION: Psoriasis ranges from mild to severe with the majority of patients having mild disease. Mild to moderate disease is often treated with topical therapies while photo-, oral, and biologic therapies are generally reserved for moderate-to-severe disease. There is a strong scientific rationale for the combination of calcipotriene (CAL) and betamethasone dipropionate (BDP) with respect to mode of action, efficacy, and safety and CAL/BDP has shown an inhibitory effect on key pathogenic cytokines in psoriasis including tumor necrosis factor-α, interleukin (IL)-17, and IL-23. METHODS: The objective of this pooled post hoc analysis is to investigate the efficacy of CAL/BDP polyaphron dispersion (PAD)-cream in subgroups of patients with moderate-to-severe psoriasis from two completed phase 3 studies conducted in the USA and Europe. RESULTS: The proportion of patients achieving Physician Global Assessment (PGA) treatment success as well as a modified Psoriasis Area and Severity Index (mPASI)75 response was higher in the subgroup with a body surface area > 10% and mPASI > 10 and Dermatology Life Quality Index > 10 at baseline compared to the overall patient population. Furthermore, the numerical difference in treatment efficacy between CAL/BDP PAD-cream and CAL/BDP topical suspension/gel increased in patient subgroups with higher baseline severity. Similar patterns were shown for the patient-reported outcomes. CONCLUSION: In this subgroup analysis, patients who had higher disease severity at baseline achieved greater efficacy than the total patient population when treated with 8 weeks of CAL/BDP PAD-cream as compared to a currently marketed active comparator. Additionally, as indicated by this analysis, CAL/BDP PAD-cream treatment may also be more convenient and less greasy, which may reduce the burden of daily treatment and improve adherence to therapy. TRIAL REGISTRATION: NCT03308799 and NCT03802344.

Sociodemographic Variables Reporting in Human Radiology Artificial Intelligence Research.

PURPOSE: Artificial intelligence (AI) is rapidly reshaping how radiology is practiced. Its susceptibility to biases, however, is a primary concern as more AI algorithms become available for widespread use. So far, there has been limited evaluation of how sociodemographic variables are reported in radiology AI research. This study aims to evaluate the presence and extent of sociodemographic reporting in human subjects radiology AI original research. METHODS: All human subjects original radiology AI articles published from January to December 2020 in the top six US radiology journals, as determined by impact factor, were reviewed. Reporting of any sociodemographic variables (age, gender, and race or ethnicity) as well as any sociodemographic-based results were extracted. RESULTS: Of the 160 included articles, 54% reported at least one sociodemographic variable, 53% reported age, 47% gender, and 4% race or ethnicity. Six percent reported any sociodemographic-based results. There was significant variation in reporting of at least one sociodemographic variable by journal, ranging from 33% to 100%. CONCLUSIONS: Reporting of sociodemographic variables in human subjects original radiology AI research remains poor, putting the results and subsequent algorithms at increased risk of biases.

Machine learning of electrophysiological signals for the prediction of ventricular arrhythmias: systematic review and examination of heterogeneity between studies.

BACKGROUND: Ventricular arrhythmia (VA) precipitating sudden cardiac arrest (SCD) is among the most frequent causes of death and pose a high burden on public health systems worldwide. The increasing availability of electrophysiological signals collected through conventional methods (e.g. electrocardiography (ECG)) and digital health technologies (e.g. wearable devices) in combination with novel predictive analytics using machine learning (ML) and deep learning (DL) hold potential for personalised predictions of arrhythmic events. METHODS: This systematic review and exploratory meta-analysis assesses the state-of-the-art of ML/DL models of electrophysiological signals for personalised prediction of malignant VA or SCD, and studies potential causes of bias (PROSPERO, reference: CRD42021283464). Five electronic databases were searched to identify eligible studies. Pooled estimates of the diagnostic odds ratio (DOR) and summary area under the curve (AUROC) were calculated. Meta-analyses were performed separately for studies using publicly available, ad-hoc datasets, versus targeted clinical data acquisition. Studies were scored on risk of bias by the PROBAST tool. FINDINGS: 2194 studies were identified of which 46 were included in the systematic review and 32 in the meta-analysis. Pooling of individual models demonstrated a summary AUROC of 0.856 (95% CI 0.755-0.909) for short-term (time-to-event up to 72 h) prediction and AUROC of 0.876 (95% CI 0.642-0.980) for long-term prediction (time-to-event up to years). While models developed on ad-hoc sets had higher pooled performance (AUROC 0.919, 95% CI 0.867-0.952), they had a high risk of bias related to the re-use and overlap of small ad-hoc datasets, choices of ML tool and a lack of external model validation. INTERPRETATION: ML and DL models appear to accurately predict malignant VA and SCD. However, wide heterogeneity between studies, in part due to small ad-hoc datasets and choice of ML model, may reduce the ability to generalise and should be addressed in future studies. FUNDING: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.

Artificial intelligence has become a ubiquitous term in radiology over the past several years, and much attention has been given to applications that aid radiologists in the detection of abnormalities and diagnosis of diseases. However, there are many potential applications related to radiologic image quality, safety, and workflow improvements that present equal, if not greater, value propositions to radiology practices, insurance companies, and hospital systems. This review focuses on six major categories for artificial intelligence applications: study selection and protocoling, image acquisition, worklist prioritization, study reporting, business applications, and resident education. All of these categories can substantially affect different aspects of radiology practices and workflows. Each of these categories has different value propositions in terms of whether they could be used to increase efficiency, improve patient safety, increase revenue, or save costs. Each application is covered in depth in the context of both current and future areas of work. Keywords: Use of AI in Education, Application Domain, Supervised Learning, Safety © RSNA, 2022.

Leveraging Social Media and Web Presence to Discuss and Promote Diversity, Equity, and Inclusion in Radiology.

PURPOSE: This article seeks to better understand how radiology residency programs leverage their social media presences during the 2020 National Residency Match Program (NRMP) application cycle to engage with students and promote diversity, equity, and inclusion to prospective residency applicants. METHODS: We used publicly available information to determine how broad a presence radiology programs have across specific platforms (Twitter [Twitter, Inc, San Francisco, California], Facebook [Facebook, Inc, Menlo Park, California], Instagram [Facebook, Inc], and website pages) as well as what strategies these programs use to promote diversity, equity, and inclusion. RESULTS: During the 2020 NRMP application cycle, radiology residency programs substantially increased their social media presence across the platforms we examined. We determined that 29.3% (39 of 133), 58.9% (43 of 73), and 29.55% (13 of 44) of programs used Twitter, Instagram, and Facebook, respectively; these accounts were established after an April 1, 2020, advisory statement from the NRMP. Program size and university affiliation were correlated with the degree of social media presence. Those programs using social media to promote diversity, equity, and inclusion used a broad but similar approach across programs and platforms. CONCLUSION: The events of 2020 expedited the growth of social media among radiology residency programs, which subsequently ushered in a new medium for conversations about representation in medicine. However, the effectiveness of this medium to promote meaningful expansion of diversity, equity, and inclusion in the field of radiology remains to be seen.

Artificial Intelligence in Quality Improvement: Reviewing Uses of Artificial Intelligence in Noninterpretative Processes from Clinical Decision Support to Education and Feedback.

The radiology workflow can be segmented into three large groups: pre-interpretative processes, interpretation, and postinterpretative processes. Each stage of this workflow represents quality improvement opportunities for artificial intelligence and machine learning. Although the focus of recent research has been targeted toward optimization of image interpretation, this article describes significant use cases for artificial intelligence in both the pre-interpretative and postinterpretative aspects of radiology. We provide examples of how current applications of AI for quality improvement purposes across the radiology workflow have been implemented and how further integration of these technologies can significantly improve clinical efficiency, reduce radiologist work burden, and ultimately optimize patient care and outcomes.

Composite hemangioendothelioma: An unusual presentation of a rare vascular tumor.

OBJECTIVES: Composite hemangioendothelioma (CHE) has been recently recognized as a low- to intermediate-grade vascular tumor. CHEs are rare vascular tumors that are clinically similar to more common vascular tumors but histologically exhibit a composite of hemangioendothelioma variants. We report the first case of a CHE on the scalp and the fifth case to show findings supportive of regional metastasis. METHODS: Our patient had a multilobulated, violaceous scalp nodule, which on histologic examination revealed epithelioid, retiform, and spindle-cell components and rare foci of intermediate-grade mitotic activity consistent with CHE. RESULTS: Imaging studies were performed and revealed an abnormal uptake of contrast media in a posterior neck nodule that, when examined via fine-needle aspiration, revealed clumps of atypical cells. CONCLUSIONS: This unique case presentation is representative of the variability seen in the presentation of CHE and highlights the importance of considering CHE on the clinical and histologic differential of vascular tumors.