Concurrent Interstitial Granulomatous Dermatitis and TNF-α Antagonist-Induced Lupus Syndrome Secondary to Adalimumab for Crohn's Disease.

Tumor necrosis factor (TNF) antagonists have revolutionized management for various autoimmune and inflammatory conditions, including rheumatoid arthritis (RA), inflammatory bowel disease, hidradenitis suppurativa, psoriatic arthritis, and plaque psoriasis. However, adverse effects may necessitate switching to another biologic with a different mechanism of action. Here we report TNF-α antagonist-induced lupus syndrome (TAILS) revealed by interstitial granulomatous dermatitis (IGD), in the atypical context of Crohn's disease (CD).

Perceptions of Artificial Intelligence Integration into Dermatology Clinical Practice: A Cross-Sectional Survey Study.

BACKGROUND: Artificial intelligence (AI) is a growing field in dermatology and has great potential for integration into clinical practice. Our objective was to assess the perceptions of artificial intelligence in dermatology practice. METHODS: An IRB-approved 18-question online survey was distributed by email. Patients were stratified by age to assess for statistical differences among perceptions. RESULTS: 90 respondents fully completed the survey. 54 (60.0%) respondents were slightly familiar with AI, and 73 (81.1%) respondents have not incorporated AI into their clinical practice. 27.8% of respondents perceived AI as superior to a human provider’s experience some of the time. 94.4% of respondents would at least use AI for certain scenarios. 65.6% of respondents believed that AI would help patients with analyzing and managing electronic health records. 38.9% respondents predict that AI will not decrease or increase the need for dermatologists. 51.6% of respondents felt that AI will at least somewhat enhance the dermatologists’ ability to screen skin lesions. The three dermatology areas that AI was perceived to most beneficial were malignant skin lesions, benign skin lesions, and pigmentation disorders. Age of respondents did not have a significant impact on the perceptions of AI. CONCLUSION: Our results show that dermatologists surveyed were generally positive toward embracing AI integration into clinical practice. Further studies should be conducted to confirm these findings. J Drugs Dermatol. 2022;21(2):135-140. doi:10.36849/JDD.6398.

Treating Cutaneous Immune-Related Adverse Events from Immune Checkpoint Blockade.

Immune checkpoint inhibitors (ICPis) have revolutionized outcomes in various advanced malignancies. Therapeutic restoration of a robust T-cell response against malignant cells is also at the root of distinct cutaneous immune-related adverse events (cirAEs). As approved indications for ICPis increase and interdisciplinary collaboration with oncology grows, identifying the most common skin toxicities from ICPis, particularly on melanin-rich skin,1 and understanding treatment strategies are increasingly crucial for dermatologists. This brief review highlights common cirAEs and summarizes the latest evidence for interventions.

Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study.

OBJECTIVE: To predict the histologic grade and type of small papillary renal cell carcinomas (pRCCs) using texture analysis and machine learning algorithms. METHODS: This was a retrospective HIPAA-compliant study. 24 noncontrast (NC), 22 corticomedullary (CM) phase, and 24 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected pRCCs were identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade and type 1 or 2. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (20 gray-level co-occurrences and 11 gray-level run-lengths) features were calculated for each tumor in each phase. Feature values in low- versus high-grade and type 1 versus 2 pRCCs were compared. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of histologic grade and type of pRCCs in each phase. Histogram, texture, and combined histogram and texture feature sets were used to train and test three classification algorithms (support vector machine (SVM), random forest, and histogram-based gradient boosting decision tree (HGBDT)) with stratified shuffle splits and threefold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of histologic grade and type of pRCCs. RESULTS: Individual histogram and texture features did not have statistically significant differences between low- and high-grade or type 1 and type 2 pRCCs across all phases. Individual features had low predictive power for tumor grade or type in all phases (AUC < 0.70). HGBDT was highly accurate at predicting pRCC histologic grade and type using histogram, texture or combined histogram and texture feature data from the CM phase (AUCs = 0.97-1.0). All algorithms had highest AUCs using CM phase feature data sets; AUCs decreased using feature sets from NC or NG phases. CONCLUSIONS: The histologic grade and type of small pRCCs can be predicted with classification algorithms using CM histogram and texture features, which outperform NC and NG phase image data. The accurate prediction of pRCC histologic grade and type may be able to further guide management of patients with small (< 4 cm) pRCCs being considered for active surveillance.

Curcumin nanoparticles as a photoprotective adjuvant.

With rising skin cancer rates and interest in preventing photoaging, adjuvants for sunscreens are in high demand. The potential of curcumin has been posited due to its anti-inflammatory, antioxidant and wound healing properties. In prior studies, curcumin decreased UV-induced inflammation, apoptotic changes in human keratinocytes and dermal fibroblasts, and the expression of matrix metalloproteinases. However, curcumin's utility has been hindered by poor aqueous solubility and rapid degradation in vivo. To overcome these limitations, we synthesized curcumin nanoparticles (curc-np), which offer sustained topical delivery and enhanced bioavailability. Curc-np and controls were applied to the skin of BALB/c mice prior to UVB irradiation. Twenty-four hours later, mice pretreated with curc-np showed less erythema, induration and scale compared to controls. Histopathology showed fewer sunburn cells, and TUNEL assay indicated decreased apoptosis in curc-np treated mice. Immunohistochemistry illustrated less p53 expression in skin pretreated with curc-np. Furthermore, cytokine analysis revealed significantly less IL-6 and significantly greater anti-inflammatory IL-10 in skin of curc-np-treated mice as compared to controls. Taken together, our results reinforce curcumin's established anti-inflammatory effects in the skin and highlight its potential as a photoprotective adjuvant when delivered through nanoparticles. Further investigation alongside sunscreens against UV-induced damage is warranted.

Mobile MIM: a Portable Solution in Imaging.

Clinicians experience various situations, such as peer consultation or tumor boards, in which they need to access and view radiologic studies without a full radiologic workstation. Mobile MIM is a diagnostic medical imaging device on the Apple App Store that is FDA-approved and can meet the portable needs of clinicians. The goal of the app is to give physicians the opportunity to visualize scans from multiple modalities on a single iOS mobile device. The intended uses include reviewing complex aspects of radiation treatment plans and analyzing medical images from a wide array of modalities. The key limitation is incompatibility for mammography. The free app allows users to manipulate image sets from ten sample patients before an organization can commit to extensive systems integration with MIMcloud 2.0 to store, encrypt, and route image sets for real-time patients. This review explores the app's strengths and weaknesses through discussion of its features and usability.