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To evaluate the development of intra- and post-operative retinal breaks after pars plana vitrectomy (PPV) for macular hole (MH) and/or vitreomacular traction (VMT). Medical records of patients who underwent PPV at Kellogg Eye Center between 1/1/2005-6/30/2018, were evaluated in three groups: group 1, MH/VMT (n = 136); group 2, epiretinal membrane (ERM) without VMT (n = 270); and group 3, diagnostic vitrectomy (DV) or vitreous opacities (n = 35). Statistical analyses were conducted using SAS. 20.6% of patients with MH/VMT, 8.5% of patients with ERM, and 5.7% of patients with DV or vitreous opacities had either intra-operative or post-operative breaks. Indication of MH/VMT versus ERM was a significant predictor for this outcome (p = .0112). The incidence of retinal breaks was higher in operations using 23-gauge versus 25-gauge PPV (25.0% vs. 7.4%, p < .0001). The presence of MH and/or VMT is a significant risk factor for retinal breaks from PPV, as is use of 23-gauge vitrectomy.

Large cell transformation (LCT) of Sezary Syndrome (SS) is a rare phenomenon. To date, there are no rigorous studies identifying risk factors for its development. Here, we seek to characterize the clinicopathologic risk factors that predispose patients with SS to develop LCT. We retrospectively evaluated all SS patient records available in the Michigan Medicine Cancer Registry from 2010-2021. Clinical and pathologic variables were compared between groups. The Kaplan-Meier method and log-rank test were used to assess overall survival. Of 28 SS patients identified, eight patients experienced LCT, and 20 did not (NLCT). Peak lactate dehydrogenase (LDH) before LCT (p = 0.0012), maximum total body surface area (TBSA) involvement before LCT (p = 0.0114), absolute CD8+ cell count measured on flow cytometry at diagnosis of SS (p = 0.0455) and at the most recent blood draw (p = 0.00736), and ulceration on biopsy (p = 0.0034) were significant clinicopathologic variables identified between the SS patients that developed LCT versus those that did not. Maximum TBSA involvement, peak LDH, presence of ulceration, and decreased levels of CD8+ cells in the peripheral blood may predict the development of LCT in patients with SS.

Background/Objectives: Demand for dermatologic services exceeds specialist capacity, with average wait times of 26–50 days in the United States. Current triage methods rely on subjective judgment or disease-specific indices that do not generalize across diagnoses or translate to operational decisions. We developed and validated the Dermatologic Complexity Score (DCS), a standardized instrument to guide case prioritization across dermatology care settings and evaluate DCS as a workload-reduction filter, enabling safe delegation of approximately half of routine teledermatology cases (DCS ≤ 40) away from specialist review. Methods: We conducted a prospective validation study of the DCS using 100 consecutive teledermatology cases spanning 30 common conditions. The DCS decomposes complexity into five domains (Diagnostic, Treatment, Risk, Patient Complexity, Monitoring) summed to a 0–100 total with prespecified bands: ≤40 (low) (41–70), (moderate) (71–89), (high), ≥90 (extreme). Five board-certified dermatologists and an automated module independently scored all cases. Two primary care physicians completed all ≤40 cases to assess feasibility. Primary outcomes were interrater reliability using ICC (2,1) and agreement with automation. Secondary outcomes included time-to-decision, referral rates, and primary care feasibility. Results: Mean patient age was 46.2 years; 47% of cases scored ≤40, 33% scored 41–70, 18% scored 71–89, and 2% scored ≥90. Interrater reliability was excellent (ICC (1,2)) = 0.979; 95% CI 0.974–0.983), with near-perfect agreement between automated and mean dermatologist scores (r = 0.998). Time-to-decision increased monotonically across DCS bands from 2.11 min (≤40) to 5 (90) min (≥90) (p = 1.36 × 10−14). Referral rates were 0% for ≤40, 3% for 41–70, 27.8% for 71–89, and 100% for ≥90 cases. DCS strongly predicted referral decisions (AUC = 0.919). Primary care physicians successfully managed all ≤40 cases but required 6–8 additional minutes per case compared to dermatologists. Conclusions: The DCS demonstrates excellent reliability and strong construct validity, mapping systematically to clinically relevant outcomes, including decision time and referral patterns. The instrument enables standardized, reproducible triage decisions that can optimize resource allocation across teledermatology, clinic, procedural, and inpatient settings. Implementation could improve access to dermatologic care by supporting appropriate delegation of low-complexity cases to primary care while ensuring timely specialist evaluation for high-complexity conditions.

Background: Artificial intelligence (AI) has shown significant promise in augmenting diagnostic capabilities across medical specialties. Recent advancements in generative AI allow for synthesis and interpretation of complex clinical data including imaging and patient history to assess disease risk. Objective: To evaluate the diagnostic performance of a dermatology-trained multimodal large language model (DermFlow, Delaware, USA) in assessing malignancy risk of pigmented skin lesions. Methods: This retrospective study utilized data from 59 patients with 68 biopsy-proven pigmented skin lesions seen at Indiana University clinics from February 2023 to May 2025. De-identified patient histories and clinical images were input into DermFlow, and clinical images only were input into Claude Sonnet 4 (Claude) to generate differential diagnoses. Clinician pre-operative diagnoses were extracted from the clinical note. Assessments were compared to histopathologic diagnoses (gold standard). Results: Among 68 clinically concerning pigmented lesions, DermFlow achieved 47.1% top diagnosis accuracy and 92.6% any-diagnosis accuracy, with F1 = 0.948, sensitivity 93.9%, and specificity 89.5% (balanced accuracy 91.7%). Claude had 8.8% top diagnosis and 73.5% any-diagnosis accuracy, F1 = 0.816, sensitivity 81.6%, specificity 52.6% (balanced accuracy 67.1%). Clinicians achieved 38.2% top diagnosis and 72.1% any-diagnosis accuracy, F1 = 0.776, sensitivity 67.3%, specificity 84.2% (balanced accuracy 75.8%). DermFlow recommended biopsy in 95.6% of cases vs. 82.4% for Claude, with multiple pairwise differences favoring DermFlow (p < 0.05). Conclusions: DermFlow demonstrated comparable or superior diagnostic performance to clinicians and superior performance to Claude in evaluating pigmented skin lesions. Although additional data must be gathered to further validate the model in real clinical settings, these initial findings suggest potential utility for dermatology-trained AI models in clinical practice, particularly in settings with limited dermatologist availability.

PURPOSE To report the technical successes, adverse events, and long-term stent patency rates of Gianturco Z-stents for management of chronic central venous occlusive disease. METHODS Overall, 137 patients, with mean age 48.6±16.1 years (range, 16-89 years), underwent placement of Gianturco Z-stents for chronic central venous occlusions. Presenting symptoms included lower extremity edema (n=66, 48.2%), superior vena cava syndrome (n=30, 21.9%), unilateral upper extremity swelling (n=20, 14.6%), hemodialysis fistula or catheter dysfunction (n=11, 8.0%), ascites (n=8, 5.8%), and both ascites and lower extremity edema (n=2, 1.5%). Most common etiologies of central venous occlusion were prior central venous access placement (n=58, 42.3%), extrinsic compression (n=29, 21.2%), and post-surgical anastomotic stenosis (n=27, 19.7%). Number of stents placed, stent implantation location, stent sizes, technical successes, adverse events, need for re-intervention, follow-up evaluation, stent patencies, and mortality were recorded. Technical success was defined as recanalization and stent reconstruction with restoration of in-line venous flow. Adverse events were defined by the Society of Interventional Radiology Adverse Event Classification criteria. Primary and primary-assisted stent patencies were analyzed using Kaplan-Meier analysis. RESULTS In total, 208 Z-stents were placed. The three most common placement sites were the inferior vena cava (n=124, 59.6%), superior vena cava (n=44, 21.2%), and brachiocephalic veins (n=27, 13.0%). Technical success was achieved in 133 patients (97.1%). There were two (1.5%) severe adverse events (two cases of stent migration to the right atrium), one (0.7%) moderate adverse event, and one (0.7%) mild adverse event. Mean follow-up was 43.6±52.7 months. Estimated 1-, 3-, and 5-year primary stent patency was 84.2%, 84.2%, and 82.1%, respectively. Estimated 1-, 3-, and 5-year primary-assisted patency was 92.3%, 89.6%, and 89.6%, respectively. The 30- and 60- day mortality rates were 2.9% (n=4) and 5.1% (n=7), none of which were directly attributable to Z-stent placement. CONCLUSION Gianturco Z-stent placement is safe and effective for the treatment for chronic central venous occlusive disease with durable short- and long-term patencies.

Background and Objectives: Shortages of dermatologists create significant barriers to care, particularly for inflammatory and history-dependent conditions where image-only artificial intelligence (AI) classifiers have limited applicability. Current teledermatology solutions largely focus on single-task, morphology-based neoplasm classifiers, leaving the vast majority of dermatologic presentations underserved. This study evaluated the diagnostic accuracy and management plan quality of Dermflow (Prava Medical, Delaware, USA), a proprietary dermatology-focused Multimodal Image-and-Data Assistant (MIDA) that autonomously gathers dermatology-specific history, integrates data with patient-submitted images, and outputs structured differential diagnoses and management summaries. Materials and Methods: Two AI systems, Dermflow and Claude Sonnet 4 (Claude, a leading vision–language model), analyzed 87 clinical images from the Skin Condition Image Network and Diverse Dermatology Images databases, representing 10 inflammatory dermatoses and 9 neoplastic conditions stratified across Fitzpatrick Skin Tone (FST) categories (I–II, III–IV, V–VI). For the diagnostic comparison, Dermflow received images and autonomously gathered clinical history, while Claude received identical images without history. For the management plan comparison, both systems received the correct diagnosis and the clinical histories gathered by Dermflow. The primary outcome was diagnostic accuracy. The secondary outcome was management plan quality, assessed by two blinded dermatologists across eight clinical dimensions using 5-point Likert scales. Chi-square tests compared diagnostic accuracy between models; t-tests and ANOVA compared management quality scores. Results: Dermflow achieved markedly superior diagnostic accuracy compared to Claude (86.2% vs. 24.1%, p < 0.001). Both models maintained consistent diagnostic performance across FST categories without significant within-model differences (Dermflow p = 0.924; Claude p = 0.828). Management plan quality showed no significant overall differences between models. However, composite management quality scores declined significantly for darker skin tones across both systems: Dermflow scored 4.20 (FST I–II), 3.99 (FST III–IV), and 3.47 (FST V–VI); Claude scored 4.35, 3.97, and 3.44, respectively (p < 0.001 for most pairwise FST comparisons within each model). Conclusions: Multimodal AI integrating targeted history with image analysis achieves substantially higher diagnostic accuracy than image-only approaches across both inflammatory and neoplastic dermatologic conditions. Autonomous history gathering addresses fundamental limitations of morphology-only classifiers and enables scalable, patient-facing triage across the full spectrum of dermatologic disease. However, both models demonstrated reduced management plan quality for darker skin tones despite receiving the correct diagnosis, suggesting persistent training data limitations that require targeted bias-mitigation strategies beyond domain-specific instruction.

Skin cutaneous melanoma is characterized by significant heterogeneity in its molecular, genomic and immunologic features. Whole transcriptome RNA sequencing data from The Cancer Genome Atlas of skin cutaneous melanoma (n = 328) was utilized. CIBERSORT was used to identify immune cell type composition, on which unsupervised hierarchical clustering was performed. Analysis of overall survival was performed using Kaplan–Meier estimates and multivariate Cox regression analyses. Membership in the lymphocyte:monocyte , monocyte  and M0 cluster was an independently poor prognostic factor for survival (HR: 3.03; 95% CI: 1.12–8.20; p = 0.029) and correlated with decreased predicted response to immune checkpoint blockade. In conclusion, an M0-macrophage-enriched, lymphocyte-to-monocyte-ratio-low phenotype in the primary melanoma tumor site independently characterizes an aggressive phenotype that may differentially respond to treatment.

Background: Alopecia areata (AA), an autoimmune condition causing non-scarring hair loss, often coexists with atopic dermatitis (AD) due to shared T-helper cell type 2 (Th2)-mediated pathways. Dupilumab, a monoclonal antibody inhibiting IL-4 and IL-13 signaling, is a cornerstone treatment for AD but has conflicting reports regarding its impact on AA, with some suggesting therapeutic benefits and others indicating AA induction. Methods: This retrospective study, utilizing the TriNetX Research Network’s de-identified data from over 300 million patient records, investigates the association between dupilumab use and AA risk in AD patients. Results: After propensity score matching, 23,782 dupilumab users were compared with an equal number of controls. Results revealed a statistically significant increased AA risk in dupilumab users (odds ratio: 1.436, 95% CI: 1.066–1.935, p = 0.0167) after 16 weeks. Cases occurring within 16 weeks were excluded. Conclusions: Potential mechanisms include immune rebalancing, with Th2 suppression possibly upregulating Th1/Th17 pathways or unmasking latent AA in predisposed individuals. These findings challenge dupilumab’s potential as an AA treatment and highlight the need for vigilant monitoring, including routine scalp examinations and patient education. Future research should focus on mechanistic pathways, risk stratification, and comparative studies with other biologics to optimize personalized treatment strategies for AD and AA.

Merkel cell carcinoma (MCC) is a neuroendocrine skin cancer with a high rate of mortality. While still relatively rare, the incidence of MCC has been rapidly rising in the US and around the world. Since 2017, two immunotherapeutic drugs, avelumab and pembrolizumab, have been FDA-approved for the treatment of metastatic MCC and have revolutionized outcomes for MCC. However, real-world outcomes can differ from clinical trial data, and the adoption of novel therapeutics can be gradual. We aimed to characterize the treatment practices and outcomes of patients with metastatic MCC across the US. A retrospective cohort study of adult cases of MCC in the National Cancer Database diagnosed from 2004 to 2019 was performed. Multivariable logistic regressions to determine the association of a variety of patient, tumor, and system factors with likelihood of receipt of systemic therapies were performed. Univariate Kaplan–Meier and multivariable Cox survival regressions were performed. We identified 1017 cases of metastatic MCC. From 2017 to 2019, 54.2% of patients received immunotherapy. This increased from 45.1% in 2017 to 63.0% in 2019. High-volume centers were significantly more likely to use immunotherapy (odds ratio 3.235, p = 0.002). On univariate analysis, patients receiving systemic immunotherapy had significantly improved overall survival (p < 0.001). One-, 3-, and 5-year survival was 47.2% (standard error [SE] 1.8%), 21.8% (SE 1.5%), and 16.5% (SE 1.4%), respectively, for patients who did not receive immunotherapy versus 62.7% (SE 3.5%), 34.4% (SE 3.9%), and 23.6% (SE 4.4%), respectively, for those who did (Fig. 1). In our multivariable survival regression, receipt of immunotherapy was associated with an approximately 35% reduction in hazard of death (hazard ratio 0.665, p  < 0.001; 95% CI 0.548–0.808). Our results demonstrate that the real-world survival advantage of immunotherapy for metastatic MCC is similar to clinical trial data. However, many patients with metastatic disease did not receive this guideline-recommended therapy in our most recent study year, and use of immunotherapy is higher at high-volume centers. This suggests that regionalization of care to high-volume centers or dissemination of their practices, may ultimately improve patient survival.

Abstract BACKGROUND Spine stereotactic body radiotherapy (sSBRT) is commonly limited to 1 or 2 vertebral levels given a paucity of efficacy and toxicity data when more than 2 levels are treated. OBJECTIVE To prove our hypothesis that multilevel sSBRT could provide similar rates of local control (LC) (primary endpoint) and toxicity as single-level treatment using the same clinical target, planning target, and planning organ-at-risk volumes. METHODS We analyzed consecutive cases of sSBRT treated from 2013 to 2017. Time-to-event outcomes for single-level and multilevel cases were compared using mixed effect Cox models and differences in toxicity rates were evaluated using linear mixed effect models. All models incorporate a patient-level random intercept to account for any within-patient correlation across cases. RESULTS There were 101 single-level and 84 multilevel sSBRT cases (2-7 continuous vertebral levels). One-year LC was 95% vs 85%, respectively. After adjusting for baseline covariates, dose delivered, and accounting for within-patient correlation, there was no significant difference in time to local failure (hazard ratio, HR 1.79 [0.59-5.4]; P = .30). Pain improved in 83.5% of the 139 initially symptomatic tumors. There were no significant differences in grade 2+ acute or late toxicities between single-level and multilevel sSBRT. CONCLUSION With rigorous patient immobilization, quality assurance, and image guidance, multilevel sSBRT provides high rates of LC, similar to single-level treatment, without need for larger planning volume margins. Efforts to improve prognostication and case selection for multilevel sSBRT are warranted to ensure that the benefits of improved LC over palliative radiation are justified.