Citations

Objective: To examine the benefit of cochlear implantation (CI) in pediatric patients with a history of temporal bone fracture resulting in single-sided deafness (SSD). Study design: Retrospective chart review. Setting: Two tertiary academic centers. Patients: Pediatric patients less than 18 years of age with SSD secondary to temporal bone fracture who underwent CI from 2012 to 2022. Intervention(s): CI. Main outcome measure(s): Postoperative audiometric testing. Results: Seven subjects were identified (median age: 10 years (y); Q1 to Q3: 5 to 16 y). Median time from temporal bone trauma to CI was 7 months (mo) (Q1 to Q3: 3 to 7 mo) and 85% had otic capsule involvement. Subjects were followed for a median length of 12 months (Q1 to Q3: 6 to 24 mo) after CI. All patients achieved full electrode insertion, though 2 required scala vestibuli insertion due to ossification of the scala tympani (one of whom had a history of meningitis after temporal bone fracture). Postoperative audiometric testing revealed variable speech perception outcomes with 4 of 7 obtaining open or closed set speech perception and 3 of 7 obtaining only sound awareness. No postoperative complications occurred. Conclusions: CI is a feasible intervention for SSD following pediatric temporal bone fractures, but outcomes are variable. Early evaluation and timely CI, ideally within 6 months of temporal bone fracture, may optimize outcomes. Cochlear ossification, either as a result of otic capsule fracture or subsequent meningitis, may lead to suboptimal outcomes.

Abstract Despite growing interest in AI for pancreatic cyst management, no prior study has systematically investigated how lesion characteristics influence model behavior or provided a comprehensive explainability analysis in IPMN malignancy risk stratification. We present the first multi-center study integrating explainable AI with uncertainty quantification to evaluate how cyst type, size, and location affect deep learning performance in IPMN malignancy prediction. Our retrospective study analyzed 170 IPMNs from seven centers using a radiomics-deep learning fusion model. Cases were stratified by dysplasia grade, IPMN type, size, and location; with model interpretability assessed using SHAP, LIME, Grad-CAM, and uncertainty quantification. Overall accuracy was 67.1%, with model uncertainty lower for correct versus incorrect predictions (0.72 vs 0.78, p < 0.001). On subgroup analysis: Low-Risk lesion accuracy exceeded High-Risk (81.3% vs. 42.9%, p < 0.001), BD accuracy was inversely correlated with cyst size while MD remained stable, and body and whole-pancreas cysts stratified more accurately than tail cysts (77.8%, 76.6% vs. 47.8%). AI models face significant, previously uncharacterized performance drops when evaluating complex high-risk IPMNs. Our study shows that integrating uncertainty quantification can successfully flag unreliable predictions, enabling a selective-prediction framework where clinicians can confidently rely on AI for low-risk, low-uncertainty lesions while deferring high-uncertainty cases to expert review.

Abstract Purpose Although the Woven EndoBridge (WEB) device is increasingly used for the treatment of wide-neck intracranial aneurysms, including in the acute rupture setting, comparative evidence assessing the impact of rupture status remains limited. This study compared angiographic, safety, and clinical outcomes between ruptured and unruptured intracranial aneurysms treated with WEB. Methods We conducted a retrospective analysis of prospectively collected data from the multicenter cohort registry WorldWideWEB, including consecutive adult patients with intracranial aneurysms treated with the WEB. Patients were stratified into groups of ruptured and unruptured aneurysms. Propensity score matching was used to balance baseline characteristics between both groups. Retreatment rate was the primary outcome. Secondary outcomes included mRS, safety events (thromboembolic complications) and angiographic outcomes (periprocedurally and last follow-up). Results Among 1,220 patients, 342 (28.0%) presented with ruptured aneurysms. There was no significant difference in the rate of thromboembolic complications between groups (11.8% vs 5.9%, p = 0.056). At follow-up, adequate occlusion was lower (82.2% vs 93.3%, p < 0.002) and functional outcomes were worse (mRS ≥2 in 34.1% vs 21.9%, p = 0.012) among patients with ruptured aneurysms. Periprocedural adequate occlusion (53.3% vs 53.8%, p>0.9) and retreatment rates (11.8% vs 7.1%, p = 0.14) did not differ between groups after matching. Conclusion Ruptured aneurysms demonstrated expected inferior follow-up functional and angiographic outcomes when compared with unruptured aneurysms, but no difference in retreatment rate and procedural safety. These findings support WEB as a safe and effective treatment option for appropriately selected ruptured intracranial aneurysms in routine clinical practice.

SUMMARY Goal: The purpose of this study was to enhance access to medication assistance programs (MAPs), which are crucial for providing free medications to patients who cannot afford them. These programs are particularly beneficial for the management of chronic conditions such as diabetes, heart failure, and cancer, for which medication adherence is vital for positive patient outcomes, and cost is a common barrier. Methods: This quality improvement project aimed at optimizing the MAP enrollment process. Interventions included the development of frequently asked questions documents, standardized templates for documentation, establishment of MAP technician office spaces, and standardization of patient referrals. A Mann-Whitney U test and a chi-square test were used to summarize the data in this study. Principal Findings: The project resulted in a 56% increase in new patient enrollments ( p < .01) and a 33% increase in medications provided, with a significant reduction in the average time from patient referral to application approval. Practical Applications: The project improved patient access to MAPs, optimized pharmacy technician resources, and significantly reduced processing times, an important factor in preventing treatment delays and improving patient care. Future plans include expanding the new enrollment process to annual reenrollments, formally establishing the MAP technician within departments, and considering additional pharmacy technician support in response to increasing demand for MAPs.

46 Background: Primary results from the randomized, multicenter, open-label, phase 3 TRITON3 (NCT02975934) study demonstrated that rucaparib significantly improved radiographic progression-free survival (rPFS) vs physician’s choice of DTX or an ARPI. ECOG PS is used as a predictor of prostate cancer survival. In this post hoc analysis, we report results from the BRCA1/2 subgroup of pts stratified by ECOG PS. Methods: Pts with disease progression after 1 prior second-generation ARPI in any setting were randomized 2:1 to rucaparib 600 mg BID or physician’s choice of DTX or ARPI (abiraterone acetate or enzalutamide). Primary endpoint was rPFS by independent radiology review (IRR). Objective response rate (ORR) for pts with measurable disease and overall survival (OS) were key secondary endpoints. Crossover from physician’s choice to rucaparib was allowed after radiographic progression was confirmed by IRR. Data cutoff was August 25, 2022. Results: Of 302 pts in the BRCA1/2 population, 147 had an ECOG status of 0 (97 rucaparib; 50 physician’s choice) and 155 had ECOG 1 (104 rucaparib; 51 physician’s choice). Baseline characteristics and demographics were generally similar between arms for pts in the ECOG 0 and ECOG 1 groups. Median rPFS favored rucaparib vs physician’s choice for pts with ECOG 0 (11.4 vs 8.2 months; HR, 0.48 [95% CI, 0.30-0.76]) and ECOG 1 (10.9 vs 6.1 months; HR, 0.48 [95% CI, 0.31-0.74]) (Table). Additionally, rPFS favored rucaparib for ECOG 0 and ECOG 1 including those whose prespecified physician’s choice was ARPI or docetaxel (Table). The confirmed ORR (95% CI) of rucaparib vs physician’s choice for ECOG 0 was 54% (37%–69%) vs 16% (3%–40%); the ORR for ECOG 1 was 37% (22%–53%) vs 18% (5%–40%), respectively. Median OS was similar between arms for rucaparib or physician’s choice; median OS was 23.7 vs 21.7 months (HR, 0.91 [95% CI, 0.61–1.37]) for ECOG 0 and 21.8 vs 20.4 months (HR, 0.85 [95% CI, 0.58–1.24]) for ECOG 1. Conclusions: Rucaparib improved rPFS and ORR in pts with BRCA-mutated prostate cancer with an ECOG PS of 0 or 1. OS was similar for these groups in both arms. These data support rucaparib as a treatment option for pts with BRCA-mutated mCRPC, whether ECOG 0 or 1. Clinical trial information: NCT02975934 . Median rPFS in patients in the ECOG 0 and ECOG 1 groups. Rucaparib, n Physician’s choice, n rPFS Rucaparib, median months rPFS Physician’s choice, median months HR (95% CI) rPFS, ECOG 0 97 50 11.4 8.2 0.48 (0.30–0.76) rPFS, ECOG 1 104 51 10.9 6.1 0.48 (0.31–0.74) rPFS, physician’s choice ARPI, ECOG 0 51 24 11.2 5.6 0.42 (0.22–0.78) rPFS, physician’s choice docetaxel, ECOG 0 46 26 11.5 8.8 0.49 (0.24–0.98) rPFS, physician’s choice ARPI, ECOG 1 52 17 11.2 4.5 0.33 (0.16–0.68) rPFS, physician’s choice docetaxel, ECOG 1 52 34 10.7 7.1 0.59 (0.34–1.01)

Objectives: To summarize recent high-quality trials and provide an update to the original 2022 American College of Radiology Appropriate Use Criteria for Treatment of Locoregional Gastric Adenocarcinoma. This multi-specialty-led committee included gastrointestinal radiation and medical oncology, gastroenterology, radiology, and surgical oncology. Methods: Using the population, intervention, comparator, outcome, timing, and study design framework, the evidence was assessed using Cochrane and PRISMA 2020 methodology. Eligible studies included prospective Phase II/II trials and select retrospective studies published between 5/1/2020 and 8/1/2025 in the Embase, Medline, and Pubmed databases. Study type and quality were assessed. Well-established RAND-UCLA consensus methodology (modified Delphi) was used to rate the appropriateness of the treatment options. Results: ARS AUC recommendations include adjuvant FLOT chemotherapy for localized gastric adenocarcinoma after neoadjuvant FLOT and surgery, poCT or perioperative chemoimmunotherapy for localized gastric adenocarcinoma, adjuvant chemotherapy +/− CRT for localized gastric adenocarcinoma that undergoes upfront surgery, and systemic therapy alone for localized gastric adenocarcinoma in a medically inoperable patient. Conclusions: This updated ARS AUC summary provides guidelines for the multimodality treatment of resectable gastric adenocarcinoma.

Abstract The adoption of electronic health records (EHRs) has transformed health care, improving efficiency and chart accessibility. However, the widespread reliance on unstructured data entry and the lack of standardized documentation frameworks have resulted in significant data fragmentation across health care systems. The prevalence of unstructured data in EHRs limits their potential for clinical decision support, trial matching, real‐world evidence (RWE) generation, and quality measurement. Data fragmentation in health care triggers a cascade of challenges that ultimately compromise patient care. Clinicians face an excessive documentation burden and struggle to locate critical information buried in unstructured notes. Researchers encounter difficulties in extracting reliable clinical data. EHR vendors grapple with standardizing unstructured information for interoperability, and payers are unable to process unstructured clinical data efficiently to support value‐based care models. These challenges are particularly acute in oncology, where complex clinical elements like cancer staging, disease status, and treatment changes require precise, structured documentation. Emerging artificial intelligence (AI) technologies, such as large language models (LLMs) and ambient listening, offer a path to automate structured data generation while reducing the workload on providers. Here, the authors propose LLM‐based workflows that balance automation with clinician verification, streamlining data entry without compromising accuracy. Realizing these benefits requires coordinated efforts among clinicians, researchers, EHR vendors, payers, and policymakers to align regulatory frameworks with AI‐driven innovations. This article outlines a strategy to enhance structured data capture within EHRs, ultimately improving patient care, research, and health care efficiency.