Citations

ABSTRACT Hereditary hemorrhagic telangiectasia (HHT, Osler‐Weber‐Rendu disease) is the second most common inherited bleeding disorder worldwide, affecting approximately 1 in 5000 people. Development of disease‐modifying and efficacious hemostatic agents to treat HHT has finally begun after decades without such medical therapies. However, the lack of consensus on standardized severity definitions, outcome criteria, and terminology remains a major obstacle to clinical investigation and therapeutic development in HHT. Additionally, with the ongoing repurposing of antiangiogenic agents and emerging development of novel HHT‐specific therapies, comparative clinical trials are expected in the future. Therefore, to end the problematic heterogeneity hindering these efforts, the Global Research and Medical Advisory Board (GRMAB) of the Cure HHT Foundation, an international group of recognized HHT experts, convened a conference of expert HHT clinician‐investigators from within GRMAB as well as invited external experts in HHT and bleeding disorders generally, patients with HHT, and patient advocates to define standard terminology and definitions for outcomes and severity classification for bleeding and anemia in HHT. These criteria and definitions should be adopted by regulators, investigators, and the pharmaceutical industry in the development and performance of interventional clinical trials and cohort studies to allow improved comparability between clinical trials, facilitate communication between clinicians and investigators, improve therapeutic guideline development, and provide a standardized framework for regulatory agencies. Search Strategy and Selection Criteria Evidence for this report was systematically identified and evaluated utilizing two search strategies in Ovid MEDLINE, described in full detail in the Appendix, pp.4–13. The searches were conducted on January 7, 2025. The titles and abstracts of each record were reviewed, and the inclusion criteria were applied to all search results to identify full text articles to be retrieved for further review. All retrieved full texts were then reviewed to reach a final determination if a study met the inclusion criteria. Included references were then compiled into evidence tables, which were then utilized by the International Consensus Report Working Group throughout the development of report recommendations.

Introduction: We developed a computed tomography (CT)-based tool designed for automated segmentation of deltoid muscles, enabling quantification of radiomic features and muscle fatty infiltration. Prior to use in a clinical setting, this machine learning (ML)-based segmentation algorithm requires rigorous validation. The aim of this study is to conduct shoulder expert validation of a novel deltoid ML auto-segmentation and quantification tool. Materials and Methods: A SwinUnetR-based ML model trained on labeled CT scans is validated by three expert shoulder surgeons for 32 unique patients. The validation evaluates the quality of the auto-segmented deltoid images. Specifically, each of the three surgeons reviewed the auto-segmented masks relative to CT images, rated masks for clinical acceptance, and performed a correction on the ML-generated deltoid mask if the ML mask did not completely contain the full deltoid muscle, or if the ML mask included any tissue other than the deltoid. Non-inferiority of the ML model was assessed by comparing ML-generated to surgeon-corrected deltoid masks versus the inter-surgeon variation in metrics, such as volume and fatty infiltration. Results: The results of our expert shoulder surgeon validation demonstrates that 97% of ML-generated deltoid masks were clinically acceptable. Only two of the ML-generated deltoid masks required major corrections and only one was deemed clinically unacceptable. These corrections had little impact on the deltoid measurements, as the median error in the volume and fatty infiltration measurements was <1% between the ML-generated deltoid masks and the surgeon-corrected deltoid masks. The non-inferiority analysis demonstrates no significant difference between the ML-generated to surgeon-corrected masks relative to inter-surgeon variations. Conclusions: Shoulder expert validation of this CT image analysis tool demonstrates clinically acceptable performance for deltoid auto-segmentation, with no significant differences observed between deltoid image-based measurements derived from the ML generated masks and those corrected by surgeons. These findings suggest that this CT image analysis tool has potential to reliably quantify deltoid muscle size, shape, and quality. Incorporating these CT image-based measurements into the pre-operative planning process may facilitate more personalized treatment decision making, and help orthopedic surgeons make more evidence-based clinical decisions.

Abstract Background To utilize high spatial resolution reconstructions for cardiac imaging at energy‐integrating detector CT (EID)‐CT with comparable noise to similar reconstructions at photon‐counting detector (PCD)‐CT, methods to control EID‐CT image noise are needed. Supervised convolutional neural networks (CNN) have shown promise for denoising, but a challenge remains to efficiently create high‐quality and unbiased estimates of noise without access to dedicated software or proprietary information, such that natural noise texture is retained in CNN‐denoised CT images. Purpose This study aims to develop and test image‐based noise estimation methods that can be used to train a CNN model, and to evaluate denoising performance and noise texture preservation for EID‐CT coronary CT angiography (cCTA) images reconstructed with high‐resolution kernels. Methods U‐net CNN models were trained for denoising. To supervise training, noise‐only images were estimated directly from high‐resolution kernel (Bv59) reconstructed EID‐CT (HR EID‐CT) patient images using two different methods: subtraction of low‐ and high‐strength iterative reconstruction (IR); subtraction of adjacent image slices with the same IR strength. The noise estimates from these methods contain differing noise texture and anatomical information. Networks were trained and validated separately for three data sets: the training data from each of the two noise‐estimation methods, and a 50%–50% partition of training data between the two methods. The trained models were applied to two sets of testing data: CT images of a uniform water phantom to measure noise power spectra (NPS), and an independent cohort of seven patient cCTA HR EID‐CT exams. The denoised patient images were compared to standard resolution EID‐CT reconstructions (Bv40). As a low‐noise reference, patient images acquired on the same day with a PCD‐CT and reconstructed using a similar kernel as HR EID‐CT were used for comparison. Results Models trained with each noise‐image estimation method denoised the HR EID‐CT images by 74%–79% to achieve a comparable noise magnitude to the HR PCD‐CT images. The peak, average, and 10% peak frequencies of the NPS of the input images (6.08, 6.24, and 12.0 cm −1 ) were better approximated by the model trained on adjacent slice subtraction (6.56, 5.87, and 11.5 cm −1 ) than by the model trained on subtraction of low‐ and high‐IR images (4.64, 5.44, and 11.3 cm −1 ). In cCTA images, the IR subtraction model images retained anatomic structures from input images but resulted in undesirable salt‐and‐pepper noise texture and CT number bias. The model trained on adjacent slice subtraction images had more natural texture and no significant bias, but the model sometimes removed small anatomic structures. The model trained on the mixed training dataset preserved both noise texture and anatomy from the model inputs and enabled visualization of small structures seen in PCD‐CT images that were previously unresolved by EID‐CT. Conclusions The noise texture and anatomical accuracy in CT images denoised with an image‐based supervised CNN are greatly influenced by the characteristics and partitioning of training data. With higher‐resolution reconstructions and noise texture‐preserving deep learning denoising, the quality of cCTA images from EID‐CT can be enhanced to enable resolvability of subtle anatomy similar to PCD‐CT.

Introduction: Ureteral stents are widely used in the specialty of urology to preserve renal function and provide ureteral patency in cases of urolithiasis, strictures, malignancy, and trauma. This paper presents a novel application of prophylactic ureteral stents deployed under MRI-guidance for ureteral protection in the setting of in-bore salvage cryoablation therapy for recurrent and metastatic prostate cancer. This is the first known case series of ureteral stent placement using near real-time MRI. Materials and Methods: A retrospective chart review was performed for all patients who underwent MRI-guided ureteral stent placement prior to in-bore cryoablation therapy from 2021 to 2022. Each case was managed by an interdisciplinary team of urologists and interventional radiologists. Preoperative and postoperative data were collected for descriptive analysis. Physics safety testing was conducted on the cystoscope and viewing apparatus prior to its implementation for stent deployment. Results: A total of seven males, mean age 73.4 years (range 65–81), underwent successful prophylactic, cystoscopic MRI-guided ureteral stent placement prior to cryoablation therapy of their prostate cancer. No intraoperative complications occurred. A Grade 2 postoperative complication of pyelonephritis and gross hematuria following stent removal occurred in one case. The majority of patients were discharged the same day as their procedure. Conclusions: This case series demonstrates the feasibility of in-bore cystoscopic aided MRI guidance for ureteral stent placement. Ureteral stents can be used to increase the safety margin of complex cryoablation treatments close to the ureter. Furthermore, by following the meticulous MRI safety protocols established by MRI facility safety design guidelines, MRI conditional tools can aid therapy in the burgeoning interventional MRI space.

Introduction: Amyloidosis complicated by heart failure (HF) poses significant mortality. We sought to identify trends in comorbid amyloidosis and HF mortality in the recent 22- year period. Methods: Mortality due to amyloid and HF as contributors of death were queried from death certificates using the CDC database from 1999 to 2020. Mortality rates and their 95% confidence intervals were adjusted for age (AAMR) through the Direct method and compared by demographic subpopulations. The Monte-Carlo permutation test was used to estimate the annual percentage change (APC). Log-linear regression models were utilized to assess temporal variation in mortality. Results: Age-adjusted mortality rates (AAMR) increased from 0.09 [0.08-0.10] in 1999 to 0.27 [0.25-0.29] in 2020. Mortality increased from 1999 to 2013 (APC +1.4, p=0.048) with an accelerating inflection point in 2013 to 2020 (APC +13.3, p<0.001). AAMR was higher among male populations (AAMR 0.20 [0.20-0.21]) compared to female populations (AAMR 0.07 [0.07- 0.07]). A significant inflection point in uprising mortality rates was observed for both male and female populations in 2013 (p<0.001). Mortality was highest among Black populations (AAMR 0.33), followed by White (AAMR 0.10), Asian/Pacific Islander (AAMR 0.06), and American Indian/Alaska Native populations (AAMR 0.04). Among Black populations, mortality remained consistent from 1999 to 2012 (APC +1.1, p=0.184), followed by an increase from 2012 to 2020 (APC +14.0, p<0.001). Among White populations, mortality remained stagnant from 1999 to 2013 (APC +0.7, p=0.302), followed by an increase starting in 2013 to 2020 (APC +13.5, p<0.001). Discussion: Our findings of a marked rise in HF-related mortality in patients with amyloidosis since 2013 highlighted the profound impact of enhanced diagnostic awareness, novel imaging techniques, and emerging therapeutics. Our analysis also showed mortality disparities between sexes, and geographic locations, races, and ethnicity that warrant targeted public health interventions. Conclusions: Amyloid and HF mortality increased in the recent 22-year period, primarily starting in 2013, emphasizing the urgent need for targeted intervention to address these disparities.

Abstract INTRODUCTION Monoclonal anti‐amyloid therapies are now accessible, but how these treatments influence changes within the brain is still not clear. We investigated overall and regional change in amyloid removal, glucose metabolism, and atrophy in trial participants with dominantly inherited Alzheimer's disease (DIAD). METHODS In the DIAN‐TU‐001 trial, 92 carriers received gantenerumab or placebo and underwent serial neuroimaging assessments including [ 11 C]‐Pittsburgh compound‐B (PiB) positron emission tomography (PET), [ 18 F]‐fluoro‐2‐deoxyglucose (FDG) PET, and magnetic resonance imaging (MRI). RESULTS Gantenerumab significantly reduced PiB‐PET uptake overall and in most regions and showed no changes in FDG‐PET or MRI measures. Drug effects were associated with baseline PiB‐PET uptake, and the largest effects occurred in medial regions. DISCUSSION Treated DIAD participants, and especially those with higher amyloid burden, showed a decrease in PiB‐PET uptake, which was more pronounced in the basal ganglia and medial frontal structures. These results may inform patient response and future drug trial design. Highlights Gantenerumab unevenly decreased Aβ burden as measured by PiB‐PET across brain regions. The strongest decrease in PiB‐PET uptake was in basal ganglia and medial frontal structures. Variable drug effect on Aβ was partly due to the amount of burden present before treatment. There was no regional effect on FDG‐PET metabolism or MRI volumetrics after 4 years.

Abstract BACKGROUND First held in 2020, the Institute on Methods and Protocols for Advancement of Clinical Trials in ADRD (IMPACT‐AD) is a program to train the next generation of Alzheimer's disease (AD) and related dementias (AD/ADRD) clinical trialists. METHODS IMPACT‐AD includes didactic, workshop, and small group components. RESULTS IMPACT‐AD has trained 18 alumni–scholars (accepted from 424 applicants), of whom 67% were female. Forty‐eight (26%) were the first in their family to attend college. Scholars included individuals from all racial and ethnic groups. Each year, participants demonstrated increased learning about AD/ADRD clinical trials through a pre/post‐test model of knowledge assessments. Among those completing annual follow‐up surveys, >84% remain in AD/ADRD trials careers, and 80% have experienced career advances or milestones. In the latter group, > 90% indicated that participating in IMPACT‐AD contributed to this career growth. DISCUSSION IMPACT‐AD is a novel educational program that is achieving its goals. Highlights Over 5 years, the Institute on Methods and Protocols for Advancement of Clinical Trials in ADRD (IMPACT‐AD) has trained 188 scholars. The course has been accessible to a broad range of trainees who were diverse in professional and demographic backgrounds. Objective assessments indicate that participants gain knowledge through participation. Eighty‐four percent of trainees have remained in Alzheimer's disease and related dementias (ADRD) trials careers. Eighty percent of alumni have experienced career advances; nearly all indicated that participating in the course contributed to their success.

ABSTRACT Background Functional lumen imaging probe (FLIP) utility is established in treatment‐naïve achalasia but less clear following lower esophageal sphincter (LES) directed therapy. Methods Achalasia patients with LES directed therapy across three tertiary care centers between 2017 and 2024 with post‐treatment FLIP and timed barium esophagram (TBE) were retrospectively identified. Reduced esophagogastric junction (EGJ) opening was defined by distensibility index (DI) < 2 mm 2 /mmHg and diameter < 12 mm. Abnormal emptying on TBE was defined as column height ≥ 5 cm at 5 min and/or retained tablet. Eckardt scores ≤ 3 defined clinical response. Key Results The study included 222 patients (46% peroral endoscopic myotomy, 46% laparoscopic Heller myotomy, 8% pneumatic dilation) with a median of 1.4 years to post‐treatment TBE/FLIP. Abnormal emptying on TBE was associated with a narrower median EGJ diameter (13.2 vs. 14.8 mm, p = 0.008), a greater frequency of EGJ diameter < 12 mm (36% vs. 21%, p = 0.012), and a smaller change in EGJ diameter (+4.6 vs. +8.6 mm, p = 0.002). Abnormal emptying on TBE occurred more frequently in patients with EGJ DI < 2 mm 2 /mmHg (8.5% vs. 2.6%, p = 0.052), but was not associated with median EGJ DI (4.5 vs. 5.1 mm 2 /mmHg, p = 0.29) nor median change in EGJ DI (+2.9 vs. +3.9 mm 2 /mmHg, p = 0.25). Patients with reduced EGJ DI or EGJ diameter more often had abnormal TBE (37% vs. 22%, p = 0.012). Only the change in DI (+3.8 vs. +1.5 mm 2 /mmHg, p = 0.012) and diameter (+8.2 vs. +1.6 mm, p = 0.002) on FLIP was associated with a clinical response based on Eckardt ≤ 3. Conclusions and Inferences FLIP following achalasia therapy generally correlates with TBE, although discrepant findings are not uncommon. In particular, FLIP EGJ‐diameter has a strong association with esophageal emptying on TBE. Both TBE and FLIP have limited association with clinical response based on Eckardt, with change in DI and diameter on FLIP most strongly associated. Consequently, FLIP as part of multimodal assessment appears useful in the longitudinal follow‐up of treated achalasia.