Citations

Key Points Automated cyst segmentation in autosomal dominant polycystic kidney disease could transform care by enabling precise monitoring of cyst progression with minimal manual effort. This technology can facilitate more consistent assessments of cyst burden across patients. Translating these segmentation models from magnetic resonance imaging to computed tomography is crucial for providing comprehensive, modality-independent evaluations. Background Total kidney and liver volumes are key image-based biomarkers to predict the severity of kidney and liver phenotype in autosomal dominant polycystic kidney disease (ADPKD). However, magnetic resonance (MR) imaging-based advanced biomarkers like total cyst number and cyst parenchyma surface area have been shown to more accurately assess cyst burden and improve the prediction of disease progression. The main aim of this study was to extend the calculation of advanced biomarkers to other imaging modalities; thus, we propose a fully automated model to segment kidney and liver cysts in computed tomography (CT) images. Methods Abdominal CTs of patients with ADPKD were gathered retrospectively between 2001 and 2018. A three-dimensional deep learning method using the nnU-Net architecture was trained to learn cyst edges-cores and the noncystic kidney/liver parenchyma. Separate segmentation models were trained for kidney cysts in contrast-enhanced CTs and liver cysts in noncontrast CTs using an active learning approach. Two experienced research fellows manually generated the reference standard segmentation, which were reviewed by an expert radiologist for accuracy. Results Two-hundred CT scans from 148 patients (mean age, 51.2±14.1 years; 48% male) were used for model training (80%) and testing (20%). In the test set, both models showed good agreement with the reference standard segmentations, similar to the agreement between two independent human readers (model versus reader: TCN kidney/liver r =0.96/0.97 and CPSA kidney r =0.98, inter-reader: TCN kidney/liver r =0.96/0.98 and CPSA kidney r =0.99). Conclusions Our study demonstrates that automated models can segment kidney and liver cysts accurately in CT scans of patients with ADPKD.

Abstract The advances in large language models (LLMs) have demonstrated sophisticated potential for automating complex tasks within the radiology workflow. From radiology report generation and report summarization to data collection for research trials, these models have proven to be powerful tools. However, optimal implementation of these models requires careful adaptation to the specialized medical domain. In addition, these models tend to generate information that is not truthful or factual, which can adversely affect patient care and clinical decisions. Strategies such as fine-tuning and prompt optimization have been shown to be impactful in eliminating these errors. Although these models undergo rapid updates and improvements, understanding the principles of prompt engineering and fine-tuning provides a foundation for evaluating and maintaining the performance of any LLM deployment. The current article aims to review the recent advancements in radiology using fine-tuning and prompt optimization to leverage LLMs’ capabilities. It delves into various techniques within each strategy, their advantages and limitations, and presents a framework to facilitate the practical integration of LLMs into radiology settings.

Purpose: Atrial fibrillation (AF), the most common arrhythmia in older adults, is often treated with cryoballoon ablation (CBA) or laser balloon ablation (LBA) when refractory to medication. We used real-world patient data to investigate differences in arrhythmia-free survival, procedure and fluoroscopy times, and complications between these techniques. Methods: In this single-center, retrospective study of 178 patients with symptomatic paroxysmal AF referred for first-time pulmonary vein isolation (PVI), 103 underwent CBA, and 75 underwent LBA. Recurrence was defined as any atrial arrhythmia of > 30 seconds’ duration after a three-month blanking period. Complications at 30 days were recorded. Kaplan-Meier analysis was used to estimate arrhythmia-free survival at one year for both groups. Categorical variables were expressed as n (%) and continuous variables as mean ± standard deviation or median (Q1, Q3). Results: Baseline characteristics were similar between groups, with exception to left atrial volume index (CBA 32.2 ± 10.9 ml/m2; LBA 36.0 ± 11.0 ml/m2, p = 0.047). History of stroke was higher in CBA (13.6% vs 2.7%; p = 0.012), as was coronary artery disease (35.0% vs 14.7%, p = 0.002). Median fluoroscopy and procedure times (min) were significantly shorter in LBA (24.2 vs 31.6, p = 0.004; 141.0 vs 210.0, p < 0.0001, respectively). One-year arrhythmia-free survival rates were similar (CBA 79.1% vs LBA 78.7%, p = 0.934), as were total complication rates (21.4% vs 16.0%, p = 0.289), including rates of transient ischemic attack/stroke and vascular complications. Conclusions: In patients with paroxysmal AF undergoing PVI, CBA and LBA were equally efficacious at one year and demonstrated acceptably low complication rates. LBA was associated with reduced procedure time and fluoroscopy exposure.

Abstract Background The CCDC26 germline variant rs55705857 is causal for development of IDH mutant (IDHmut) adult glioma. However, ~60% of IDHmut patients do not carry the rs55705857 risk allele. We aimed to identify variants associated with developing IDHmut glioma among patients that do not have the rs55705857 risk allele and to further understand development of IDHwt glioma. Methods We used three datasets that included 1216 IDHmut and 1442 IDHwt glioma patients and a case–case design to perform genome-wide association (GWAS) analyses comparing IDHmut versus IDHwt glioma. Analyses were performed overall and stratified by rs55705857 genotype and sex. Multivariable logistic regression and regression trees were used to develop models to predict IDH status using germline variants, age, and contrast enhancement on MRI. Results Three regions were identified comparing IDHmut versus IDHwt: rs55705857 (meta P-value [P] = 1.35 × 10−43), PHLDB1 (rs7125115, P = 3.46 × 10−17), and D2HGDH (rs71430382, P = 2.43 × 10−12). When analyzing only patients that do not have the rs55705857 risk allele, PHLDB1 (rs7125115, P = 1.73 × 10−13) and D2HGDH (rs71430382, P = 8.86 × 10−10) were identified. Among patients who have the rs55705857 risk allele, four variants between ROBO1 and ROBO2 (rs4680975, P = 4.65 × 10−7) increased the likelihood of having an IDHwt tumor. Tumor expression of ROBO1 was associated with rs4680975 genotype in IDHwt patients that have the rs55705857 risk allele (P = 0.034). Multivariable logistic analysis demonstrated that rs55705857, rs71430382 (D2HGDH), and age predicted IDH mutation status. Conclusions To understand the development of adult glioma, we demonstrate that D2HGDH and PHLDB1 should be prioritized for functional studies in IDHmut tumors. The ROBO1 region warrants further investigation in IDHwt tumors.

OBJECTIVE Patients with growth hormone (GH)–secreting pituitary adenomas (PAs) experience various symptoms and comorbidities, which can ultimately lead to increased mortality. This study aimed to develop and validate a machine learning (ML) model for predicting long-term outcomes in patients with GH-secreting PAs following endonasal transsphenoidal surgery (ETS). METHODS The authors conducted a retrospective three-institution cohort study that included patients with GH-secreting PAs treated with ETS between 2013 and 2023. Clinical, radiological, and biochemical data were collected. The main outcome of interest was the intervention-free rate (IFR) after primary ETS. Supervised ML algorithms, including decision trees and random forests, were developed to predict the IFR. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC) and Shapley Additive Explanations (SHAP) values. RESULTS The median follow-up for 100 patients with GH-secreting PAs (53% female) was 64 months (range 1–130 months). Additional intervention for persistent or recurrent acromegaly was required in 32% of patients. Following primary ETS alone, the 3-year IFR was 70% and the 5-year IFR was 67%. Multiple ML models were developed and evaluated using AUROCs. The decision tree analysis achieved an accuracy of 81% and emphasized the importance of both gross-total resection (GTR) and patient age in determining the long-term IFR. To better understand the factors that contributed to model performance, SHAP analysis was applied to the best-performing model. The SHAP dependence plots showed that key factors associated with a longer IFR included tumor size < 9 mm, GTR, patient age > 65 years, and Knosp grade 0. CONCLUSIONS This ML model offers a more nuanced and potentially more accurate approach to identify patients more likely to develop recurrent or persistent acromegaly following primary ETS and require additional treatment. Following external validation, this ML model could improve personalized treatment planning and follow-up strategies and enhance patient care and resource allocation in clinical practice.

Recent advancements in compact MRI scanners, portable low-field-strength MRI systems, and high-performance gradient inserts address the challenges of conventional MRI for brain imaging, providing more accessible and versatile technology.

Purpose Spine-related pain disorders commonly require interventions such as spinal injections, which are often image-guided and increase in demand as populations age. This study investigates the impact of lifestyle changes following retirement on the incidence of these procedures, hypothesizing that retirement significantly influences the requirement for such treatments. Methods A review of medical records from XXX County between 2000 and 2023 was conducted, focusing on patients who received spinal injections, including epidural steroid, facet, and sacroiliac joint injections. Data were stratified by age, employment status pre- and post-procedure, and type of injection. Statistical analysis, including segmental linear regression and t-tests, was used to assess changes in incidence rates over time and across demographic subsets. Results Out of 134,318 patients analyzed, a total of 359,224 spinal injections were performed. There was a significant ( p -value <.01) increase in the frequency of procedures among individuals around the age of 60, which coincides with the typical retirement age in the United States. The study also highlighted a subsequent decline in the incidence of these procedures between the ages of 70 and 74, suggesting potential adaptation to chronic conditions or the effectiveness of earlier treatments. Conclusion The findings of this study confirm the hypothesis that retirement significantly impacts the demand for spinal injections, as evidenced by the marked increase in procedures at typical retirement ages. This underscores the importance of integrating preventive care and personalized medical guidance into healthcare strategies for individuals nearing retirement to effectively manage spine-related pain disorders and reduce the necessity for invasive treatments.

Background Right ventricular (RV) maladaptation to elevated pulmonary afterload is the primary determinant of outcomes in pulmonary artery (PA) hypertension; however, the pathobiological mechanisms underlying RV decompensation remain poorly understood. Methods We performed global untargeted metabolomics on plasma from 55 patients who underwent gold‐standard RV‐PA coupling measurements using multibeat pressure volume loop assessment in a single‐center cohort and from 1027 patients with coupling surrogate measurements in a larger multicenter cohort, the PVDOMICS (Pulmonary Vascular Disease Phenomics) study. Age and sex‐adjusted linear regression was performed to identify associations between metabolites and coupling metrics. Additionally, we performed a metabolic flux analysis using gene expression data from RV tissue in an independent cohort of 32 patients. Partial least squares–discriminant analysis was used to identify metabolites and reactions characteristic of the decompensated RV. Results RV‐PA coupling was negatively associated with tricarboxylic acid (TCA) cycle intermediate levels. Specifically, plasma α‐ketoglutarate and fumarate were significantly associated with all coupling metrics in both cohorts. Metabolic flux analysis indicated that decompensated RVs exhibited aberrant TCA cycle activity, including reduced acetyl coenzyme A entry and increased lactate elimination, suggesting a shift from the TCA cycle toward glycolysis at the RV tissue level. Conclusions We identify an association between circulating TCA cycle intermediate levels and RV‐PA uncoupling in 2 independent cohorts, and dysregulated TCA cycle metabolism in decompensated PA hypertension RVs, suggesting that aberrant TCA cycle metabolism could represent a hallmark of RV maladaptation in PA hypertension. Further study of this pathway is warranted to develop novel biomarkers of RV function or RV‐targeted therapies.