Citations

Abstract Background Computed tomography (CT) is an essential imaging modality for disease diagnosis, treatment efficacy, and image‐based guidance of various medical procedures. The locally deposited radiation dose in tissues, as estimated by the computed tomography dose index (CTDI), can vary considerably across exposures delivered by CT scanners from different vendors, even if the scans are performed using similar technique factors, such as tube potential and tube current. The volumetric CTDI (CTDI vol ) is a common dose metric that reports an average radiation dose (in mGy) delivered to a specific volume within a test phantom. The CTDI vol is important in dosimetry applications as the organ absorbed dose within the patient has been shown to scale in near‐linear proportion, creating a basis for comparing organ doses across different scan protocols and scanner models. Purpose To develop a database of tube current‐time product (mAs) normalized CTDI vol values for currently utilized CT scanner models for each of the four primary CT vendors for use in the MIRDct organ dosimetry software available at MIRDsoft.org. This data forms the basis of the MIRDct code, which reports organ doses across a range of computational phantoms based upon axial organ dose coefficient libraries generated through Monte Carlo radiation transport for a reference CT scanner. Organ doses delivered by alternate CT scanner vendors and models may then be reported using ratios of normalized CTDI vol values under similar technique factors. Methods Scanners were selected from four major CT manufacturers: Philips Healthcare, GE Healthcare, Canon Medical Systems, and Siemens Healthineers. Technique parameters were also selected for each scanner that closely matched values used in the generation of an equivalent CT source term (small to large bowtie filters; 80–140‐kVp tube voltage; and 10‐mm to 40‐mm beam collimation). For each scanner chosen, the appropriate technique factors and protocols were selected, and the console‐reported CTDI vol values were recorded and normalized to a set value of 100 mAs. The normalized CTDI vol data collected for use within the MIRDct code were analyzed for noticeable patterns, features, and trends, and were compared to similar normalized CTDI vol datasets used within the National Cancer Institute NCICT software and the Virtual Phantoms, Inc. VirtualDose software. Results For all given CT scanner and technique factor combinations, there was strong agreement in normalized CTDI vol values across all three codes: between 0% and 12% difference for the compared scanners. Ratios of CTDI vol values for various CT scanner vendors and models to the corresponding CTDI vol values for the MIRDct reference scanner (Cannon Aquilion One Genesis) were also compared on the basis of either the 16‐cm head PMMA phantom or the 32‐cm body PMMA phantom. The mean quotient of these normalized CTDI vol ratios (head ratios to body ratios) was found to be approximately 1.06, and thus either ratio may be applied in reporting patient organ dose by MIRDct. Conclusions A database of normalized CTDI vol (mGy/100 mAs) was created for 17 models of CT scanners from four manufacturers at varying tube potentials, collimations, x‐ray bowtie filters, and phantom sizes for use in the MIRDct software.

ABSTRACT Background Ventricular tachycardia (VT) is a life‐threatening arrhythmia that requires both relatively rapid and accurate detection in intensive care units (ICUs). Continuous monitoring systems play a crucial role in detecting them. However, previous studies have reported that nearly 9 out of 10 arrhythmia alarms in ICUs tend to be false positives, which usually transpire to a well‐documented phenomenon called “alarm fatigue” that leads to desensitization, delayed responses, and increased cognitive burden on healthcare providers. Methods We developed a deep learning based, one‐dimensional convolutional neural network (1D‐CNN) to classify VT alarms using multiple raw waveform inputs, including two electrocardiogram (ECG) leads, photoplethysmogram (PPG) and arterial blood pressure (ABP) signals. The model was trained using the publicly available VTaC Arrhythmia Benchmark Dataset. We used the 10‐second waveform segments that preceded each VT alarm, pre‐processed and then used them to train the machine learning model to correctly classify the VT alarm. Results On the test set, the model achieved an area under the receiver operating characteristic curve of 0.901, overall accuracy of 83.22%, F1‐score of 73.3%, sensitivity of 77.53%, specificity of 85.63%, and positive predictive value of 69.57%. The model successfully detected over three‐quarters of them while significantly reducing false positive rates for the detection of VT. Conclusions This study demonstrates that a deep learning based 1D‐CNN model using short segments of raw waveform data can achieve robust performance in distinguishing true and false VT alarms.

Abstract Background Diabetes mellitus is a prevalent global health issue, affecting approximately 14% of individuals as latest reported. Among its complications, diabetic peripheral polyneuropathy (DPN) is a major cause of morbidity, impacting up to half of patients with long-standing diabetes. Given the limitations of current diagnostic methods, there is an urgent need for reliable biomarkers for early detection and management of DPN. Neurofilament light chain (NFL), a structural neuronal protein, has been proposed as a potential biomarker due to its release into the blood and cerebrospinal fluid following axonal damage. This systematic review and meta-analysis aim to evaluate serum NFL levels in diabetic patients with and without polyneuropathy compared to healthy controls. Methods A systematic search was conducted in four databases: Web of Science, PubMed, Scopus, and Embase. Studies were included if they measured serum NFL levels in diabetic patients, with or without polyneuropathy, and compared these levels to healthy controls. The Newcastle-Ottawa Scale (NOS) and JBI Critical Appraisal tools were used for quality assessment. We performed a meta-analysis using Review Manager 5.4 version, and assessed heterogeneity through chi-square tests and I² statistics. Results Eight studies (n = 2980 participants, including 950 diabetic patients and 2030 healthy controls) met the inclusion criteria. Three studies specifically evaluated serum NFL levels in diabetic patients with polyneuropathy. Meta-analysis revealed significantly elevated serum NFL levels in diabetic patients compared to healthy controls. Similarly, diabetic patients with polyneuropathy exhibited higher NFL levels than healthy controls. However, due to the limited number of studies comparing diabetic patients with and without neuropathy, a definitive conclusion on NFL's specificity to DPN remains uncertain. Heterogeneity analysis showed a moderate to high variation among studies, with a slight reduction after sensitivity analysis. Conclusion This study highlights elevated serum NFL levels in diabetic patients, irrespective of polyneuropathy status, suggesting that diabetes itself may contribute to increased NFL release. However, due to limited studies assessing NFL in DPN, further research is required to clarify its role as a specific biomarker for DPN.

Abstract Early-onset Alzheimer’s disease (EOAD) and Late-onset AD (LOAD) differ in clinical presentations and rates of progression. We aimed to compare baseline and longitudinal tau PET burden, and their relationship with clinical variables in amyloid-PET positive, cognitively impaired participants from the Longitudinal Early-Onset Alzheimer’s Disease Study (EOAD; n=390) and Alzheimer’s Disease Neuroimaging Initiative (LOAD; n=211). Patients with EOAD showed higher baseline tau PET retention, broader neuroanatomical involvement and faster accumulation rates over time compared to LOAD, after adjusting for amyloid load and clinical stage. Tau PET showed stronger correlations with baseline amyloid burden and clinical measures of global cognition and function in EOAD than LOAD. We conclude that earlier age of onset in AD is linked to a more aggressive tauopathy, which in turn is a primary driver of clinical decline. These findings suggest that optimal therapeutic targets and strategies may differ between EOAD and LOAD. One Sentence Summary Younger patients with Alzheimer’s disease show more aggressive tau spread, suggesting age of onset defines distinct disease pathways with key clinical implications.

PURPOSE Hot flashes are a common side effect reported by men receiving androgen-deprivation therapy (ADT) for the treatment of prostate cancer. We sought to determine whether oxybutynin could improve hot flash symptoms in men with prostate cancer. PATIENTS AND METHODS Patients with prostate cancer receiving a stable regimen of ADT with at least 28 hot flashes per week were randomly assigned to receive either oxybutynin 2.5 mg twice daily, oxybutynin 5 mg twice daily, or matching placebo for 6 weeks. The primary end point was the change in patient-reported hot flash scores since baseline at 6 weeks. Additional outcomes included incidence of adverse events (AEs), changes since baseline in Hot Flash–Related Daily Interference Scale (HFRDIS) scores, and patient-reported symptoms. RESULTS Eighty-eight patients were enrolled, with the 81 participants eligible for final analysis reporting an average of 10.1 (standard deviation [SD], 5.55) hot flashes per day and an average daily hot flash score of 18.2 (SD, 13.5) included in final analysis. On average, patients on the placebo arm, 2.5 mg oxybutynin arm, and 5 mg oxybutynin arm had reductions in hot flashes/day of 2.15, 4.77 ( P = .02), and 6.89 ( P < .001), respectively. Daily hot flash scores for placebo, 2.5 mg oxybutynin, and 5 mg oxybutynin reduced by an average of 4.85, 9.94 ( P = .07), and 13.95 ( P = .002) points, respectively. No treatment-related grade 3+ AEs occurred. HFRDIS total scores improved by 14.2 and 20.7 points in the 2.5 mg ( P = .042) and 5 mg ( P < .01) oxybutynin arms, respectively, compared with a 3.1-point improvement with placebo. CONCLUSION Oxybutynin is superior to a placebo for the management of ADT-associated hot flashes in men with prostate cancer.

Abstract Recent updates to monocyte count thresholds recognize oligomonocytic chronic myelomonocytic leukemia (OM-CMML) as an early form of CMML. However, the clinical validity of these changes remains uncertain without incorporating biological and genomic factors. In this study, we analyzed a cohort of 911 patients (249 with OM-CMML, 359 with overt CMML, and 303 with myelodysplastic syndromes) using unsupervised clustering to evaluate the role of genomic determinants in refining CMML diagnosis. Our findings show that CMML molecular signatures (biallelic TET2 mutations or SRSF2-TET2 comutations) are linked to a distinct transcriptome, monocytic bias, classical monocytosis, and a higher risk of progression to overt CMML in OM-CMML cases. We developed a weighted genomic model and diagnostic workflow showing that combining genomic signatures with bone marrow monocyte frequencies in OM-CMML more accurately predicts progression to overt CMML. These findings support integrating genomic determinants and our clinic-ready diagnostic workflow into the CMML diagnostic framework to improve accuracy. Significance: Through comprehensive clinical and genomic profiling of a large patient cohort, alongside immunophenotypic and transcriptional cellular analyses, this study provides evidence that incorporating genomic determinants into the diagnostic criteria for OM-CMML improves diagnostic accuracy and refines the identification of early-stage CMML, thereby preventing misclassification.