Automated pelvic MRI measurements associated with urinary incontinence for prostate cancer patients undergoing radical prostatectomy

Background: Pelvic morphological parameters on magnetic resonance imaging (MRI), such as the membranous urethral length (MUL), can predict urinary incontinence after radical prostatectomy but are prone to interobserver disagreement. Our objective was to improve interobserver agreement among radiologists in measuring pelvic parameters using deep learning (DL)-based segmentation of pelvic structures on MRI scans. Methods: Preoperative MRI was collected from 167 prostate cancer patients undergoing radical prostatectomy within our regional multicentric cohort. Two DL networks (nnU-Net) were trained on coronal and sagittal scans and evaluated on a test cohort using an 80/20% train-test split. Pelvic parameters were manually measured by three abdominal radiologists on raw MRI images and with the use of DL-generated segmentations. Automated measurements were also performed for the pelvic parameters. Interobserver agreement was evaluated using the intraclass correlation coefficient (ICC) and the Bland–Altman plot. Results: The DL models achieved median Dice similarity coefficient (DSC) values of 0.85–0.97 for coronal structures and 0.87–0.98 for sagittal structures. When radiologists used DL-generated segmentations of pelvic structures, the interobserver agreement for sagittal MUL improved from 0.64 (95% confidence interval 0.28–0.83) to 0.91 (95% CI 0.84–0.95). Furthermore, there was an increase in ICC values for the obturator internus muscle from 0.74 (95% CI 0.42–0.87) to 0.86 (95% CI 0.75–0.92) and for the levator ani muscle from 0.40 (95% CI 0.05–0.66) to 0.61 (95% CI 0.31–0.78). Conclusions: DL-based automated segmentation of pelvic structures improved interobserver agreement in measuring pelvic parameters on preoperative MRI scans. Relevance statement: The implementation of deep learning segmentations allows for more consistent measurements of pelvic parameters by radiologists. Standardized measurements are crucial for incorporating these parameters into urinary continence prediction models. Key points: • DL-generated segmentations improve interobserver agreement for pelvic measurements among radiologists. • Membranous urethral length measurement improved from substantial to almost perfect agreement. • Artificial intelligence enhances objective pelvic parameter assessment for continence prediction models. Graphical Abstract: [Figure not available: see fulltext.

Early systemic microvascular damage in pigs with atherogenic diabetes mellitus coincides with renal angiopoietin dysbalance

Background: Diabetes mellitus (DM) is associated with a range of microvascular complications including diabetic nephropathy (DN). Microvascular abnormalities in the kidneys are common histopathologic findings in DN, which represent one manifestation of ongoing systemic microvascular damage. Recently, sidestream dark-field (SDF) imaging has emerged as a noninvasive tool that enables one to visualize the microcirculation. In this study, we investigated whether changes in the systemic microvasculature induced by DM and an atherogenic diet correlated spatiotemporally with renal damage. Methods: Atherosclerotic lesion development was triggered in streptozotocin-induced DM pigs (140 mg/kg body weight) by administering an atherogenic diet for approximately 11 months. Fifteen months following induction of DM, microvascular morphology was visualized in control pigs (n = 7), non-diabetic pigs fed an atherogenic diet (ATH, n = 5), and DM pigs fed an atherogenic diet (DM+ATH, n = 5) using SDF imaging of oral mucosal tissue. Subsequently, kidneys were harvested from anethesized pigs and the expression levels of well-established markers for microvascular integrity, such as Angiopoietin-1 (Angpt1) and Angiopoietin-2 (Angpt2) were determined immunohistochemically, while endothelial cell (EC) abundance was determined by immunostaining for von Willebrand factor (vWF). Results: Our study revealed an increase in the capillary tortuosity index in DM+ATH pigs (2.31±0.17) as compared to the control groups (Controls 0.89±0.08 and ATH 1.55±0.11; p<0.05). Kidney biopsies showed marked glomerular lesions consisting of mesangial expansion and podocyte lesions. Furthermore, we observed a disturbed Angpt2/ Angpt1balance in the cortex of the kidney, as evidenced by increased expression of Angpt2 in DM+ATH pigs as compared to Control pigs (p<0.05). Conclusion: In the setting of DM, atherogenesis leads to the augmentation of mucosal capillary tortuosity, indicative of systemic microvascular damage. Concomitantly, a dysbalance in renal angiopoietins was correlated with the development of diabetic nephropathy. As such, our studies strongly suggest that defects in the systemic microvasculature mirror the accumulation of microvascular damage in the kidney

Evaluation of a website providing information on regional health care services for patients with rheumatoid arthritis: an observational study

Studies on the effectiveness of information provision for patients with arthritis through the Internet are scarce. This study aimed to describe rheumatoid arthritis (RA) patients’ knowledge and information needs before and after launching a website providing information on regional health care services for patients with rheumatic conditions. The intervention consisted of a weekly updated website comprising practical information on regional health care services for patients with arthritis. In addition, patients were offered information leaflets and an information meeting. Before (T1) and 24 months after (T2) the website was launched, a random sample of 400 RA patients filled in a questionnaire regarding knowledge and information need (scores 0–18) about accessibility and contents of 18 regional health care services. Two hundred and fifty-one patients returned the questionnaire (response rate 63%) at T1 and 200 patients (50%) at T2, respectively, with 160 paired observations (112 females (70%), mean age 60.4 years (SD 9.9)). The total score for insufficient knowledge about contents decreased from 9.3 (SD 4.9) to 8.5 (SD 4.8; p = 0.03) and for accessibility from 8.6 (SD 4.7) to 8.4 (SD 4.9; p = 0.59). Total score for information need about contents decreased from 4.2 (SD 4.5) to 1.9 (SD 2.9; p < 0.01) and for accessibility from 3.6 (SD 4.5) to 1.4 (SD 2.4; p < 0.01) (paired t-tests)

Deep learning for automated contouring of neurovascular structures on magnetic resonance imaging for prostate cancer patients

Background and purpose: Manual contouring of neurovascular structures on prostate magnetic resonance imaging (MRI) is labor-intensive and prone to considerable interrater disagreement. Our aim is to contour neurovascular structures automatically on prostate MRI by deep learning (DL) to improve workflow and interrater agreement. Materials and methods: Segmentation of neurovascular structures was performed on pre-treatment 3.0 T MRI data of 131 prostate cancer patients (training [n = 105] and testing [n = 26]). The neurovascular structures include the penile bulb (PB), corpora cavernosa (CCs), internal pudendal arteries (IPAs), and neurovascular bundles (NVBs). Two DL networks, nnU-Net and DeepMedic, were trained for auto-contouring on prostate MRI and evaluated using volumetric Dice similarity coefficient (DSC), mean surface distances (MSD), Hausdorff distances, and surface DSC. Three radiation oncologists evaluated the DL-generated contours and performed corrections when necessary. Interrater agreement was assessed and the time required for manual correction was recorded. Results: nnU-Net achieved a median DSC of 0.92 (IQR: 0.90–0.93) for the PB, 0.90 (IQR: 0.86–0.92) for the CCs, 0.79 (IQR: 0.77–0.83) for the IPAs, and 0.77 (IQR: 0.72–0.81) for the NVBs, which outperformed DeepMedic for each structure (p < 0.03). nnU-Net showed a median MSD of 0.24 mm for the IPAs and 0.71 mm for the NVBs. The median interrater DSC ranged from 0.93 to 1.00, with the majority of cases (68.9%) requiring manual correction times under two minutes. Conclusions: DL enables reliable auto-contouring of neurovascular structures on pre-treatment MRI data, easing the clinical workflow in neurovascular-sparing MR-guided radiotherapy

The Development and External Validation of Artificial Intelligence-Driven MRI-Based Models to Improve Prediction of Lesion-Specific Extraprostatic Extension in Patients with Prostate Cancer

Adequate detection of the histopathological extraprostatic extension (EPE) of prostate cancer (PCa) remains a challenge using conventional radiomics on 3 Tesla multiparametric magnetic resonance imaging (3T mpMRI). This study focuses on the assessment of artificial intelligence (AI)-driven models with innovative MRI radiomics in predicting EPE of prostate cancer (PCa) at a lesion-specific level. With a dataset encompassing 994 lesions from 794 PCa patients who underwent robot-assisted radical prostatectomy (RARP) at two Dutch hospitals, the study establishes and validates three classification models. The models were validated on an internal validation cohort of 162 lesions and an external validation cohort of 189 lesions in terms of discrimination, calibration, net benefit, and comparison to radiology reporting. Notably, the achieved AUCs ranged from 0.86 to 0.91 at the lesion-specific level, demonstrating the superior accuracy of the random forest model over conventional radiological reporting. At the external test cohort, the random forest model was the best-calibrated model and demonstrated a significantly higher accuracy compared to radiological reporting (83% vs. 67%, p = 0.02). In conclusion, an AI-powered model that includes both existing and novel MRI radiomics improves the detection of lesion-specific EPE in prostate cancer

Rabies Virus Populations in Humans and Mice Show Minor Inter-Host Variability within Various Central Nervous System Regions and Peripheral Tissues

Rabies virus (RABV) has a broad host range and infects multiple cell types throughout the infection cycle. Next-generation sequencing (NGS) and minor variant analysis are powerful tools for studying virus populations within specific hosts and tissues, leading to novel insights into the mechanisms of host-switching and key factors for infecting specific cell types. In this study we investigated RABV populations and minor variants in both original (non-passaged) samples and in vitro-passaged isolates of various CNS regions (hippocampus, medulla oblongata and spinal cord) of a fatal human rabies case, and of multiple CNS and non-CNS tissues of experimentally infected mice. No differences in virus populations were detected between the human CNS regions, and only one non-synonymous single nucleotide polymorphism (SNP) was detected in the fifth in vitro passage of virus isolated from the spinal cord. However, the appearance of this SNP shows the importance of sequencing newly passaged virus stocks before further use. Similarly, we did not detect apparent differences in virus populations isolated from different CNS and non-CNS tissues of experimentally infected mice. Sequencing of viruses obtained from pharyngeal swab and salivary gland proved difficult, and we propose methods for improving sampling

The association between maternal 25-hydroxyvitamin D concentration during gestation and early childhood cardio-metabolic outcomes: is there interaction with pre-pregnancy BMI?

Both maternal 25-hydroxyvitamin D (25OHD) status and pre-pregnancy BMI (pBMI) may influence offspring cardio-metabolic outcomes. Lower 25OHD concentrations have been observed in women with both low and high pBMIs, but the combined influence of pBMI and 25OHD on offspring cardio-metabolic outcomes is unknown. Therefore, this study investigated the role of pBMI in the association between maternal 25OHD concentration and cardio-metabolic outcomes in 5-6 year old children. Data were obtained from the ABCD cohort study and 1882 mother-child pairs were included. The offspring outcomes investigated were systolic and diastolic blood pressure, heart rate, BMI, body fat percentage (%BF), waist-to-height ratio, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, glucose, C-peptide, and insulin resistance (HOMA2-IR). 62% of the C-peptide samples were below the detection limit and were thus imputed using survival analysis. Models were corrected for maternal and offspring covariates and tested for interaction with pBMI. Interaction with pBMI was observed in the associations with insulin resistance markers: in offspring of overweight mothers (≥25.0 kg/m2), a 10 nmol/L increase in maternal 25OHD was associated with a 0.007(99%CI:-0.01,-0.001) nmol/L decrease in C-peptide and a 0.02(99%CI:-0.03,-0.004) decrease in HOMA2-IR. When only non-imputed data were analyzed, there was a trend for interaction in the relationship but the results lost significance. Interaction with pBMI was not observed for the other outcomes. A 10 nmol/L increase in maternal 25OHD was significantly associated with a 0.13%(99%CI:-0.3,-0.003) decrease in %BF after correction for maternal and child covariates. Thus, intrauterine exposure to both low 25OHD and maternal overweight may be associated with increased insulin resistance in offspring, while exposure to low 25OHD in utero may be associated with increased offspring %BF with no interactive effects from pBMI. Due to the limitations of this study, these results are not conclusive, however the observations of this study pose important research questions for future studies to investigate

Severe Pediatric COVID-19 and Multisystem Inflammatory Syndrome in Children from Wild-type to Population Immunity: A Prospective Multicenter Cohort Study with Real-time Reporting

Background: SARS-CoV-2 variant evolution and increasing immunity altered the impact of pediatric SARS-CoV-2 infection. Public health decision-making relies on accurate and timely reporting of clinical data. Methods: This international hospital-based multicenter, prospective cohort study with real-time reporting was active from March 2020 to December 2022. We evaluated longitudinal incident rates and risk factors for disease severity. Results: We included 564 hospitalized children with acute COVID-19 (n = 375) or multisystem inflammatory syndrome in children (n = 189) from the Netherlands, Curaçao and Surinam. In COVID-19, 134/375 patients (36%) needed supplemental oxygen therapy and 35 (9.3%) required intensive care treatment. Age above 12 years and preexisting pulmonary conditions were predictors for severe COVID-19. During omicron, hospitalized children had milder disease. During population immunity, the incidence rate of pediatric COVID-19 infection declined for older children but was stable for children below 1 year. The incidence rate of multisystem inflammatory syndrome in children was highest during the delta wave and has decreased rapidly since omicron emerged. Real-time reporting of our data impacted national pediatric SARS-CoV-2 vaccination- and booster-policies. Conclusions: Our data supports the notion that similar to adults, prior immunity protects against severe sequelae of SARS-CoV-2 infections in children. Real-time reporting of accurate and high-quality data is feasible and impacts clinical and public health decision-making. The reporting framework of our consortium is readily accessible for future SARS-CoV-2 waves and other emerging infections