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Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures microvascular perfusion by capturing the temporal changes of an MRI contrast agent in a target tissue, and it provides valuable information for the diagnosis and prognosis of a wide range of tumors. Quantitative DCE-MRI analysis commonly relies on the nonlinear least square (NLLS) fitting of a pharmacokinetic (PK) model to concentration curves. However, the voxel-wise application of such nonlinear curve fitting is highly time-consuming. The arterial input function (AIF) needs to be utilized in quantitative DCE-MRI analysis. and in practice, a population-based arterial AIF is often used in PK modeling. The contribution of intravascular dispersion to the measured signal enhancement is assumed to be negligible. The MR dispersion imaging (MRDI) model was recently proposed to account for intravascular dispersion, enabling more accurate PK modeling. However, the complexity of the MRDI hinders its practical usability and makes quantitative PK modeling even more time-consuming. In this paper, we propose fast MR dispersion imaging (fMRDI) to effectively represent the intravascular dispersion and highly accelerated PK parameter estimation. We also propose a deep learning-based, two-stage framework to accelerate PK parameter estimation. We used a deep neural network (NN) to estimate PK parameters directly from enhancement curves. The estimation from NN was further refined using several steps of NLLS, which is significantly faster than performing NLLS from random initializations. A data synthesis module is proposed to generate synthetic training data for the NN. Two data-processing modules were introduced to improve the model's stability against noise and variations. Experiments on our in-house clinical prostate MRI dataset demonstrated that our method significantly reduces the processing time, produces a better distinction between normal and clinically significant prostate cancer (csPCa) lesions, and is more robust against noise than conventional DCE-MRI analysis methods.

Low bone mineral density and impaired bone quality have been shown to be important prognostic factors for curve progression in adolescent idiopathic scoliosis (AIS). There is no evidence-based integrative interpretation method to analyze high-resolution peripheral quantitative computed tomography (HR-pQCT) data in AIS. This study aimed to (1) utilize unsupervised machine learning to cluster bone microarchitecture phenotypes on HR-pQCT parameters in girls with AIS, (2) assess the phenotypes' risk of curve progression and progression to surgical threshold at skeletal maturity (primary cohort), and (3) investigate risk of curve progression in a separate cohort of girls with mild AIS whose curve severity did not reach bracing threshold at recruitment (secondary cohort). Patients were followed up prospectively for 6.22 ± 0.33 years in the primary cohort (n = 101). Three bone microarchitecture phenotypes were clustered by fuzzy C-means at time of peripubertal peak height velocity (PHV). Phenotype 1 had normal bone characteristics. Phenotype 2 was characterized by low bone volume and high cortical bone density, and phenotype 3 had low cortical and trabecular bone density and impaired trabecular microarchitecture. The difference in bone quality among the phenotypes was significant at peripubertal PHV and continued to skeletal maturity. Phenotype 3 had significantly increased risk of curve progression to surgical threshold at skeletal maturity (odd ratio [OR] = 4.88; 95% CI, 1.03-28.63). In the secondary cohort (n = 106), both phenotype 2 (adjusted OR = 5.39; 95% CI, 1.47-22.76) and phenotype 3 (adjusted OR = 3.67; 95% CI, 1.05-14.29) had increased risk of curve progression ≥6° with mean follow-up of 3.03 ± 0.16 years. In conclusion, 3 distinct bone microarchitecture phenotypes could be clustered by unsupervised machine learning on HR-pQCT-generated bone parameters at peripubertal PHV in AIS. The bone quality reflected by these phenotypes was found to have significant differentiating risk of curve progression and progression to surgical threshold at skeletal maturity in AIS., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society for Bone and Mineral Research. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: A direct-aspiration first-pass technique (ADAPT) in mechanical thrombectomy has been described in recent studies as an efficacious strategy compared with using a stent retriever (SR). We sought to evaluate for cost differences of ADAPT technique versus SR as an initial approach., Methods: We conducted a retrospective analysis of consecutive patients with mechanical thrombectomy at our institution between 2022 and 2023. Patients were grouped into ADAPT with/without SR as a rescue strategy and SR as an initial approach with allowance of concomitant aspiration. Direct cost data (consumables) were obtained. Baseline demographics, stroke metrics, procedure outcomes and cost, and last follow-up outcomes in modified Rankin Scale were compared between 2 groups., Results: Fifty-six patients were included. Thirty-seven (66.1%) underwent ADAPT, with 11 (29.7%) eventually requiring an SR. Mean age was 64.8 years. The average National Institutes of Health Stroke Scale score was 13.2 in the ADAPT group and 14.0 in the SR group (P = 0.68), with a similar proportion of tissue plasminogen activator (P = 0.53), site of occlusion (P = 0.66), and tandem occlusion (P = 0.69) between the groups. Recanalization was achieved in 94.6% of all patients, with an average of 1.9 passes, 89.3% being TICI 2B or above, with no differences between the 2 groups. Significantly lower cost (P < 0.01) was observed in ADAPT ($14,243.4) compared with SR ($19,003.6). Average follow-up duration was 180.2 days, with mortality of 23.2%. At last follow-up, 55.4% remained functionally independent (modified Rankin Scale score <3) with no difference (P = 0.56) between the ADAPT (59.5%) and SR (47.4%) groups., Conclusions: Outcomes were comparable between the ADAPT and SR groups. ADAPT reduced procedural consumables cost by approximately $5000 (25%), even if stent retrievers were allowed to be used for rescue. Establishing ADPAT as initial approach may bring significant direct cost savings while obtaining similar outcomes., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: The longitudinal management of unruptured brain arteriovenous malformation (bAVM) is crucial. To date, no study in the United States has evaluated the impact of socioeconomic status (SES) on bAVM outcome. Herein, the authors aimed to clarify the impact of SES, as indicated by the area deprivation index (ADI), on bAVM outcome., Methods: A retrospective analysis of an institutional bAVM database was conducted. Non-hereditary hemorrhagic telangiectasia patients with a single unruptured bAVM in the period from 1990 to 2021 were included in the analysis. The ADI was categorized as low (ADI ≤ 15th percentile), mid (15th percentile < ADI < 85th percentile), and high (ADI ≥ 85th percentile), with a low ADI indicating the most advantaged group. Patient baseline and follow-up data were analyzed. The primary outcome of interest was nonindependence (modified Rankin Scale [mRS] score > 2) at the last follow-up. A multivariable logistic regression model was performed., Results: A total of 589 patients with unruptured bAVMs were included in the study. The mean patient age at diagnosis was 37.2 years, and 283 patients (48.0%) were male. Of the bAVMs, 238 (40.4%) had a low Spetzler-Martin grade (SMG < III), 194 (32.9%) had a moderate grade (SMG III), and 157 (26.7%) had a high grade (SMG > III). Sixty-nine patients (11.7%) were in the low-ADI group, 476 (80.8%) in the mid-ADI group, and 44 (7.5%) in high-ADI group. Increasing age (OR 1.02, 95% CI 1.01-1.04, p < 0.001), poor baseline mRS score (OR 3.27, 95% CI 1.32-7.88, p = 0.008), treatment with surgery plus radiosurgery with or without embolization (OR 3.21, 95% CI 1.03-9.81, p = 0.041), mid SMG (OR 1.94, 95% CI 1.11-3.44, p = 0.021), high SMG (OR 2.08, 95% CI 1.13-3.88, p = 0.020), longer follow-up (OR 1.05, 95% CI 1.03-1.08, p < 0.001), and mid ADI (OR 3.08, 95% CI 1.34-8.39, p = 0.015) were significantly associated with a poor outcome. A high ADI tended toward a poor outcome (OR 2.93, 95% CI 0.92-9.88, p = 0.071). Eventual obliteration of a bAVM was the only protective predictor of poor outcome (OR 0.55, 95% CI 0.30-0.98, p = 0.046)., Conclusions: This study revealed that relatively disadvantaged patients with unruptured bAVMs are more likely to experience nonindependent outcomes at the last follow-up, after adjusting for confounding variables. An emphasis on social support may be beneficial for patients with a lower SES.

Background and Objectives: Natural history of hemorrhage in brain arteriovenous malformations (bAVM) is reported at 2%-4% per year. Published studies using survival analysis fail to account for recurrent hemorrhagic events. In this study, we present a large, single institution series to elucidate the natural history of bAVM using multivariable Poisson regression., Methods: This is a retrospective cohort study. All patients with bAVM seen at our institution from 1990 to 2021 were included. Hemorrhages after detection of bAVM during the untreated interval were recorded. Natural history of hemorrhage was calculated by dividing number of hemorrhages by untreated interval. The frequency of hemorrhages followed a Poisson distribution. Multivariable Poisson regression with an offset variable of untreated interval in patient-years was constructed. Model selection was through a stepwise Akaike information criterion method. Stratified hemorrhagic rate was presented using different combinations of significant factors., Results: A total of 1066 patients with nonhereditary hemorrhagic telangiectasia harboring a single bAVM were included. Ninety (8.44%) patients had 101 hemorrhages during an untreated interval of 3596.3344 patient-years, translating to an overall hemorrhagic rate of 2.81% per year. Significant factors increasing hemorrhage risk included ruptured presentation ( P < .001), increasing age ( P < .001), female sex ( P = .043), and deep location ( P = .040). Adult male patients with ruptured presentation and deep bAVMs sustained the highest annual risk at 10.81%, whereas no hemorrhages occurred in unruptured pediatric male patients or ruptured pediatric (younger than 18 years) male patients with superficial bAVMs., Conclusion: Hemorrhage after bAVM detection occurs in 8.41% of all patients, and the rate averages 2.81% per year. However, this risk varies from 0.00% to 10.81% per year depending on various risk factor combinations. Efforts should be made to stratify bAVM hemorrhage rate by risk factors for more precise estimation of bleeding risk if left untreated., (Copyright © Congress of Neurological Surgeons 2023. All rights reserved.)

Introduction : Bracing is one of the first-line treatment for early-onset idiopathic scoliosis (EOIS) to control curves from progression. This study aimed to explore the determinants that govern bracing effectiveness in EOIS. Methods : One hundred and eleven patients with EOIS (mean age of 8.6 ± 1.25 at diagnosis) received bracing treatment and had a final follow-up beyond skeletal maturity were identified from records between 1988 and 2021. Demographic data and clinical features of spinal curvature were obtained for correlation analyses to determine the associations between curve outcomes and clinical features. Results : Most patients were female (85.6%) and had a major curve on the left side (67%). The mean baseline Cobb angle of major curves was 21.73 ± 7.92°, with a mean Cobb angle progression of 18.05 ± 19.11°. The average bracing duration was 5.3 ± 1.9 years. Only 26 (23.4%) of them underwent surgery. The final Cobb angle and curve progression at the final follow-up with a Cobb angle of ≥50° were positively correlated with the initial Cobb angle (r = 0.206 and r = 0.313, respectively) and negatively correlated with maturity parameters. The lumbar curve type was found to correlate with a smaller final Cobb angle. Conclusions : The majority of patients had a final Cobb angle < 50°, which was considered a successful bracing outcome. The final Cobb angle correlated with the initial Cobb angle and curve types observed in EOIS.

A large portion of volumetric medical data, especially magnetic resonance imaging (MRI) data, is anisotropic, as the through-plane resolution is typically much lower than the in-plane resolution. Both 3D and purely 2D deep learning-based segmentation methods are deficient in dealing with such volumetric data since the performance of 3D methods suffers when confronting anisotropic data, and 2D methods disregard crucial volumetric information. Insufficient work has been done on 2.5D methods, in which 2D convolution is mainly used in concert with volumetric information. These models focus on learning the relationship across slices, but typically have many parameters to train. We offer a Cross-Slice Attention Module (CSAM) with minimal trainable parameters, which captures information across all the slices in the volume by applying semantic, positional, and slice attention on deep feature maps at different scales. Our extensive experiments using different network architectures and tasks demonstrate the usefulness and generalizability of CSAM. Associated code is available at https://github.com/aL3x-O-o-Hung/CSAM.