T2-weighted Images Research Articles

Evaluation of Middle Cerebral Artery Culprit Plaque Inflammation in Ischemic Stroke Using CAIPIRINHA-Dixon-TWIST Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

Middle cerebral artery (MCA) plaques are a leading cause of ischemic stroke (IS). Plaque inflammation is crucial for plaque stability and urgently needs quantitative detection. To explore the utility of Controlled Aliasing in Parallel Imaging Results in Higher Acceleration (CAIPIRINHA)-Dixon-Time-resolved angiography With Interleaved Stochastic Trajectories (TWIST) (CDT) dynamic contrast-enhanced MRI (DCE-MRI) for evaluating MCA culprit plaque inflammation changes over stroke time and with diabetes mellitus (DM). Prospective. Ninety-four patients (51.6 ± 12.23 years, 32 females, 23 DM) with acute IS (AIS; N = 43) and non-acute IS (non-AIS; 14 days < stroke time ≤ 3 months; N = 51). 3-T, CDT DCE-MRI and three-dimensional (3D) Sampling Perfection with Application optimized Contrast using different flip angle Evolution (3D-SPACE) T1-weighted imaging (T1WI). Stroke time (from initial IS symptoms to MRI) and DM were registered. For 94 MCA culprit plaques, Ktrans from CDT DCE-MRI and enhancement ratio (ER) from 3D-SPACE T1WI were compared between groups with and without AIS and DM. Shapiro-Wilk test, Bland-Altman analysis, Passing and Bablok test, independent t-test, Mann-Whitney U test, Chi-squared test, Fisher's exact test, receiver operating characteristics (ROC) with the area under the curve (AUC), DeLong's test, and Spearman rank correlation test with the P-value significance level of 0.05. Ktrans and ER of MCA culprit plaques were significantly higher in AIS than non-AIS patients (Ktrans = 0.098 s-1 vs. 0.037 s-1; ER = 0.86 vs. 0.55). Ktrans showed better AUC for distinguishing AIS from non-AIS patients (0.87 vs. 0.75) and stronger negative correlation with stroke time than ER (r = -0.60 vs. -0.34). DM patients had significantly higher Ktrans and ER than non-DM patients in IS and AIS groups. Imaging by CDT DCE-MRI may allow to quantitatively evaluate MCA culprit plaques over stroke time and DM. 2 TECHNICAL EFFICACY: Stage 2.

Multi-parametric magnetic resonance imaging of prostate cancer: correlation between Ktrans, Gleason score, and PI-RADS score

PurposeTo measure Ktrans and correlate it with Gleason score (GS) and PI-RADS score in patients with prostate cancer.MethodsThis retrospective study included patients with pathologically proven prostate cancer who had undergone clinically indicated 1.5 Tesla multi-parametric magnetic resonance imaging (MRI) examination between February and December 2020. T2-weighted (T2w) images, diffusion-weighted images (DWI), and dynamic contrast-enhanced (DCE) sequences were obtained. PI-RADS score was calculated for all tumor lesions. From DCE-MRI dataset, Ktrans was computed and compared between patients with clinically insignificant (GS ≤ 6) and clinically significant (GS ≥ 7) prostate cancer. Spearman rank-order correlation coefficient (ρ) was used to assess the correlation strength between Ktrans and GS and between Ktrans and PI-RADS score.ResultsTwenty-one patients (age: 67 ± 12 years; BMI: 26.63 ± 4.04 kg/m2) with a PSA of 7.91 ± 3.01 were included in the study. Seven patients (33.3%) had clinically insignificant prostate cancer, while 14 patients (66.7%) were diagnosed with clinically significant prostate cancer. Mean Ktrans value was 0.42 ± 0.20 min−1 (range: 0.15–0.75). Ktrans was significantly higher (0.50 ± 0.17 min−1) in clinically significant prostate cancer compared to clinical insignificant prostate cancer (0.23 ± 0.15 min−1; P = 0.001). Ktrans showed moderate significant correlation with GS (ρ = 0.575, P = 0.006), but showed no significant correlation with PI-RADS (ρ = 0.386, P = 0.069).ConclusionKtrans may discriminate between clinically insignificant and significant prostate cancer and shows moderate correlation with GS. Thus, MP-MRI may serve as an imaging biomarker in prostate cancer.

Application of a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram in peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer.

This study aims to use a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram to predict preoperative peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer. We included a total of 68 PNI patients and 80 LVI patients who underwent surgical resection and pathological confirmation of rectal cancer. According to the PNI/LVI status, patients were divided into PNI positive group (n = 39), the PNI negative group (n = 29), LVI positive group (n = 48), and the LVI negative group (n = 32). External validation included a total of 42 patients with nerve and vascular invasion in patients with surgically resected and pathologically confirmed rectal cancer at another healthcare facility, with a PNI positive group (n = 32) and a PNI-negative group (n = 10) as well as an LVI positive group (n = 35) and LVI-negative group (n = 7). All patients underwent 3.0T magnetic resonance T1WI enhanced scanning. We use Firevoxel software to delineate the region of interest (ROI), extract histogram parameters, and perform univariate analysis, LASSO regression, and multivariate logistic regression analysis in sequence to screen for the best predictive factors. Then, we constructed a clinical prediction model and plotted it into a column chart for personalized prediction. Finally, we evaluate the performance and clinical practicality of the model based on the area under curve (AUC), calibration curve, and decision curve. Multivariate logistic regression analysis found that variance and the 75th percentile were independent risk factors for PNI, while maximum and variance were independent risk factors for LVI. The clinical prediction model constructed based on the above factors has an AUC of 0.734 (95% CI: 0.591-0.878) for PNI in the training set and 0.731 (95% CI: 0.509-0.952) in the validation set; The training set AUC of LVI is 0.701 (95% CI: 0.561-0.841), and the validation set AUC is 0.685 (95% CI: 0.439-0.932). External validation showed an AUC of 0.722 (95% CI: 0.565-0.878) for PNI; and an AUC of 0.706 (95% CI: 0.481-0.931) for LVI. This study indicates that the combination of enhanced T1WI full volume histogram and clinical prediction model can be used to predict the perineural and lymphovascular invasion status of rectal cancer before surgery, providing valuable reference information for clinical diagnosis.

Efficacy of compressed sensing and deep learning reconstruction for adult female pelvic MRI at 1.5 T

BackgroundWe aimed to determine the capabilities of compressed sensing (CS) and deep learning reconstruction (DLR) with those of conventional parallel imaging (PI) for improving image quality while reducing examination time on female pelvic 1.5-T magnetic resonance imaging (MRI).MethodsFifty-two consecutive female patients with various pelvic diseases underwent MRI with T1- and T2-weighted sequences using CS and PI. All CS data was reconstructed with and without DLR. Signal-to-noise ratio (SNR) of muscle and contrast-to-noise ratio (CNR) between fat tissue and iliac muscle on T1-weighted images (T1WI) and between myometrium and straight muscle on T2-weighted images (T2WI) were determined through region-of-interest measurements. Overall image quality (OIQ) and diagnostic confidence level (DCL) were evaluated on 5-point scales. SNRs and CNRs were compared using Tukey’s test, and qualitative indexes using the Wilcoxon signed-rank test.ResultsSNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.010). CNRs of T1WI and T2WI obtained using CS with DLR were higher than those using CS without DLR or conventional PI (p < 0.003). OIQ of T1WI and T2WI obtained using CS with DLR were higher than that using CS without DLR or conventional PI (p < 0.001). DCL of T2WI obtained using CS with DLR was higher than that using conventional PI or CS without DLR (p < 0.001).ConclusionCS with DLR provided better image quality and shorter examination time than those obtainable with PI for female pelvic 1.5-T MRI.Relevance statementCS with DLR can be considered effective for attaining better image quality and shorter examination time for female pelvic MRI at 1.5 T compared with those obtainable with PI.Key PointsPatients underwent MRI with T1- and T2-weighted sequences using CS and PI.All CS data was reconstructed with and without DLR.CS with DLR allowed for examination times significantly shorter than those of PI and provided significantly higher signal- and CNRs, as well as OIQ.Graphical

Prediction of H3K27M alteration in midline gliomas of brain using radiomics- A multi-institute study

Abstract Background Non-invasive prediction of H3K27M-altered Diffuse midline gliomas is important because of the involvement of deep locations and proximity to eloquent structures. We aim to predict H3K27M alteration in midline gliomas using radiomics features of T2W images. Methods Radiomics features extracted from 124 subjects (69 H3K27M-altered/55 H3K27M-wildtype). T2W-images were resampled to 1x1x1mm voxel-size, pre-processed, and normalized for artefact correction, intensity variations. Feature-set was normalized, subjected to reduction by variance thresholding, correlation coefficient thresholding, and sequential feature selector. Adaptive synthesis oversampling technique was used to over-sample the training data. Random forest classifier (RFC), Decision tree classifier (DTC) and K-nearest neighbors classifier (KNN) were trained over the training dataset and the performance was assessed over the internal test dataset and external test data set (52 subjects: 33 H3K27M-altered/19-H3K27M-wildtype). Results DTC achieved validation score of 77.33% (5-fold cross-validation) and accuracy of 80.64%, 75% on internal and external test dataset. RFC achieved validation score of 80.7% (5-fold cross-validation) and accuracy of 80.6%, 73% on internal and external test dataset. DTC achieved validation score of 78.67% (5-fold cross-validation) and accuracy of 80.64%, 61.53% on internal and external test dataset. Accuracy score of DTC, RFC, and KNN on internal test dataset was approximately 80% while on the external test dataset, DTC achieved 75% accuracy, RFC achieved 73% accuracy and KNN achieved 65.1% accuracy. Conclusion H3K27M alteration is a potential immunotherapeutic marker and is associated with poor prognosis and radiomics features extracted from conventional T2W-images can help in identifying H3K27M-altered cases non-invasively with high precision.

Hypertension and cerebral blood flow in the development of Alzheimer's disease.

We investigated the interactive associations between amyloid and hypertension on the entorhinal cortex (EC) tau and atrophy and the role of cerebral blood flow (CBF) as a shared mechanism by which amyloid and hypertension contribute to EC tau and regional white matter hyperintensities (WMHs). We analyzed data from older adults without dementia participating in the Add-Tau study (NCT02958670, n=138) or Alzheimer's Disease Neuroimaging Initiative (ADNI) (n=523) who had available amyloid-positron emission tomography (PET), tau-PET, fluid-attenuated inversion recovery (FLAIR), and T1-weighted magnetic resonance imaging (MRI). A subsample in both cohorts had available arterial spin labeling (ASL) MRI (Add-Tau: n=78; ADNI: n=89). The detrimental effects of hypertension on AD pathology and EC thickness were more pronounced in the Add-Tau cohort. Increased amyloid burden was associated with decreased occipital gray matter CBF in the ADNI cohort. In both cohorts, lower regional gray matter CBF was associated with higher EC tau and posterior WMH burden. Reduced cerebral perfusion may be one common mechanism through which hypertension and amyloid are related to increased EC tau and WMH volume. Hypertension is associated with increased entorhinal cortex (EC) tau, particularly in the presence of amyloid. Decreased cortical cerebral blood flow (CBF) is associated with higher regional white matter hyperintensity volume. Increasing amyloid burden is associated with decreasing CBF in the occipital lobe. MTL CBF and amyloid are synergistically associated with EC tau.

Comparative analysis of cortical anatomy inmale participants with internet gaming disorder or tobacco use disorder: Insights fromnormative modeling.

Research on individual differences in brain structural features of internet gaming disorder (IGD) and established addictions such as tobacco use disorder (TUD) is currently limited. This study utilized normative modeling to analyze the cortical thickness (CT) development patterns of male patients with IGD and TUD, aiming to provide further insights into whether IGD qualifies as an addiction. Surface-based brain morphometry (SBM) was used to calculate CT from T1-weighted magnetic resonance imaging data of 804 male participants (665 healthy individuals, 68 IGD and 71 TUD). Gaussian process regression was employed to generate normative models of CT development. Deviation maps were produced to depict deviations of IGD and TUD participants from the typical developmental patterns. Both addiction groups exhibited widespread cortical thinning, particularly in regions such as the bilateral temporal pole and medial orbitofrontal cortex. The TUD group demonstrated a higher degree of individualization and limited spatial overlap compared to the IGD group. Opposite trends in CT changes were observed between the two groups in the bilateral pericalcarine cortex and pars triangularis. These findings regarding the similarities and differences between IGD and TUD provide support for the idea that IGD shares common features with substance-related addictions and contribute to a deeper understanding of the neural mechanisms underlyingIGD.

The impact of benign tissue within cancerous regions in the prostate: Characterizing sparse and dense prostate cancers on whole-mount histopathology and on multiparametric MRI

PurposeTo establish the incidence, size, zonal location and Gleason Score(GS)/Gleason Grade Group(GG) of sparse versus dense prostate cancer (PCa) lesions and to identify the imaging characteristics of sparse versus dense cancers on multiparametric MRI (mpMRI). MethodsSeventy-six men with untreated PCa were scanned prior to prostatectomy with endorectal-coil 3 T MRI including T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced MRI. Cancerous regions were outlined and graded on the whole-mount, processed specimens, with tissue compositions estimated. Regions with cancer comprising <50 % and ≥ 50 % of the tissue were considered sparse and dense respectively. Regions of interest (ROI) were manually drawn on T2-weighted MRI. Within each patient, area-weighted ROI averages were calculated for each imaging measure for each tissue type, GS/GG, and sparse/dense composition. ResultsA large number of cancer regions were identified on histopathology (n = 1193: 939 (peripheral zone (PZ)) and 254 (transition zone (TZ))). Thirty-seven percent of these lesions were sparse. Sparse lesions were primarily low-grade with the majority of PZ and 100 % of TZ sparse lesions ≤GS3 + 3/GG1. Dense lesions were significantly larger than sparse lesions in both PZ and TZ, p < 0.0001. On imaging, 246/45 PZ and 109/8 TZ dense/sparse 2D cancerous ROIs were drawn. Sparse GS3 + 3 and sparse ≥GS3 + 4 cancers did not have significantly different MRI intensities to dense GS3 + 3 cancers, while sparse GS3 + 3/GG1 cancers differed from benign, p < 0.05. ConclusionHistopathologically identified prostate cancer lesions were sparse in 37 % of cases. Sparse cancers were entirely low grade in TZ and predominantly low-grade in PZ and generally small, thus likely posing lower risk for spread and progression than dense lesions. Sparse lesions were not distinguishable from dense lesions on mpMRI, but could be distinguished from benign tissues.

A parasitic leiomyoma of the sigmoid mesentery with schwannoma-like image findings

BackgroundParasitic leiomyoma (PL) consists of uterine fibroids separate from the uterus that grow in extrauterine tissues such as the peritoneum and mesenterium. The diagnosis of PL requires a thorough medical history of laparoscopic myomectomies using a morcellator and the identification of typical magnetic resonance imaging (MRI) findings as uterine fibroids. Imaging diagnosis of PL is occasionally difficult when PL degenerates in various ways, owing to atypical findings on computed tomography (CT) and MRI.Case presentationA 29-year-old woman with a history of laparoscopic myomectomy visited a local hospital with lower abdominal pain. A mesenteric tumor on the sigmoid mesentery was suspected on MRI, and she was referred to our hospital. CT scan showed strong early contrast uptake in the center of the tumor, and MRI T2-weighted images showed high signals at the tumor margins and low signals in the center, suggesting a schwannoma. PL was also part of the differential diagnosis because of the patient’s history of laparoscopic myomectomy. With a preoperative diagnosis of a sigmoid colon mesenteric tumor undeniably of malignant origin, laparoscopic resection of the sigmoid mesenteric tumor was performed. Histopathological examination revealed it to be a PL.ConclusionsWe report a case of PL of the sigmoid mesentery with schwannoma-like findings on imaging that was treated laparoscopically. PL is sometimes difficult to distinguish from schwannomas because of the variety of imaging findings, such as uterine fibroids. PL should be considered in the differential diagnosis of mesenteric tumors following laparoscopic myomectomies, even if it does not show typical imaging findings, such as uterine fibroids.

Diagnostic accuracy of AI for bpMRI screening of prostate cancer: a systematic review and meta-analysis

The primary objective of this systematic literature review and meta-analysis is to evaluate the quality of prognostic models created for screening of prostate cancer (PCa). Methods: The systematic search of publications from January 2019 to September 2023 in the electronic databases ELibrary, PubMed, Google Scholar, Web of Science and Research Gate was used in accordance with the PRISMA protocol. Two authors independently assessed the need for inclusion or exclusion of the relevant studies Results: This meta-analysis included 21 studies. In total, 3,630 patients, of which 47% were with prostate cancer and 53% with benign prostate tumors. The average age of patients was 67.1 (mainly from 36 to 90 years). Eighty one percent (81%) of studies were based on T2-weighted imaging (T2-WI), 57% on diffusion-weighted imaging (DWI), and 76% on the apparent diffusion coefficients imaging (ADC). Forty three percent (43%) of studies were devoted to a malignancy formation in the transitional zone (TZ), 33% to the peripheral zone (PZ) of the prostate gland. Fifty two percent (52%) of authors conducted research on the entire organ, without dividing it into zones. The analysis showed that the researchers used machine learning (ML) algorithms: MLR (multiple logistic regression, in 76%), SVM (support vector machine, in 38%) and RF (random forest, in 24%). According to a meta-analysis of ROC-AUC assessment in 73 prognostic models described in the publications we studied, using methodological random effects, a final ROC-AUC value of 0.793 [95%CI 0.768; 0.818], I2 = 86.71%, p0.001. The most predictive models are based on T2-WI + ADC protocol: 0.860 [95%CI 0.813; 0.907], and those models that were created according to the “white box” principle (0.834 [95%CI 0.806; 0.861]). For comparison the values for “black box” are (0.733 [95%CI 0.695; 0.771]). Models using MRI and physiological features were slightly more accurate than the MRI parameters alone (0.869 [95% CI 0.844, 0.895] vs. 0.779 [95% CI 0.751, 0.807]). Model accuracy was virtually the same across PZ and/or TZ studies. Conclusion: The results reveal the most promising AI models. However, the clinical applicability may require more rigorous institutional validation and evaluation of efficacy in the prospective studies.

Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver disease etiologies

Image segmentation of the liver is an important step in treatment planning for liver cancer. However, manual segmentation at a large scale is not practical, leading to increasing reliance on deep learning models to automatically segment the liver. This manuscript develops a generalizable deep learning model to segment the liver on T1-weighted MR images. In particular, three distinct deep learning architectures (nnUNet, PocketNet, Swin UNETR) were considered using data gathered from six geographically different institutions. A total of 819 T1-weighted MR images were gathered from both public and internal sources. Our experiments compared each architecture’s testing performance when trained both intra-institutionally and inter-institutionally. Models trained using nnUNet and its PocketNet variant achieved mean Dice-Sorensen similarity coefficients>0.9 on both intra- and inter-institutional test set data. The performance of these models suggests that nnUNet and PocketNet liver segmentation models trained on a large and diverse collection of T1-weighted MR images would on average achieve good intra-institutional segmentation performance.

Ultrashort-Echo-Time MRI of the Disco-Vertebral Junction: Modulation of Image Contrast via Echo Subtraction and Echo Times.

The disco-vertebral junction (DVJ) of the lumbar spine contains thin structures with short T2 values, including the cartilaginous endplate (CEP) sandwiched between the bony vertebral endplate (VEP) and the nucleus pulposus (NP). We previously demonstrated that ultrashort-echo-time (UTE) MRI, compared to conventional MRI, is able to depict the tissues at the DVJ with improved contrast. In this study, we sought to further optimize UTE MRI by characterizing the contrast-to-noise ratio (CNR) of these tissues when either single echo or echo subtraction images are used and with varying echo times (TEs). In four cadaveric lumbar spines, we acquired 3D Cones (a UTE sequence) images at varying TEs from 0.032 ms to 16 ms. Additionally, spin echo T1- and T2-weighted images were acquired. The CNRs of CEP-NP and CEP-VEP were measured in all source images and 3D Cones echo subtraction images. In the spin echo images, it was challenging to distinguish the CEP from the VEP, as both had low signal intensity. However, the 3D Cones source images at the shortest TE of 0.032 ms provided an excellent contrast between the CEP and the VEP. As the TE increased, the contrast decreased in the source images. In contrast, the 3D Cones echo subtraction images showed increasing CNR values as the second TE increased, reaching statistical significance when the second TE was above 10 ms (p < 0.05). Our study highlights the feasibility of incorporating UTE MRI for the evaluation of the DVJ and its advantages over conventional spin echo sequences for improving the contrast between the CEP and adjacent tissues. Additionally, modulation of the contrast for the target tissues can be achieved using either source images or subtraction images, as well as by varying the echo times.

BrainSuite BIDS App: Containerized Workflows for MRI Analysis.

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

MRI-measured periprostatic to subcutaneous adipose tissue thickness ratio as an independent risk factor in prostate cancer patients undergoing radical prostatectomy

The purpose of this study is to evaluate whether the periprostatic adipose tissue thickness (PPATT) is an independent prognostic factor for prostate cancer patients after laparoscopic radical prostatectomy (LRP). This retrospective cohort study included consecutive prostate cancer patients who underwent LRP treatment at Wuhan Union Hospital from June 2, 2016, to September 7, 2023. PPATT was defined as the thickness of periprostatic fat and was obtained by measuring the shortest vertical distance from the pubic symphysis to the prostate on the midsagittal T2-weighted MR images. Subcutaneous adipose tissue thickness (SATT) was obtained by measuring the shortest vertical distance from the pubic symphysis to the skin at the same slice with PPATT. The primary outcome of the study was biochemical recurrence (BCR), and the secondary outcome was overall survival (OS). Multivariable Cox regression analysis was used to identify independent prognostic factors for prostate cancer survival and prognosis. Based on the optimal cutoff value, 162 patients were divided into a low PPATT/SATT group (n = 82) and a high PPATT/SATT group (n = 80). During the entire follow-up period (median 23.5 months), 26 patients in the high PPATT/SATT group experienced BCR (32.5%), compared to 18 in the low PPATT/SATT group (22.0%). Kaplan–Meier curve analysis indicated that the interval to BCR was significantly shorter in the high PPATT/SATT group (P = 0.037). Multivariable Cox regression analysis revealed that an increase in the PPATT/SATT ratio was associated with BCR (hazard ratio: 1.90, 95% CI, 1.03–3.51; P = 0.040). The PPATT/SATT ratio is a significant independent risk factor for BCR after LRP for prostate cancer patients.

Association of open skill exercise and long-chain polyunsaturated fatty acid intake with brain volume changes among older community-dwelling Japanese individuals

Considering that a multifactorial lifestyle approach may prove more effective than a single factor approach to improve or maintain brain health, we evaluated the association of exercise (open skill exercise [OSE] or closed skill exercise [CSE]) combined with long-chain polyunsaturated fatty acid (LCPUFAs) (docosahexaenoic acid [C22:6n-3, DHA], eicosapentaenoic acid [C20:5n-3, EPA], and arachidonic acid [C20:4n-6, ARA]) intake with brain atrophy among older Japanese individuals (n = 795, aged 60–88 years) without a self-reported history of dementia based on the datasets of a two-year longitudinal study. Brain volumes were measured using three-dimensional T1-weighted brain magnetic resonance imaging for follow-up periods of two years. The associations between multivariate-adjusted changes in brain volumes and OSE or CSE frequency (≥ once/month and < once/month) along with LCPUFA intake (≥ median and < median) at the baseline were assessed using a general linear model. Subgroup analysis was performed by restricting DHA and EPA intakes (n = 263; median, 323 mg/d), which represented levels similar to those in countries with low fish consumption. Higher OSE frequencies, ARA intakes, and their combination were inversely associated with decreases in total gray matter and frontal cortex volumes. In subgroup analysis, a combination of higher OSE frequencies and DHA intakes was also associated with a smaller decrease in total gray matter volume. Overall, our findings suggest that regular OSE engagement and appropriate LCPUFA intake may contribute to preventing brain volume decreases in older individuals.

Clinical study on improving the diagnostic accuracy of adult elbow joint cartilage injury by multisequence magnetic resonance imaging.

Due to frequent and high-risk sports activities, the elbow joint is susceptible to injury, especially to cartilage tissue, which can cause pain, limited movement and even loss of joint function. To evaluate magnetic resonance imaging (MRI) multisequence imaging for improving the diagnostic accuracy of adult elbow cartilage injury. A total of 60 patients diagnosed with elbow cartilage injury in our hospital from January 2020 to December 2021 were enrolled in this retrospective study. We analyzed the accuracy of conventional MRI sequences (T1-weighted imaging, T2-weighted imaging, proton density weighted imaging, and T2 star weighted image) and Three-Dimensional Coronary Imaging by Spiral Scanning (3D-CISS) in the diagnosis of elbow cartilage injury. Arthroscopy was used as the gold standard to evaluate the diagnostic effect of single and combination sequences in different injury degrees and the consistency with arthroscopy. The diagnostic accuracy of 3D-CISS sequence was 89.34% ± 4.98%, the sensitivity was 90%, and the specificity was 88.33%, which showed the best performance among all sequences (P < 0.05). The combined application of the whole sequence had the highest accuracy in all sequence combinations, the accuracy of mild injury was 91.30%, the accuracy of moderate injury was 96.15%, and the accuracy of severe injury was 93.33% (P < 0.05). Compared with arthroscopy, the combination of all MRI sequences had the highest consistency of 91.67%, and the kappa value reached 0.890 (P < 0.001). Combination of 3D-CISS and each sequence had significant advantages in improving MRI diagnostic accuracy of elbow cartilage injuries in adults. Multisequence MRI is recommended to ensure the best diagnosis and treatment.

Effect of Dialysis on Structural Brain Connectivity in Patients with End-Stage Renal Disease

Introduction: Patients with end-stage renal disease (ESRD) are known to have reduced structural and functional brain connectivity in the brain regions associated with cognitive function. However, the effect of dialysis on brain connectivity remains unclear. This study aimed to evaluate the effects of dialysis on structural brain connectivity in patients with ESRD. Methods: This prospective study included 20 patients with ESRD in the pre-dialysis stage and 35 healthy controls. The patients underwent T2-weighted and three-dimensional T1-weighted magnetic resonance imaging before and 3 months after dialysis initiation. Moreover, the cortical thickness was calculated. We applied graph theoretical analysis to calculate the structural covariance network based on cortical thickness. We compared the cortical thickness and structural covariance network of patients with ESRD in the pre-dialysis stage with those of healthy controls and with those of patients with ESRD in the post-dialysis stage. Results: The mean cortical thickness in both hemispheres was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (2.296 vs. 2.354, p = 0.030; 2.282 vs. 2.362, p = 0.004, respectively) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (2.333 vs. 2.296, p = 0.001; 2.322 vs. 2.282, p = 0.002, respectively). Analysis of the structural covariance network revealed that the assortative coefficient was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (−0.062 vs. −0.031, p = 0.029) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (−0.002 vs. −0.062, p = 0.042). Conclusion: We observed differences in the cortical thickness and structural covariance networks before and after dialysis in patients with ESRD. This indicates that dialysis affects structural brain connectivity, contributing to the understanding of the pathophysiological mechanism of cognitive function alterations resulting from dialysis in patients with ESRD.

Multi-modality radiomics of conventional T1 weighted and diffusion tensor imaging for differentiating Parkinson’s disease motor subtypes in early-stages

This study aimed to develop and validate a multi-modality radiomics approach using T1-weighted and diffusion tensor imaging (DTI) to differentiate Parkinson's disease (PD) motor subtypes, specifically tremor-dominant (TD) and postural instability gait difficulty (PIGD), in early disease stages. We analyzed T1-weighted and DTI scans from 140 early-stage PD patients (70 TD, 70 PIGD) and 70 healthy controls from the Parkinson's Progression Markers Initiative database. Radiomics features were extracted from 16 brain regions of interest. After harmonization and feature selection, four machine learning classifiers were trained and evaluated for both three-class (HC vs TD vs PIGD) and binary (TD vs PIGD) classification tasks. The light gradient boosting machine (LGBM) classifier demonstrated the best overall performance. For the three-class classification, LGBM achieved an accuracy of 85% and an area under the receiver operating characteristic curve (AUC) of 0.94 using combined T1 and DTI features. In the binary classification task, LGBM reached an accuracy of 95% and AUC of 0.95. Key discriminative features were identified in the Thalamus, Amygdala, Hippocampus, and Substantia Nigra for the three-group classification, and in the Pallidum, Amygdala, Hippocampus, and Accumbens for binary classification. The combined T1 + DTI approach consistently outperformed single-modality classifications, with DTI alone showing particularly low performance (AUC 0.55–0.62) in binary classification. The high accuracy and AUC values suggest that this approach could significantly improve early diagnosis and subtyping of PD. These findings have important implications for clinical management, potentially enabling more personalized treatment strategies based on early, accurate subtype identification.

Radiogenomics based survival prediction of small-sample glioblastoma patients by multi-task DFFSP model.

In this paper, the multi-task dense-feature-fusion survival prediction (DFFSP) model is proposed to predict the three-year survival for glioblastoma (GBM) patients based on radiogenomics data. The contrast-enhanced T1-weighted (T1w) image, T2-weighted (T2w) image and copy number variation (CNV) is used as the input of the three branches of the DFFSP model. This model uses two image extraction modules consisting of residual blocks and one dense feature fusion module to make multi-scale fusion of T1w and T2w image features as backbone. Also, a gene feature extraction module is used to adaptively weight CNV fragments. Besides, a transfer learning module is introduced to solve the small sample problem and an image reconstruction module is adopted to make the model anatomy-aware under a multi-task framework. 256 sample pairs (T1w and corresponding T2w MRI slices) and 187 CNVs of 74 patients were used. The experimental results show that the proposed model can predict the three-year survival of GBM patients with the accuracy of 89.1 %, which is improved by 3.2 and 4.7 % compared with the model without genes and the model using last fusion strategy, respectively. This model could also classify the patients into high-risk and low-risk groups, which will effectively assist doctors in diagnosing GBM patients.

Elastin-specific MR probe for visualization and evaluation of an interleukin-1β targeted therapy for atherosclerosis

Atherosclerosis is a chronic inflammatory condition of the arteries and represents the primary cause of various cardiovascular diseases. Despite ongoing progress, finding effective anti-inflammatory therapeutic strategies for atherosclerosis remains a challenge. Here, we assessed the potential of molecular magnetic resonance imaging (MRI) to visualize the effects of 01BSUR, an anti-interleukin-1β monoclonal antibody, for treating atherosclerosis in a murine model. Male apolipoprotein E-deficient mice were divided into a therapy group (01BSUR, 2 × 0.3 mg/kg subcutaneously, n = 10) and control group (no treatment, n = 10) and received a high-fat diet for eight weeks. The plaque burden was assessed using an elastin-targeted gadolinium-based contrast probe (0.2 mmol/kg intravenously) on a 3 T MRI scanner. T1-weighted imaging showed a significantly lower contrast-to-noise (CNR) ratio in the 01BSUR group (pre: 3.93042664; post: 8.4007067) compared to the control group (pre: 3.70679168; post: 13.2982156) following administration of the elastin-specific MRI probe (p < 0.05). Histological examinations demonstrated a significant reduction in plaque size (p < 0.05) and a significant decrease in plaque elastin content (p < 0.05) in the treatment group compared to control animals. This study demonstrated that 01BSUR hinders the progression of atherosclerosis in a mouse model. Using an elastin-targeted MRI probe, we could quantify these therapeutic effects in MRI.