Magnetic Resonance Imaging MRI Research Articles

Prospective evaluation of PI-RADSv2.1 using multiparametric and biparametric MRI for detecting clinically significant prostate cancer based on MRI/US fusion-guided biopsy.

To evaluate the cancer detection rates for each category of Prostate Imaging-Reporting and Data System version 2.1 (PI-RADSv2.1) using multiparametric magnetic resonance imaging (mpMRI) and biparametric MRI (bpMRI) based on MRI/ultrasound (US)-fusion biopsy. This prospective study included participants who underwent mpMRI or bpMRI with a PI-RADSv2.1 interpretation and subsequently received MRI/US-fusion biopsy between August 2022 and December 2023. The lesion-based detection rates of clinically significant prostate cancer (csPCa) in each PI-RADSv2.1 category and the correlation between PI-RADSv2.1 categories and International Society of Urological Pathology (ISUP) grade groups were analyzed. The diagnostic performance of PI-RADSv2.1 in predicting csPCa was evaluated, and diagnostic performance of mpMRI and bpMRI was compared using cut-offs, with PI-RADSv2.1 categories ≥ 3 or ≥ 4 defined as positive. A total of 247 lesions from 216 participants were included in this study. A total of 157 patients underwent mpMRI and the remaining 59 underwent bpMRI. The csPCa detection rates for each PI-RADSv2.1 category of mpMRI and bpMRI were as follows: category 1, 0% (0/11); 2, 13% (3/23); 3, 16% (5/31); 4, 60% (43/72); 5, 65% (26/40), in mpMRI; category 1, 0% (0/4); 2, 33% (1/3); 3, 25% (3/12); 4, 61% (19/31); 5, 75% (15/20) in bpMRI. PI-RADSv2.1 categories were significantly positively associated with csPCa detection rates in both mpMRI and bpMRI (p < 0.0001 and p = 0.00048, respectively). PI-RADSv2.1 categories correlated with ISUP grade groups for mpMRI and bpMRI (p < 0.0001 for both). There were no significant differences in the detection rates between mpMRI and bpMRI for PI-RADS v2.1 positive and negative lesions. PI-RADSv2.1 using mpMRI and bpMRI could stratify the risk of csPCa, and the csPCa detection rate of bpMRI was compatible with that of mpMRI using cut-offs of PI-RADSv2.1 categories ≥ 3 or ≥ 4.

Neuroradiologic, clinic and genetic characterization of cerebellar heterotopia: a pediatric multicentric study.

Background and purpose:Cerebellar heterotopia (CH) is a neuroradiological abnormality poorly reported and investigated in the literature. It can be observed as an isolated finding, but it has been mainly reported in the context of cerebellar dysgenesis and in syndromic conditions. The aim of this study is to provide a comprehensive neuroradiological, clinical, and genetic characterization of a cohort of pediatric patients with cerebellar heterotopia.Materials and methods:Patients with a diagnosis of CH were systematically selected from the neuroimaging databases of the four Italian Centers participating in this retrospective study. For each patient, information regarding demographic, clinical, genetic and neuroradiological data were collected.Results:Thirty-two pediatric patients were recruited and subdivided into two groups: patients with isolated CH and/or cerebellar malformations (n= 18) and patients with CH associated with cerebral malformations (n=14). Isolated CH consistently showed a peripheral subcortical localization in the inferior portion of cerebellar hemispheres, with either unilateral or bilateral distribution. Ten patients belonging to the second group had a diagnosis of CHARGE syndrome, and their nodules of CH were mainly but not exclusively bilateral, symmetric, located in the peripheral subcortical zone and in the inferior portion of the cerebellar hemispheres; the remaining 4 patients of the second group, showed either bilateral or unilateral CH, located in both peripheral cortex and deep white matter and in the superior and inferior portions of cerebellum. Patients with isolated CH showed high prevalence of language development delay; neurodevelopmental disorders were the most represented clinical diagnoses. Recurring features were behavioral problems and motor difficulties. A conclusive genetic diagnosis was found in 18/32 patients.Conclusions:We found distinctive neuroradiological patterns of CH. Genetic results raise the possibility of a correlation between cerebellar morphological and functional developmental disruption, underscoring the importance of CH detection and reporting to orient the diagnostic path.Abbreviations CH Cerebellar heterotopia; MRI Magnetic resonance imaging; CC Corpus callosum; ASD autism spectrum disorder; IVH inferior vermian hypoplasia.

Effect of apremilast on hand and whole-body MRI assessments of inflammation in patients with psoriatic arthritis (MOSAIC): a phase 4, multicentre, single-arm, open-label study.

The Psoriatic Arthritis Magnetic Resonance Imaging Scoring System (PsAMRIS) and MRI Whole-Body Scoring System for Inflammation in Peripheral Joints and Entheses in Inflammatory Arthritis (MRI-WIPE) have not been used together to assess treatment of psoriatic arthritis in a clinical trial. We aimed to assess the effect of apremilast treatment on inflammation, with outcomes measured by PsAMRIS and MRI-WIPE. MOSAIC was a phase 4, multicentre, single-arm, open-label study conducted at 29 sites across ten countries (Belgium, Canada, Denmark, Germany, Italy, Russia, Spain, Switzerland, the UK, and the USA). Adults aged 18 years or older with a documented diagnosis of psoriatic arthritis for a duration of 3 months to 5 years self-enrolled and were included if they met the classification criteria for active psoriatic arthritis at screening. Patients were required to have at least three swollen and three tender joints with hand involvement and at least one active enthesitis site according to the Spondyloarthritis Research Consortium of Canada enthesitis index or the Leeds enthesitis index. Patients were excluded if they had previous treatment with a biological disease-modifying antirheumatic drug or previous treatment with more than two conventional synthetic disease-modifying antirheumatic drugs. After a 5-day titration period, patients received apremilast 30 mg orally twice per day. Concomitant stable methotrexate up to 25 mg per week was permitted. The primary endpoint was change from baseline to week 24 in a composite inflammation score of bone marrow oedema, synovitis, and tenosynovitis in the hand as assessed by PsAMRIS. The full analysis set and safety population included all enrolled patients who received at least one dose of apremilast. This completed study is registered with ClinicalTrials.gov (NCT03783026). Between Feb 6, 2019, and May 11, 2022, 123 patients were enrolled in the MOSAIC study. Of these 123 patients, 122 (99%) were treated with apremilast and included in the full analysis set and safety population. 67 (55%) of 122 patients were female, 55 (45%) were male, and 116 (95%) were White. 80 (66%) of 122 patients completed 48 weeks of treatment. The least squares mean change from baseline to week 24 in the composite inflammation score of bone marrow oedema, synovitis, and tenosynovitis as assessed by PsAMRIS was -2·32 (95% CI -4·73 to 0·09). 95 (78%) of 122 patients had at least one treatment-emergent adverse event. Six (5%) patients had a severe treatment-emergent adverse event and six (5%) patients had a serious treatment-emergent adverse event. No serious treatment-emergent adverse events were considered to be related to apremilast. Apremilast improved inflammation in joints and entheses on assessment of MRI measures in the hand and the whole body. Our findings encourage the use of MRI, including whole-body MRI, as an objective outcome measure in trials in patients with psoriatic arthritis. Amgen.

Utility of dynamic contrast enhancement for clinically significant prostate cancer detection.

This study aimed to evaluate the association of dynamic contrast enhancement (DCE) with clinically significant prostate cancer (csPCa, Gleason Grade Group ≥2) and compare biparametric magnetic resonance imaging (bpMRI) and multiparametric MRI (mpMRI) nomograms. We identified a retrospective cohort of biopsy naïve patients who underwent pre-biopsy MRI separated by individual MRI series from 2018 to 2022. csPCa detection rates were calculated for patients with peripheral zone (PZ) lesions scored 3-5 on diffusion weighted imaging (DWI) with available DCE (annotated as - or +). bpMRI Prostate Imaging Reporting and Data System (PIRADS) (3 = 3-, 3+; 4 = 4-, 4+; 5 = 5-, 5+) and mpMRI PIRADS (3 = 3-; 4 = 3+, 4-, 4+; 5 = 5-, 5+) approaches were compared in multivariable logistic regression models. Nomograms for detection of csPCa and ≥GG3 PCa incorporating all biopsy naïve patients who underwent prostate MRI were generated based on available serum biomarkers [PHI, % free prostate-specific antigen (PSA), or total PSA] and validated with an independent cohort. Patients (n = 1010) with highest PIRADS lesion in PZ were included in initial analysis with 127 (12.6%) classified as PIRADS 3+ (PIRADS 3 on bpMRI but PIRADS 4 on mpMRI). On multivariable analysis, PIRADS 3+ lesions were associated with higher csPCa rates compared to PIRADS 3- (3+ vs. 3-: OR 1.86, p= 0.024), but lower csPCa rates compared to PIRADS DWI 4 lesions (4 vs. 3+: OR 2.39, p< 0.001). csPCa rates were 19% (3-), 31% (3+), 41.5% (4-), 65.9% (4+), 62.5% (5-), and 92.3% (5+). bpMRI nomograms were non-inferior to mpMRI nomograms in the development (n= 1410) and independent validation (n= 353) cohorts. Risk calculators available at: https://rossnm1.shinyapps.io/MynMRIskCalculator/. While DCE positivity by itself was associated with csPCa among patients with highest PIRADS lesions in the PZ, nomogram comparisons suggest that there is no significant difference in performance of bpMRI and mpMRI. bpMRI may be considered as an alternative to mpMRI for prostate cancer evaluation in many situations.

Structure-function interrelationships and associated neurotransmitter profiles in drug-naïve benign childhood epilepsy with central-temporal spikes patients.

To explore the interrelationships between structural and functional changes as well as the potential neurotransmitter profile alterations in drug-naïve benign childhood epilepsy with central-temporal spikes (BECTS) patients. Structural magnetic resonance imaging (sMRI) and resting-state functional MRI data from 20 drug-naïve BECTS patients and 33 healthy controls (HCs) were acquired. Parallel independent component analysis (P-ICA) was used to identify covarying components among gray matter volume (GMV) maps and fractional amplitude of low-frequency fluctuations (fALFF) maps. Furthermore, we explored the spatial correlations between GMV/fALFF changes derived from P-ICA and neurotransmitter maps in JuSpace toolbox. A significantly positive correlation (p < 0.001) was identified between one structural component (GMV_IC6) and one functional component (fALFF_IC4), which showed significant group differences between drug-naïve BECTS patients and HCs (GMV_IC6: p < 0.01; fALFF_IC4: p < 0.001). GMV_IC6 showed increased GMV in the frontal lobe, temporal lobe, thalamus, and precentral gyrus as well as fALFF_IC4 had enhanced fALFF in the cerebellum in drug-naïve BECTS patients compared to HCs. Moreover, significant correlations between GMV alterations in GMV_IC6 and the serotonin (5HT1a: p < 0.001; 5HT2a: p < 0.001), norepinephrine (NAT: p < 0.001) and glutamate systems (mGluR5: p < 0.001) as well as between fALFF alterations in fALFF_IC4 and the norepinephrine system (NAT: p < 0.001) were detected. The current findings suggest co-altered structural/functional components that reflect the correlation of language and motor networks as well as associated with the serotonergic, noradrenergic, and glutamatergic neurotransmitter systems. The relationship between anatomical brain structure and intrinsic neural activity was evaluated using a multimodal fusion analysis and neurotransmitters which might provide an important window into the multimodal neural and underlying molecular mechanisms of benign childhood epilepsy with central-temporal spikes. Structure-function relationships in drug-naïve benign childhood epilepsy with central-temporal spikes (BECTS) patients were explored. The interrelated structure-function components were found and correlated with the serotonin, norepinephrine, and glutamate systems. Co-altered structural/functional components reflect the correlation of language and motor networks and correlate with the specific neurotransmitter systems.

Comprehensive analysis of stereotactic Radiosurgery outcomes in triple-negative breast cancer patients with brain metastases: The influence of immunotherapy and prognostic factors

IntroductionBreast cancer stands as the second most common solid tumors with a propensity for brain metastasis. Among metastatic breast cancer cases, the brain metastasis incidence ranges from 10 % to 30 %, with triple-negative breast cancer (TNBC) displaying a heightened risk and poorer prognosis. SRS has emerged as an effective local treatment modality for brain metastases; however, data on its outcomes specifically in pure triple-negative subtype remain scarce. MethodWe retrospectively reviewed the electronic medical records of all brain metastasis (BM) TNBC patients treated with SRS. Patient, tumour characteristics and treatment details data were collected. This retrospective cohort study aimed to evaluate local control (LC), distant brain metastasis free survival (DBMFS), and overall survival (OS) outcomes in TNBC patients undergoing SRS for brain metastases while identifying potential prognostic factors. ResultForty-three patients with TNBC and brain metastases treated with SRS between January 2017 and 2023 were included. The study found rates of LC (99 % at 1 year) and DBMFS (76 % at 1 year) after SRS, with brain metastasis count (p = 0,003) and systemic treatment modality (p = 0,001) being significant predictors of DBMFS. The median OS following SRS was 19.5 months, with neurological deficit (p = 0.003) and systemic treatment modality (p = 0.019) identified as significant predictors of OS. ConclusionSRS demonstrates favourable outcomes in terms of local control and distant brain metastasis-free survival in TNBC. Neurological deficit and systemic treatment significantly influence overall survival, emphasizing the importance of personalized treatment approaches and (magnetic resonance imaging) MRI surveillance based on these factors.

Diffusion tensor imaging in differentiation of spinal cord lesions

Background Spinal lesions sometimes remain insufficiently visualized by ‘conventional’ magnetic resonance imaging techniques; indeed very often the lesions seen on T2 weighted images do not completely display the effects of pathology on the long tracts. Diffusion imaging, which enables the imaging of molecular water motion, has been applied recently to spinal cord diseases Aim The purpose of this study was to assess the role of Diffusion tensor imaging in in differentiation between different spinal cord lesions Patients &amp; methods This prospective study included 30 patients with myelopathy symptoms. All patients were subjected to full history taking, clinical examination, magnetic resonance imaging and diffusion tensor imaging MRI. Results The mean FA values at the site of the lesion was significantly lower than the normal appearing spinal cord in inflammation/demyelination, compressive myelopathy and traumatic spinal cord injury groups while the mean apparent diffusion coefficient values at the site of the lesions was significantly higher than the normal appearing spinal cord in inflammation/demyelination group. FA was sensitive than apparent diffusion coefficient in the detection of the spinal cord abnormalities with a sensitivity of 90% versus 60% respectively. Conclusion Diffusion tensor imaging with its quantitative indices is a non-invasive tool that is used in functional assessment of normal and pathologic spinal cord white matter, giving detailed information about different pathology and helping early management.

A machine learning radiomics model based on bpMRI to predict bone metastasis in newly diagnosed prostate cancer patients.

ObjectivesTo develop and evaluate a machine learning radiomics model based on bpMRI to predict bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients. MethodsWe retrospectively analyzed biparametric magnetic resonance imaging MRI (bpMRI) scans of PCa patients from multiple centers between January 2016 and October 2021. 348 PCa patients were recruited from two institutions for this study. The first institution contributed 284 patients, stratified and randomly divided into training and internal validation cohorts at a 7:3 ratio. The remaining 64 patients were sourced from the second institution and comprised the external validation cohort. Radiomics features were extracted from axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) tumor regions. We developed the radiomics prediction model for BM in the training cohort and validated it in the internal and external validation cohorts. As a benchmark, we trained the logistic regression model with lasso feature reduction (LFR-LRM) in the training cohort and further compared it with Naive Bayes, eXtreme Gradient Boosting (XGboost), Random Forest (RF), GBDT, SVM, Adaboost, and KNN algorithms and validated in both the internal and external cohorts. The performance of several predictive models was assessed by receiver operating characteristic (ROC). ResultsThe LFR-LRM model achieved an area under the receiver operating characteristic curve (AUC) of 0.89 (95% CI: 0.822–0.974) and an accuracy of 0.828 (95% CI: 0.713–0.911). The AUC and accuracy in external validation were 0.866 (95% CI: 0.784–0.948) and 0.769 (95% CI: 0.648–0.864), respectively. The RF and XGBoost models outperformed the LFR-LRM, with AUCs of 0.907 (95% CI: 0.863–0.949) and 0.928 (95% CI: 0.882–0.974) and accuracies of 0.831 (95% CI: 0.727–0.907) and 0.884 (95% CI: 0.792–0.946). External validation for these models yielded AUCs and accuracies of 0.911 (95% CI: 0.861–0.966), 0.921 (95% CI: 0.889–0.953), and 0.846 (95% CI: 0.735–0.923) and 0.876 (95% CI: 0.771–0.945), respectively. ConclusionsThe XGboost machine learning model is more accurate than LFR-LRM for predicting BM in patients with newly confirmed PCa.

A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease

BackgroundFunctional connectivity (FC) biomarkers play a crucial role in the early diagnosis and mechanistic study of Alzheimer’s disease (AD). However, the identification of effective FC biomarkers remains challenging. In this study, we introduce a novel approach, the spatiotemporal graph convolutional network (ST-GCN) combined with the gradient-based class activation mapping (Grad-CAM) model (STGC-GCAM), to effectively identify FC biomarkers for AD.MethodsThis multi-center cross-racial retrospective study involved 2,272 participants, including 1,105 cognitively normal (CN) subjects, 790 mild cognitive impairment (MCI) individuals, and 377 AD patients. All participants underwent functional magnetic resonance imaging (fMRI) and T1-weighted MRI scans. In this study, firstly, we optimized the STGC-GCAM model to enhance classification accuracy. Secondly, we identified novel AD-associated biomarkers using the optimized model. Thirdly, we validated the imaging biomarkers using Kaplan–Meier analysis. Lastly, we performed correlation analysis and causal mediation analysis to confirm the physiological significance of the identified biomarkers.ResultsThe STGC-GCAM model demonstrated great classification performance (The average area under the curve (AUC) values for different categories were: CN vs MCI = 0.98, CN vs AD = 0.95, MCI vs AD = 0.96, stable MCI vs progressive MCI = 0.79). Notably, the model identified specific brain regions, including the sensorimotor network (SMN), visual network (VN), and default mode network (DMN), as key differentiators between patients and CN individuals. These brain regions exhibited significant associations with the severity of cognitive impairment (p < 0.05). Moreover, the topological features of important brain regions demonstrated excellent predictive capability for the conversion from MCI to AD (Hazard ratio = 3.885, p < 0.001). Additionally, our findings revealed that the topological features of these brain regions mediated the impact of amyloid beta (Aβ) deposition (bootstrapped average causal mediation effect: β = -0.01 [-0.025, 0.00], p < 0.001) and brain glucose metabolism (bootstrapped average causal mediation effect: β = -0.02 [-0.04, -0.001], p < 0.001) on cognitive status.ConclusionsThis study presents the STGC-GCAM framework, which identifies FC biomarkers using a large multi-site fMRI dataset.

Epilepsy-related functional brain network alterations are already present at an early age in the GAERS rat model of genetic absence epilepsy.

Genetic Absence Epilepsy Rats from Strasbourg (GAERS) represent a model of genetic generalized epilepsy. The present longitudinal study in GAERS and age-matched non-epileptic controls (NEC) aimed to characterize the epileptic brain network using two functional measures, resting state-functional magnetic resonance imaging (rs-fMRI) and manganese-enhanced MRI (MEMRI) combined with morphometry, and to investigate potential brain network alterations, following long-term seizure activity. Repeated rs-fMRI measurements at 9.4 T between 3 and 8 months of age were combined with MEMRI at the final time point of the study. We used graph theory analysis to infer community structure and global and local network parameters from rs-fMRI data and compared them to brain region-wise manganese accumulation patterns and deformation-based morphometry (DBM). Functional connectivity (FC) was generally higher in GAERS when compared to NEC. Global network parameters and community structure were similar in NEC and GAERS, suggesting efficiently functioning networks in both strains. No progressive FC changes were observed in epileptic animals. Network-based statistics (NBS) revealed stronger FC within the cortical community, including regions of association and sensorimotor cortex, and with basal ganglia and limbic regions in GAERS, irrespective of age. Higher manganese accumulation in GAERS than in NEC was observed at 8 months of age, consistent with higher overall rs-FC, particularly in sensorimotor cortex and association cortex regions. Functional measures showed less similarity in subcortical regions. Whole brain volumes of 8 months-old GAERS were higher when compared to age-matched NEC, and DBM revealed increased volumes of several association and sensorimotor cortex regions and of the thalamus. rs-fMRI, MEMRI, and volumetric data collectively suggest the significance of cortical networks in GAERS, which correlates with an increased fronto-central connectivity in childhood absence epilepsy (CAE). Our findings also verify involvement of basal ganglia and limbic regions. Epilepsy-related network alterations are already present in juvenile animals. Consequently, this early condition seems to play a greater role in dynamic brain function than chronic absence seizures.

A noninvasive method for predicting clinically significant prostate cancer using magnetic resonance imaging combined with PRKY promoter methylation level: a machine learning study

BackgroundTraditional process for clinically significant prostate cancer (csPCA) diagnosis relies on invasive biopsy and may bring pain and complications. Radiomic features of magnetic resonance imaging MRI and methylation of the PRKY promoter were found to be associated with prostate cancer.MethodsFifty-four Patients who underwent prostate biopsy or photoselective vaporization of the prostate (PVP) from 2022 to 2023 were selected for this study, and their clinical data, blood samples and MRI images were obtained before the operation. Methylation level of two PRKY promoter sites, cg05618150 and cg05163709, were tested through bisulfite sequencing PCR (BSP). The PI-RADS score of each patient was estimated and the region of interest (ROI) was delineated by 2 experienced radiologists. After being extracted by a plug-in of 3D-slicer, radiomic features were selected through LASSCO regression and t-test. Selected radiomic features, methylation levels and clinical data were used for model construction through the random forest (RF) algorithm, and the predictive efficiency was analyzed by the area under the receiver operation characteristic (ROC) curve (AUC).ResultsMethylation level of the site, cg05618150, was observed to be associated with prostate cancer, for which the AUC was 0.74. The AUC of T2WI in csPCA prediction was 0.84, which was higher than that of the apparent diffusion coefficient ADC (AUC = 0.81). The model combined with T2WI and clinical data reached an AUC of 0.94. The AUC of the T2WI-clinic-methylation-combined model was 0.97, which was greater than that of the model combined with the PI-RADS score, clinical data and PRKY promoter methylation levels (AUC = 0.86).ConclusionsThe model combining with radiomic features, clinical data and PRKY promoter methylation levels based on machine learning had high predictive efficiency in csPCA diagnosis.

Speckle Noise Removal from Biomedical MRI Images and Classification by Multi-Support Vector Machine

INTRODUCTION: Image Processing (IP) methods play a vital role in medical images for diagnosing and predicting illness, as well as monitoring the patient's progress. The IP methods are utilized in many applications for example in the field of medicine. OBJECTIVES: The images that are obtained by the MRI magnetic Resonance imaging and x rays are analyzed with the help of image processing. METHODS: This application is very costly to the patient. Because of the several non-idealities in the image process, medical images are frequently tainted by impulsive, multiplicative, and addictive noise. RESULTS: By replacing some of the original image's pixels with new ones that have luminance values which are less than the allowed dynamic luminance range, noise frequently affects medical images. CONCLUSION: In this research work, the Speckle type noises are eliminated with the help of Mean Filter (MF) and classify the images using Multi-SVM classifier. The entire system developed using python programming.

The impact of pre-biopsy MRI and additional testing on prostate cancer screening outcomes: A rapid review.

This work aims to examine the latest evidence on the impact of pre-biopsy MRI, in addition to prostate-specific antigen (PSA) testing, on health outcomes and quality of life. We conducted a literature search including PubMed and Cochrane Central Register of Controlled Trials (CENTRAL) databases, with a limited scan of (i) guidelines and (ii) references from trial reports, from January 2005 to 25th January 2023. Two independent reviewers selected randomised controlled trials (RCT) and cohort studies which met our inclusion criteria. One hundred thirty-seven articles were identified, and seven trial articles were selected. Trial interventions were as follows: (i) PSA blood test, (ii) additional tests such as pre-biopsy multiparametric magnetic resonance imaging (mpMRI) and Biparametric MRI (bpMRI), and (iii) MRI targeted biopsy and standard biopsy. Compared with standard biopsy, MRI-based interventions led to increased detection of clinically significant cancers in three studies and decreased detection of clinically insignificant cancer (Gleason grade 3 + 3) in four studies. However, PROstate Magnetic resonance Imaging Study (PROMIS) and Stockholm3 with MRI (STHLM3-MRI) studies reported different trends depending on the scenario studied in PROMIS (MRI triage and MRI directed biopsy vs. MRI triage and standard biopsy) and thresholds used in STHLM3-MRI (≥0·11 and ≥0·15). MRI also helped 8%-49% of men avoid biopsy, in six out of seven studies, but not in STHLM3-MRI at ≥0.11. Interestingly, the proportion of men who experienced sepsis and UTI was low across studies. This review found that a combination of approaches, centred on the use of pre-biopsy MRI, may improve the detection of clinically significant cancers and reduce (i) the diagnosis of clinically insignificant cancers and (ii) unnecessary biopsies, compared with PSA testing and standard biopsy alone. However, the impact of such interventions on longer term outcomes such as prostate cancer-specific mortality has not yet been assessed.

The network characteristics in schizophrenia with prominent negative symptoms: a multimodal fusion study

Previous studies on putative neural mechanisms of negative symptoms in schizophrenia mainly used single modal imaging data, and seldom utilized schizophrenia patients with prominent negative symptoms (PNS).This study adopted the multimodal fusion method and recruited a homogeneous sample with PNS. We aimed to identify negative symptoms-related structural and functional neural correlates of schizophrenia. Structural magnetic resonance imaging (sMRI) and resting-state functional MRI (rs-fMRI) were performed in 31 schizophrenia patients with PNS and 33 demographically matched healthy controls.Compared to healthy controls, schizophrenia patients with PNS exhibited significantly altered functional activations in the default mode network (DMN) and had structural gray matter volume (GMV) alterations in the cerebello-thalamo-cortical network. Correlational analyses showed that negative symptoms severity was significantly correlated with the cerebello-thalamo-cortical structural network, but not with the DMN network in schizophrenia patients with PNS.Our findings highlight the important role of the cerebello-thalamo-cortical structural network underpinning the neuropathology of negative symptoms in schizophrenia. Future research should recruit a large sample and schizophrenia patients without PNS, and apply adjustments for multiple comparison, to verify our preliminary findings.

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it is a natural way to organise these data as tensors prior to performing automated analyses such as discrimination of neurological disorders like Parkinson's Disease (PD) and Attention Deficit and Hyperactivity Disorder (ADHD). However, the existing approaches are often subject to performance bottlenecks (e.g., conventional feature extraction and deep learning based feature construction), as these can lose the structural information that correlates multiple data dimensions or/and demands excessive empirical and application-specific settings. This study proposes a Deep Factor Learning model on a Hilbert Basis tensor (namely, HB-DFL) to automatically derive latent low-dimensional and concise factors of tensors. This is achieved through the application of multiple Convolutional Neural Networks (CNNs) in a non-linear manner along all possible dimensions with no assumed a priori knowledge. HB-DFL leverages the Hilbert basis tensor to enhance the stability of the solution by regularizing the core tensor to allow any component in a certain domain to interact with any component in the other dimensions. The final multi-domain features are handled through another multi-branch CNN to achieve reliable classification, exemplified here using MRI discrimination as a typical case. A case study of MRI discrimination has been performed on public MRI datasets for discrimination of PD and ADHD. Results indicate that 1) HB-DFL outperforms the counterparts in terms of FIT, mSIR and stability (mSC and umSC) of factor learning; 2) HB-DFL identifies PD and ADHD with an accuracy significantly higher than state-of-the-art methods do. Overall, HB-DFL has significant potentials for neuroimaging data analysis applications with its stability of automatic construction of structural features.

Functional Connectivity of Entorhinal Cortex Predicts Tau in the Elderly

AbstractBackgroundHuman neuroimaging efforts to understand the pathophysiology of Alzheimer’s disease (AD) have solely focused on the amyloid‐beta (Aβ) deposition and tau neurofibrillary tangle formation. Until recently, the idea of a connectivity‐based molecular functional framework for understanding AD pathophysiology was missing, which eluded researchers from connecting functional connectivity (FC) with Aβ/Tau deposition.MethodThe initial dataset includes 631 older (67.44 ± 6.48 years) adults (521 healthy controls and 110 mild cognitive impairment (MCI)) who have been imaged for Tau pathology as well as resting‐state functional magnetic resonance imaging (rsfMRI) and structural MRI scans. Using Schaefer’s 200 parcellation atlas and a regional cutoff (SUVR = 1.25), we identified four groups of tau staging based on the number of regions having higher than cutoff tau SUVR (no tau stage (N = 193), pre‐acceleration stage (N = 222), acceleration stage (N = 149), and post‐acceleration stage (N = 67)). Since the entorhinal cortex (EC) is widely regarded as the epi‐center for tau accumulation, the left and right EC time series were derived from rsfMRI data using the Desikan‐Killiany atlas and used as seed regions for our Pearson correlation coefficient based functional connectivity analysis with all 200 regions in the Schaefer atlas. Finally, linear regression models were run to predict the mean regional accumulation of tau based on the strength of mean FC between that region and EC.ResultAs shown in Fig. 1, left/right EC functional connectivity is strongly associated with the accumulation of tau in that region during the pre‐acceleration phase (left: t = 3.586, p &lt; 0.0004; right: t = 4.404, p &lt; 0.0001) and in the acceleration phase (left: t = 6.229, p &lt; 0.0001; right: t = 7.577, p &lt; 0.0001) (results not shown). The functional connectivity values from left/right EC for the pre‐acceleration phase are plotted on the surface of the brain, as shown in Fig. 2.ConclusionOur results provide evidence of the role of functional connectivity in tau spreading from EC to the rest of the brain and can help throw light on the dynamic patterns of tau spreading in MCI/AD subjects in different disease stages.

Dysregulated neural activity between the thalamus and cerebral cortex mediates cortical reorganization in cervical spondylotic myelopathy

Neuroimaging research has revealed significant changes in brain structure and function in patients with cervical spondylotic myelopathy(CSM). The thalamus plays a crucial role in this process, although its mechanisms of action remain incompletely understood. This study aimed to investigate whether spinal cord compression leads to alterations in the functional connectivity between the thalamus and the cerebral cortex, and to determine if such changes are associated with structural and functional remodeling of the brain in patients with CSM, and to identify potential neuroimaging biomarkers for classification. The study included 40 patients with CSM and 34 healthy controls(HCs) who underwent resting-state functional magnetic resonance imaging(fMRI) and structural MRI scans. Brain structural and functional metrics were quantified using functional connectivity(FC), fractional amplitude of low-frequency fluctuations(fALFF), surface-based morphometry(SBM), and independent component analysis(ICA) based on functional and structural MRI. Patients with CSM exhibited significantly reduced fALFF in the bilateral lateral lingual gyrus, bilateral calcarine fissure, left precentral gyrus and postcentral gyrus, left middle and superior occipital gyrus, left superior marginal gyrus, left inferior parietal gyrus, and right Rolandic operculum. ICA results revealed weakened functional connectivity between the sensorimotor network (SMN) and the left and right frontoparietal network(FPN), and lateral visual network (lVN), along with decreased connectivity between lVN and rFPN, and increased connectivity between lFPN and rFPN. Patients with CSM also had decreased sulcus depth in the bilateral insula, left precentral and postcentral gyrus, and right lingual gyrus and calcarine fissure. Furthermore, cervical spondylotic myelopathy patients showed decreased functional connectivity between the left ventral posterolateral nucleus (VPL) of the thalamus and the right middle occipital gyrus (MOG). Finally,multimodal neuroimaging with support vector machine(SVM) classified patients with CSM and healthy controls with 86.00% accuracy. Our study revealed that the decrease in functional connectivity between the thalamus and cortex mediated by spinal cord compression leads to structural and functional reorganization of the cortex. Features based on neuroimaging markers have the potential to become neuroimaging biomarkers for CSM.

Reduced in utero substrate supply decreases mitochondrial abundance and alters the expression of metabolic signalling molecules in the fetal sheep heart.

Babies born with fetal growth restriction (FGR) are at higher risk of developing cardiometabolic diseases across the life course. The reduction in substrate supply to the developing fetus that causes FGR not only alters cardiac growth and structure but may have deleterious effects on metabolism and function. Using a sheep model of placental restriction to induce FGR, we investigated key cardiac metabolic and functional markers that may be altered in FGR. We also employed phase-contrast magnetic resonance imaging MRI to assess left ventricular cardiac output (LVCO) as a measure of cardiac function. We hypothesized that signalling molecules involved in cardiac fatty acid utilisation and contractility would be impaired by FGR and that this would have a negative impact on LVCO in the late gestation fetus. Key glucose (GLUT4 protein) and fatty acid (FATP, CD36 gene expression) substrate transporters were significantly reduced in the hearts of FGR fetuses. We also found reduced mitochondrial numbers as well as abundance of electron transport chain complexes (complexes II and IV). These data suggest that FGR diminishes metabolic and mitochondrial capacity in the fetal heart; however, alterations were not correlated with fetal LVCO. Overall, these data show that FGR alters fetal cardiac metabolism in late gestation. If sustained ex utero, this altered metabolic profile may contribute to poor cardiac outcomes in FGR-born individuals after birth. KEY POINTS: Around the time of birth, substrate utilisation in the fetal heart switches from carbohydrates to fatty acids. However, the effect of fetal growth restriction (FGR) on this switch, and thus the ability of the fetal heart to effectively metabolise fatty acids, is not fully understood. Using a sheep model of early onset FGR, we observed significant downregulation in mRNA expression of fatty acid receptors CD36 and FABP in the fetal heart. FGR fetuses also had significantly lower cardiac mitochondrial abundance than controls. There was a reduction in abundance of complexes II and IV within the electron transport chain of the FGR fetal heart, suggesting altered ATP production. This indicates reduced fatty acid metabolism and mitochondrial function in the heart of the FGR fetus, which may have detrimental long-term implications and contribute to increased risk of cardiovascular disease later in life.

A case report of early detection of rare femoral shaft bone metastasis in a penis cancer patient

Background. Bone metastasis is very common in the advanced stage of numerous carcinomas. In penile carcinoma, lymph nodes metastasis is somehow common but it is very rare reported to be secondary from penile cancer. till the date, there are only few cases of penis carcinoma reported bone metastasis in literature worldwide.Case Presentation. Herein, We presented a 51-year-old Nepalese male with squamous cell carcinoma of penis. computed tomography (ct) scan of the patient revealed that there was carcinoma involving glans penis and precure with bilateral external &amp; internal inguinal lymphadenopathies. After then, the patient was under gone for partial penectomy and bilateral inguinal lymphadenectomy and complete 6-cycle chemotherapy. After one year of treatment, patient developed thigh pain and headache and he advised to have magnetic Resonance imaging (mRi) of brain, 99mTc-MDP whole body bone scan and ct scan of pelvis and thigh. The examination report reveals that there was a sclerotic change in vertex of skull bone and moderate 99mTc-MDP uptake in right proximal shaft of femur just below the neck d/d metastasis. The histopathological examination of the true cut biopsy taken from the lesion of the femur showed metastatic keratinizing squamous cell carcinoma which is rare case of femoral shaft bone metastasis secondary from penile carcinoma. Then patient was sent for surgical reconstruction of femur. Based on the case studies review femur shaft bone metastasis from penile cancer is extremely rare.Conclusion. The best of our knowledge; this is the first early detected bone metastases to shaft of the femur in a patient with penile cancer. early diagnosis helps to radical treatment as well as palliative treatment. surgery is the preferred option of the treatments, especially for metastatic foci in the long bones.

Multiparametric magnetic resonance imaging in the assessment of pathological axillary lymph nodes in cases of breast cancer

BackgroundBreast cancer is the most frequent cancer affecting females. It represents leading cause of death from all cancers in females. Traditionally, axillary staging was surgically assessed by axillary lymph node dissection (ALND), which is associated with complications. Sentinel lymph node biopsy (SLNB) is a minimally invasive surgical method for axillary staging in patients with primary breast cancer with lower morbidity and better quality of life. Clinical examination of the axilla is usually associated with a high false negative rate, so role of imaging is crucial to identify lymph nodes with or without suspicious features, to predict the pathological state of the lymph nodes and to direct the diagnostic and therapeutic process toward SLNB, ALND, or chemotherapy. Aim of this study was to assess axillary lymph nodes in breast cancer patients by magnetic resonance imaging and functional MRI preoperatively to determine its nature and eliminate invasive procedures as invasive dissection used in diagnosis.ResultsLymph node size cannot significantly predict malignant infiltration with p value 0.425, using cut-off value of 21.5 mm, with a sensitivity of 14.6% and specificity of 100%. Cortical thickness of lymph nodes can significantly predict malignant infiltration with p value 0.006, using cut-off value of 4.5 mm, with a sensitivity of 68.8%, specificity of 62.5%, and diagnostic accuracy of 64.1%. ADC can significantly predict lymph node infiltration with p value 0.011, using a cut-off value of 0.99, with sensitivity of 43.85, specificity of 100%, diagnostic accuracy of 57.8%, and AUC of 71.4%. There was a statistically significant correlation between pathological findings and DCE-MRI curve type III with p value 0.0001, showing a sensitivity of 37.5%, specificity of 100%, and diagnostic accuracy of 84.4% for detection of malignant lymph nodes.ConclusionsCortical thickness and effaced fatty hilum of lymph nodes can significantly predict malignant infiltration, while lymph node size cannot significantly predict malignant infiltration. Diffusion weighted images and ADC maps can be of significant value in predicting metastatic lymph nodes with approximate ADC cut-off value of 0.99. Kinetic MRI features of the axillary lymph nodes are not reliable enough to be used alone in the clinical management of breast cancer patients.