Magnetic Resonance Imaging For Detection Research Articles

Role of diffusion-weighted magnetic resonance imaging in detection of lymph node metastasis in rectal cancer

BackgroundRectal cancer is the most prevalent gastrointestinal tumor. Early diagnosis, accurate staging as well as early treatment are the keys for improving the five-year survival rate. The objective of this research is to assess the effectiveness of diffusion-weighted MRI (DWI) in identifying lymph nodes and distinguishing between benign and metastatic nodes throughout the first stage of primary rectal cancer.ResultsThe study showed that mean ADC value was significantly higher in mucinous carcinoma (1.72 ± 0.36 × 10–3 mm2/sec) than that in non-mucinous carcinoma (0.981 ± 0.276 × 10–3 mm2/sec) with a cutoff value of (1.3 × 10–3) mm2/s which was the precise value to produce high sensitivity, specificity and accuracy of 93%, 94%, and 94%, respectively. ADC analysis showed either intermediate or low signal in 49 (70%) and high signal in 21 (30%) L.Ns. Mean ADC value showed a significant reduction in malignant L.Ns (1.01 ± 0.54 × 10–3 mm2/sec) compared to benign L.Ns (1.51 ± 0.51 × 10–3 mm2/sec), AUC of 0.674 (P = 0.008) and a cutoff value of 0.987 × 10–3 mm2/s with sensitivity, specificity and accuracy of 44.4%, 91.2% and 67.5%, respectively. The mean L.N /tumor ratio was 1.65 ± 0.73 in benign L.Ns and 1.06 ± 0.37 in malignant L.Ns.ConclusionsIn rectal cancer, there was a significant difference between benign and malignant L.Ns regarding diffusion result, L.Ns size, shape, and margin. The study demonstrated the effectiveness of DWI in diagnosing lymph node metastasis in colorectal cancer; true diffusion restriction was significantly noted in malignant L.Ns compared to benign L.Ns. Mean ADC value showed a significant reduction in malignant L.Ns compared to benign L.Ns. L.N/tumor ratio showed a significant reduction in malignant L.Ns compared to benign L.Ns.

An Integrated Multi-omics prediction model for stroke recurrence based on [formula omitted] transformer layer and dynamic weighting mechanism

Background and objectiveStroke is a disease with high mortality and disability. Importantly, the fatality rate demonstrates a significant increase among patients afflicted by recurrent strokes compared to those experiencing their initial stroke episode. Currently, the existing research encounters three primary challenges. The first is the lack of a reliable, multi-omics image dataset related to stroke recurrence. The second is how to establish a high-performance feature extraction model and eliminate noise from continuous magnetic resonance imaging (MRI) data. The third is how to integration multi-omics data and dynamically weighted for different omics data. MethodsWe systematically compiled MRI and conventional detection data from a cohort comprising 737 stroke patients and established PSTSZC, a multi-omics dataset for predicting stroke recurrence. We introduced the first-ever Integrated Multi-omics Prediction Model for Stroke Recurrence, MPSR, which is based on ResNet, Lnet-transformer, LSTM and dynamically weighted DNN. The MPSR model comprises two principal modules, the Feature Extraction Module, and the Integrated Multi-Omics Prediction Module. In the Feature Extraction module, we proposed a novel Lnet regularization layer, which effectively addresses noise issues in MRI data. In the Integrated Multi-omics Prediction Module, we propose a dynamic weighted mechanism based on evaluators, which mitigates the noise impact brought about by low-performance omics. ResultsWe compared seven single-omics models and six state-of-the-art multi-omics stroke recurrence models. The experimental results demonstrate that the MPSR model exhibited superior performance. The accuracy, AUROC, specificity, and sensitivity of the MPSR model can reach 0.96, 0.97, 1, and 0.94, respectively, which is higher than the results of contrast model. ConclusionMPSR is the first available high-performance multi-omics prediction model for stroke recurrence. We assert that the MPSR model holds the potential to function as a valuable tool in assisting clinicians in accurately diagnosing individuals with a predisposition to stroke recurrence.

Diagnostics Accuracy of Magnetic Resonance Imaging in Detection of Atherosclerotic Plaque Characteristics in Carotid Arteries Compared to Histology: A Systematic Review.

Carotid plaque composition represents one of the main risk factors of future ischemic stroke. MRI provides excellent soft tissue contrast that can distinguish plaque characteristics. Our objective was to analyze the diagnostic accuracy of MRI imaging in the detection of carotid plaque characteristics compared to histology in patients with symptomatic and asymptomatic carotid atherosclerosis through a systematic review. After prospective registration in PROSPERO (ID CRD42022329690), Medline Ovid, Embase.com, Cochrane Library, and Web of Science Core were searched without any search limitation up to May 27, 2022 to identify eligible articles. Of the 8168 studies, 53 (37 × 1.5 T MRI, 17 × 3 T MRI) evaluated MRI accuracy in the detection of 13 specific carotid plaque characteristics in 169 comparisons. MRI demonstrated high diagnostic accuracy for detection of calcification (3 T MRI: mean sensitivity 92%/mean specificity 90%; 1.5 T MRI: mean sensitivity 81%/mean specificity 91%), fibrous cap (1.5 T: 89%/87%), unstable plaque (1.5 T: 89%/87%), intraplaque hemorrhage (1.5 T: 86%/88%), and lipid-rich necrotic core (1.5 T: 89%/79%). MRI also proved to have a high level of tissue discrimination for the carotid plaque characteristics investigated, allowing potentially for a better risk assessment and follow-up of patients who may benefit from more aggressive treatments. These results emphasize the role of MRI as the first-line imaging modality for comprehensive assessment of carotid plaque morphology, particularly for unstable plaque. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Diagnostics Accuracy of Magnetic Resonance Imaging in Detection of Atherosclerotic Plaque Characteristics in Carotid Arteries Compared to Histology: A Systematic Review.

Carotid plaque composition represents one of the main risk factors of future ischemic stroke. MRI provides excellent soft tissue contrast that can distinguish plaque characteristics. Our objective was to analyze the diagnostic accuracy of MRI imaging in the detection of carotid plaque characteristics compared to histology in patients with symptomatic and asymptomatic carotid atherosclerosis through a systematic review. After prospective registration in PROSPERO (ID CRD42022329690), Medline Ovid, Embase.com, Cochrane Library, and Web of Science Core were searched without any search limitation up to May 27, 2022 to identify eligible articles. Of the 8168 studies, 53 (37 × 1.5 T MRI, 17 × 3 T MRI) evaluated MRI accuracy in the detection of 13 specific carotid plaque characteristics in 169 comparisons. MRI demonstrated high diagnostic accuracy for detection of calcification (3 T MRI: mean sensitivity 92%/mean specificity 90%; 1.5 T MRI: mean sensitivity 81%/mean specificity 91%), fibrous cap (1.5 T: 89%/87%), unstable plaque (1.5 T: 89%/87%), intraplaque hemorrhage (1.5 T: 86%/88%), and lipid-rich necrotic core (1.5 T: 89%/79%). MRI also proved to have a high level of tissue discrimination for the carotid plaque characteristics investigated, allowing potentially for a better risk assessment and follow-up of patients who may benefit from more aggressive treatments. These results emphasize the role of MRI as the first-line imaging modality for comprehensive assessment of carotid plaque morphology, particularly for unstable plaque. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Prostate magnetic resonance imaging utilization and its relationship with advanced prostate cancer detection

BackgroundThe rise in advanced prostate cancer has coincided with increased use of Magnetic Resonance Imaging (MRI), leading to the hypothesis that this increase in surveillance registries is an artifact of more sensitive imaging tools. We assessed the association between regional variation in prostate MRI and advanced prostate cancer diagnoses. MethodsWe utilized SEER-Medicare data (2004–2015), including men > 65 diagnosed with localized prostate cancer. The predictor variable was the utilization of prostate MRI in each hospital referral region (HRR, representing regional healthcare markets). We compared the proportion of disease recorded as locally advanced or of regional risk group (cT3, cT4, and cN1) which would plausibly have been detected by prostate MRI. We conducted adjusted multivariable analysis and performed correlation analysis with Spearman rank coefficient at the level of the HRR. Sensitivity analysis for years 2011 to 2015 was conducted. ResultsOf 98,921 men diagnosed, 4.01% had locally advanced or regional disease. The median prostate MRI utilization rate was 4.58% (IQR [3.03%, 8.12%]). Adjusted multivariable analysis revealed no statistically significant correlation between MRI utilization and proportion of advanced prostate cancer (aOR = 1.01, 95% CI, [0.99,1.03]) in each region. The correlation between MRI usage and advanced diagnosis was not significant (Spearman Ρ = 0.09, P = 0.4). Sensitivity analysis conducted between 2011 and 2015 showed similar results (aOR = 1.008, 95% CI, [0.989, 1.027]; Spearman Ρ = 0.16, P = 0.1). ConclusionsDuring our study period, HRR-level utilization of MRI was not associated with higher incidences of advanced prostate cancer. This suggests the rising advanced prostate cancer diagnoses observed in this period are unlikely an artifact of greater sensitivity of modern imaging tests, but potentially due to other factors such as changes in screening or risk factors. With increased utilization and evolving techniques in recent years, the association between MRI and advanced prostate cancer detection warrants continued monitoring.

CNN-based glioma detection in MRI: A deep learning approach.

More than a million people are affected by brain tumors each year; high-grade gliomas (HGGs) and low-grade gliomas (LGGs) present serious diagnostic and treatment hurdles, resulting in shortened life expectancies. Glioma segmentation is still a significant difficulty in clinical settings, despite improvements in Magnetic Resonance Imaging (MRI) and diagnostic tools. Convolutional neural networks (CNNs) have seen recent advancements that offer promise for increasing segmentation accuracy, addressing the pressing need for improved diagnostic and therapeutic approaches. The study intended to develop an automated glioma segmentation algorithm using CNN to accurately identify tumor components in MRI images. The goal was to match the accuracy of experienced radiologists with commercial instruments, hence improving diagnostic precision and quantification. 285 MRI scans of high-grade gliomas (HGGs) and low-grade gliomas (LGGs) were analyzed in the study. T1-weighted sequences were utilised for segmentation both pre-and post-contrast agent administration, along with T2-weighted sequences (with and without Fluid Attenuation by Inversion Recovery [FAIRE]). The segmentation performance was assessed with a U-Net network, renowned for its efficacy in medical image segmentation. DICE coefficients were computed for the tumour core with contrast enhancement, the entire tumour, and the tumour nucleus without contrast enhancement. The U-Net network produced DICE values of 0.7331 for the tumour core with contrast enhancement, 0.8624 for the total tumour, and 0.7267 for the tumour nucleus without contrast enhancement. The results align with previous studies, demonstrating segmentation accuracy on par with professional radiologists and commercially accessible segmentation tools. The study developed a CNN-based automated segmentation system for gliomas, achieving high accuracy in recognising glioma components in MRI images. The results confirm the ability of CNNs to enhance the accuracy of brain tumour diagnoses, suggesting a promising avenue for future research in medical imaging and diagnostics. This advancement is expected to improve diagnostic processes for clinicians and patients by providing more precise and quantitative results.

Evaluation of Infantile Brachial Plexopathy Using 3T MRI and High-Resolution Ultrasound: Experience From a Tertiary Care Centre.

Currently, clinical assessment is the main tool for the evaluation of brachial plexus injury, complemented by electrophysiologic studies (EPS), and imaging studies whenever available. Imaging plays an important role as it enables the differentiation of pre-ganglionic and postganglionic injuries, and adds objectivity to presurgical evaluation. The primary objective was to evaluate the utility of magnetic resonance imaging (MRI) and high-resolution ultrasonography (USG) in the localization and characterization of brachial plexus injury in infants. In this prospective study, 34 infants with signs and symptoms of brachial plexus injury were evaluated by clinical examination, EPS, MRI, and USG. Imaging findings were correlated with intraoperative findings in infants who underwent surgical management. The association between EPS and MRI findings, and USG and MRI findings were assessed using Fisher's exact test. Semi-quantitative subjective analysis of various MRI sequences was done as well. The most common findings of preganglionic injury and postganglionic injury, in our study, were pseudomeningocele and nerve thickening, respectively. MRI detection of injuries had a significant association with EPS findings. All MRI-detected injuries had a muscle power of grade 3 or less. muscle. Three-dimensional (3D) short tau inversion recovery (STIR) sequence was found to be superior for detecting postganglionic injuries (P < 0.05). Imaging studies enable localization of the site of injury, determining the extent, and nature/morphology of injury. The gamut of findings obtained from MRI is far wider compared to that from USG. USG can be used as the first-line screening investigation.

Sensitivity of Magnetic Resonance Imaging in Detection of Choroidal Metastases

Introduction: The objective of this study was to determine the sensitivity of brain magnetic resonance imaging (MRI) in the detection of choroidal metastasis (CM) from systemic primary cancers. Methods: A retrospective chart review identified patients with clinically confirmed CM seen on the Oncology Service (Byers Eye Institute) between January 2018 and March 2022. Patients had an MRI brain and/or orbits performed within 3 months of CM diagnosis. Evaluation of CM detection by MRI was then divided into two parts: an initial “standard read,” where determination of CM detection was based solely on the original radiology report, to reflect real-world performance, and a subsequent “dedicated read,” for which a board-certified neuroradiologist, blinded to the laterality and location of the CM, reevaluated the studies to provide an objective “gold standard” interpretation regarding the radiographic detection of CM. Results: The study included 42 eyes of 40 patients with confirmed CM. On standard read, MRI detection of CM occurred in 21 of 42 eyes (50%), with no significant difference between MRI brain and orbit protocols (p = 0.249). Features associated with improved detection were increased tumor basal diameter (p < 0.001) and ultrasonographic tumor thickness (p = 0.003). On dedicated read, MRI detection of CM improved to 26 of 33 eyes (76%; limited to eyes with full complement of pre- and post-gadolinium sequences). Post-gadolinium 3D fluid-attenuated inversion recovery (FLAIR) sequence with fat suppression was the most sensitive (88%) for CM detection. 42% and 58% of lesions were visualized using conventional pre-gadolinium T1- and T2-weighted imaging, respectively. Conclusions: MRI sensitivity to detect CM improved from 50% to 76% with focused reinterpretation. Increased utilization of the post-gadolinium 3D FLAIR sequence and increased ocular scrutiny in cancer patients undergoing brain imaging may facilitate earlier diagnosis of CM.

MRI Detection of Lymph Node Metastasis through Molecular Targeting of C-C Chemokine Receptor Type 2 and Monocyte Hitchhiking.

Biopsy is the clinical standard for diagnosing lymph node (LN) metastasis, but it is invasive and poses significant risk to patient health. Magnetic resonance imaging (MRI) has been utilized as a noninvasive alternative but is limited by low sensitivity, with only ∼35% of LN metastases detected, as clinical contrast agents cannot discriminate between healthy and metastatic LNs due to nonspecific accumulation. Nanoparticles targeted to the C-C chemokine receptor 2 (CCR2), a biomarker highly expressed in metastatic LNs, have the potential to guide the delivery of contrast agents, improving the sensitivity of MRI. Additionally, cancer cells in metastatic LNs produce monocyte chemotactic protein 1 (MCP1), which binds to CCR2+ inflammatory monocytes and stimulates their migration. Thus, the molecular targeting of CCR2 may enable nanoparticle hitchhiking onto monocytes, providing an additional mechanism for metastatic LN targeting and early detection. Hence, we developed micelles incorporating gadolinium (Gd) and peptides derived from the CCR2-binding motif of MCP1 (MCP1-Gd) and evaluated the potential of MCP1-Gd to detect LN metastasis. When incubated with migrating monocytes in vitro, MCP1-Gd transport across lymphatic endothelium increased 2-fold relative to nontargeting controls. After administration into mouse models with initial LN metastasis and recurrent LN metastasis, MCP1-Gd detected metastatic LNs by increasing MRI signal by 30-50% relative to healthy LNs. Furthermore, LN targeting was dependent on monocyte hitchhiking, as monocyte depletion decreased accumulation by >70%. Herein, we present a nanoparticle contrast agent for MRI detection of LN metastasis mediated by CCR2-targeting and demonstrate the potential of monocyte hitchhiking for enhanced nanoparticle delivery.

Kiosk 7R-TA-12 - Role of Cardiac Magnetic Resonance Imaging in Detection of Subclinical Cardiac Involvement in Patients with Dysferlinopathy - A Prospective Study

Kiosk 7R-TA-12 - Role of Cardiac Magnetic Resonance Imaging in Detection of Subclinical Cardiac Involvement in Patients with Dysferlinopathy - A Prospective Study

Modality for estimating NMR relaxation time using perturbed angular correlation in double-photon emission nuclides

T2 relaxation time in magnetic resonance imaging (MRI), which is determined by magnetic dipole interactions, has been employed as a parameter for tumor detection. However, owing to the lack of MRI detection sensitivity, nuclear medicine imaging is currently the basic option for tracking low concentrations of chemical probes. Previous studies utilizing cascade radionuclides have focused on the relaxation due to electric quadrupole interactions. However, magnetic dipole interactions, which are crucial in MRI, remains to be elucidated. In this study, we determined the magnetic relaxation rate by using 111In, a cascade radionuclide used in clinical single-photon emission computed tomography (SPECT) scans. As the angle between the gamma rays from the nuclei is affected by the electrical and magnetic interactions acting on the nuclei, we measured the angular correlation ratio by using eight gadolinium gallium garnet (GAGG) multi-pixel photon counter (MPPC) 8 × 8 array detectors and extracted the magnetic relaxation rate. Consequently, we obtained a rate that increased with the Fe concentration, similar to the T2 relaxation rate, although it was influenced by the inhomogeneity of the external magnetic field. This study utilized low concentrations of the liquid-state radionuclide, which is commonly used in clinical nuclear medicine imaging scans and is expected to provide much higher sensitivity and more selective detection of tumors than conventional MRI.

Cardiac magnetic resonance imaging in detection of progressive graft dysfunction in pediatric heart transplantation.

Chronic graft failure (CGF) in pediatric heart transplant (PHT) is multifactorial and may present with findings of fibrosis and microvessel disease (MVD) on endomyocardial biopsy (EMB). There is no optimal CGF surveillance method. We evaluated associations between cardiac magnetic resonance imaging (CMR) and historical/EMB correlates of CGF to assess CMR's utility as a surveillance method. Retrospective analysis of PHT undergoing comprehensive CMR between September 2015 and January 2022 was performed. EMB within 6 months was graded for fibrosis (scale 0-5) and MVD (number of capillaries with stenotic wall thickening per field of view). Correlation analysis and logistic regression were performed. Forty-seven PHT with median age at CMR of 15.7 years (11.6, 19.3) and time from transplant of 6.4 years (4.1, 11.0) were studied. Cardiac allograft vasculopathy (CAV) was present in 11/44 (22.0%) and historical rejection in 14/41 (34.2%). CAV was associated with higher global T2 (49.0 vs. 47.0 ms; p = 0.038) and peak T2 (57.0 vs. 53.0 ms; p = 0.013) on CMR. Historical rejection was associated with higher global T2 (49.0 vs. 47.0 ms; p = 0.007) and peak T2 (57.0 vs. 53.0 ms; p = 0.03) as well as global extracellular volume (31.0 vs. 26.3%; p = 0.03). Higher fibrosis score on EMB correlated with smaller indexed left ventricular mass (rho = -0.34; p = 0.019) and greater degree of MVD with lower indexed left ventricular end-diastolic volume (rho = -0.35; p = 0.017). Adverse ventricular remodeling and abnormal myocardial characteristics on CMR are present in PHT with CAV, historical rejection, as well as greater fibrosis and MVD on EMB. CMR has the potential use for screening of CGF.

MRI Detection of Osteoporosis Bone Fractures Opposed to DEXA Scan

The study emphasizes the advancement achieved through Magnetic Resonance Imaging (MRI) technology in identifying bone fractures, particularly in cases such as osteoporosis. The paper explores the utilization of MRI in examining bone health to determine fracture risks, particularly those caused by osteoporosis while identifying the limitations of Dual-Energy X-ray absorptiometry (DEXA) scans for comprehensive insights. As evident, the utilization of DEXA scans permits the measurement of Bone Mineral Density (BMD) and the identification of fracture susceptibility. However, the depth of information provided by DEXA scans falls short of that attainable through MRI scans. The researcher's strategy involves a comprehensive evaluation of methods for assessing bone health, encompassing the significance of BMD and the limitations associated with DEXA scans. Moreover, the study delves into the potential utilization of MRI in offering a holistic measurement of bone health and mitigating the risk of osteoporotic fractures. As revealed, MRI meticulously examines bone properties via a process involving magnetic manipulation of protons, creating cross-sectional images that offer unique insights into internal structure. MRI surpasses DEXA in detecting Osteoporotic Bone Fractures, offering a comprehensive understanding essential for treatment planning and patient recovery. Its early detection prevents complications and guides interventions effectively. This technique employs T1-weighted, T2-weighted, and proton-density imaging to analyze complex bone webs. Therefore, the researcher strongly recommends further research to harness MRI's evolving capabilities, enabling more accurate, individualized assessments of fracture risks and treatment strategies, ultimately enhancing fracture care with precise diagnoses and tailored approaches for improved health outcomes.

Comparison of cone-beam and fan-beam computed tomography and low-field magnetic resonance imaging for detection of palmar/plantar osteochondral disease in Thoroughbred horses.

Palmar/plantar osteochondral disease (POD) of the metacarpal/tarsal condyles is a common pathological finding in racehorses. To compare diagnoses, imaging details, and measurements of POD lesions between cone-beam computed tomography CT (CBCT), fan-beam CT (FBCT), and low-field magnetic resonance imaging (MRI) using macroscopic pathology as a gold standard. Cross-sectional study. Thirty-five cadaver limbs from 10 horses underwent CBCT, FBCT, MRI, and macroscopic examination. CT and MR images were examined for presence of POD, imaging details of POD, and measurements of POD dimensions and areas. Imaging diagnoses, details, and measurements were compared with macroscopic examination and between modalities. Forty-eight POD lesions were seen over 70 condyles. Compared with macroscopic examination the sensitivity and specificity of diagnosis were 95.8% (CI95 = 88%-99%) and 63.6% (CI95 = 43%-81%) for FBCT, 85.4% (CI95 = 74%-94%) and 81.8% (CI95 = 63%-94%) for CBCT, and 69.0% (CI95 = 54%-82%) and 71.4% (CI95 = 46%-90%) for MRI. Inter-modality agreement on diagnosis was moderate between CBCT and FBCT (κ = 0.56, p < 0.001). POD was identified on CT as hypoattenuating lesions with surrounding hyperattenuation and on MRI as either T1W, T2*W, T2W, and STIR hyperintense lesions or T1W and T2*W heterogeneous hypointense lesions with surrounding hypointensity. Agreement on imaging details between CBCT and FBCT was substantial for subchondral irregularity (κ = 0.61, p < 0.001). Macroscopic POD width strongly correlated with MRI (r = 0.81, p < 0.001) and CBCT (r = 0.79, p < 0.001) and moderately correlated with FBCT (r = 0.69, p < 0.001). Macroscopic POD width was greater than all imaging modality (p < 0.001). Effect of motion artefact in live horse imaging could not be assessed. All imaging modalities were able to detect POD lesions, but underestimated lesion size. The CT systems were more sensitive, but the differing patterns of signal intensity may suggest that MRI can detect changes associated with POD pathological status or severity. The image features observed by CBCT and FBCT were similar.

Brain tumour MRI detection and classification based on the convolutional neural network

Magnetic Resonance Imaging (MRI) has emerged as a widely used diagnostic technique for brain tumour detection. However, the diagnosis of brain tumours poses significant challenges due to their occurrence in diverse locations and various types. Furthermore, MRI generates images that require manual analysis by physicians, which can be laborious and prone to errors. To enhance the efficacy and accuracy of brain tumour detection, recent advances in artificial intelligence have led to the development of machine learning algorithms. In this study, a convolutional neural network (CNN) based method was proposed for brain tumour detection and classification through the preprocessing of raw MRI images. The customized CNN model achieves an accuracy of 98% on a dataset consisting of four types of MRI images, including three types of brain tumours and healthy brain images, with preprocessing applied to all images. The CNN model demonstrates an accuracy of 95% in classifying raw MRI images from the dataset. The CNN model's performance is further improved by training the model with preprocessed images that have been transformed into the same colour space and object area zoomed in. These findings provide a promising avenue for the development of automated and efficient brain tumour detection systems using CNN and MRI.

Open Extended Pelvic Lymph Node Dissection as Validation of the Results of Preoperative Multiparametric Contrast-enhanced Magnetic Resonance Imaging in Detection of Lymph Node Metastases in Intermediate- and High-risk Prostate Cancer

Open Extended Pelvic Lymph Node Dissection as Validation of the Results of Preoperative Multiparametric Contrast-enhanced Magnetic Resonance Imaging in Detection of Lymph Node Metastases in Intermediate- and High-risk Prostate Cancer

Expression of clMagR/clCry4 protein in mBMSCs provides T2-contrast enhancement of MRI

Here, we propose for the first time the evaluation of magnetosensitive clMagR/clCry4 as a magnetic resonance imaging (MRI) reporter gene that imparts sensitivity to endogenous contrast in eukaryotic organisms. Using a lentiviral vector, we introduced clMagR/clCry4 into C57BL/6 mice-derived bone marrow mesenchymal stem cells (mBMSCs), which could specifically bind with iron, significantly affected MRI transverse relaxation, and generated readily detectable contrast without adverse effects in vivo. Specifically, clMagR/clCry4 makes mBMSCs beneficial for enhancing the sensitivity of MRI-R2 for iron-bearing granules, in which cells recruit exogenous iron and convert these stores into an MRI-detectable contrast; this is not achievable with control cells. Additionally, Prussian blue staining was performed together with ultrathin cell slices to provide direct evidence of natural iron-bearing granules being detectable on MRI. Hence, it was inferred that the sensitivity of MRI detection should be correlated with clMagR/clCry4 and exogenous iron. Taken together, the clMagR/clCry4 has great potential as an MRI reporter gene. Statement of significanceIn this study, we propose the evaluation of magnetosensitive clMagR/clCry4 as an MRI reporter gene, imparting detection sensitivity to eukaryotic mBMSCs for endogenous contrast. At this point, the clMagR and clCry4 were located within the cytoplasm and possibly influence each other. The clMagR/clCry4 makes mBMSCs beneficial for enhancing the sensitivity of MRI-R2 for iron-bearing granules, in which protein could specifically bind with iron and convert these stores into MRI-detectable contrast; this is not achieved by control cells. The viewpoint was speculated that the clMagR/clCry4 and exogenous iron were complementary to each other. Additionally, Prussian blue staining was performed together with TEM observations to provide direct evidence that the iron-bearing granules were sensitive to MRI.

A nomogram based on biparametric magnetic resonance imaging for detection of clinically significant prostate cancer in biopsy-naïve patients

PurposeThis study aimed to develop and validate a model based on biparametric magnetic resonance imaging (bpMRI) for the detection of clinically significant prostate cancer (csPCa) in biopsy-naïve patients.MethodThis retrospective study included 324 patients who underwent bpMRI and MRI targeted fusion biopsy (MRGB) and/or systematic biopsy, of them 217 were randomly assigned to the training group and 107 were assigned to the validation group. We assessed the diagnostic performance of three bpMRI-based scorings in terms of sensitivity and specificity. Subsequently, 3 models (Model 1, Model 2, and Model 3) combining bpMRI scorings with clinical variables were constructed and compared with each other using the area under the receiver operating characteristic (ROC) curves (AUC). The statistical significance of differences among these models was evaluated using DeLong’s test.ResultsIn the training group, 68 of 217 patients had pathologically proven csPCa. The sensitivity and specificity for Scoring 1 were 64.7% (95% CI 52.2%-75.9%) and 80.5% (95% CI 73.3%-86.6%); for Scoring 2 were 86.8% (95% CI 76.4%-93.8%) and 73.2% (95% CI 65.3%-80.1%); and for Scoring 3 were 61.8% (95% CI 49.2%-73.3%) and 80.5% (95% CI 73.3%-86.6%), respectively. Multivariable regression analysis revealed that scorings based on bpMRI, age, and prostate-specific antigen density (PSAD) were independent predictors of csPCa. The AUCs for the 3 models were 0.88 (95% CI 0.83–0.93), 0.90 (95% CI 0.85–0.94), and 0.88 (95% CI 0.83–0.93), respectively. Model 2 showed significantly higher performance than Model 1 (P = 0.03) and Model 3 (P < 0.01).ConclusionAll three scorings had favorite diagnostic accuracy. While in conjunction with age and PSAD the prediction power was significantly improved, and the Model 2 that based on Scoring 2 yielded the highest performance.

Comparison of cone‐beam and fan‐beam computed tomography and low‐field magnetic resonance imaging for detection of proximal phalanx dorsoproximal osteochondral defects

Comparison of cone‐beam and fan‐beam computed tomography and low‐field magnetic resonance imaging for detection of proximal phalanx dorsoproximal osteochondral defects

Midline suprapineal pseudocyst in brain of fetuses with open spina bifida.

Recently, it was noted that fetuses with open spina bifida (OSB) may have a midline cystic structure evident on ultrasound. Our aims were to determine the prevalence of this cystic structure, shed light on its pathophysiology and investigate the association between its presence and other characteristic brain findings in fetuses with OSB. This was a single-center retrospective study of all fetuses with OSB and available cineloop images in the axial plane referred to the Ontario Fetal Centre, Toronto, Canada, between June 2017 and May 2022. Ultrasound and magnetic resonance imaging (MRI) data obtained between 18 + 0 and 25 + 6 weeks were reviewed in search of a midline cystic structure. Pregnancy and lesion characteristics were collected. Transcerebellar diameter (TCD), clivus-supraocciput angle (CSA) and additional brain abnormalities (abnormal cavum septi pellucidi (CSP), abnormal corpus callosum (CC) and periventricular nodular heterotopia (PNH)) were assessed. In cases of in-utero repair, imaging findings were reviewed postoperatively. In cases of termination, neuropathological findings were reviewed, if available. Of 76 fetuses with OSB, 56 (73.7%) had a suprapineal cystic structure on ultrasound. The percentage of agreement between ultrasound and MRI detection was 91.5% (Cohen's kappa coefficient, 0.78 (95% CI, 0.57-0.98)). Brain autopsy in terminated cases revealed a dilatation of the posterior third ventricle, with redundant tela choroidea and arachnoid forming the membranous roof of the third ventricle, anterior and superior to the pineal gland. A cyst wall could not be identified, indicating that the structure was a pseudocyst. The presence of the pseudocyst was associated with a smaller CSA (pseudocyst absent, 62.11 ± 9.60° vs pseudocyst present, 52.71 ± 8.22°; P = 0.04). When the pseudocyst was present, its area was correlated inversely with TCD (r, -0.28 (95% CI, -0.51 to -0.02); P = 0.04). Fetal surgery did not have any impact on the growth rate of the pseudocyst (fetal surgery, 5.07 ± 3.29 mm2 /week vs expectant management, 4.35 ± 3.17 mm2 /week; P = 0.58). The presence of the pseudocyst was not associated with abnormal CSP, CC or presence of PNH. None of the cases with available postnatal follow-up required surgical procedure related to the pseudocyst. Approximately 75% of all OSB cases have a suprapineal pseudocyst. Its presence is associated with the degree of hindbrain herniation but not with abnormalities of the CSP and CC or presence of PNH. Thus, it should not be regarded as additional brain pathology and should not preclude fetuses from undergoing fetal surgery for OSB. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.