Multiparametric MRI-based radiomic nomogram for predicting HER-2 2+ status of breast cancer.

Objective: To explore the application of multiparametric MRI-based radiomic nomogram for assessing HER-2 2+ status of breast cancer (BC). Methods: Patients with pathology-proven HER-2 2+ invasive BC, who underwent preoperative MRI were divided into training (72 patients, 21 HER-2-positive and 51 HER-2-negative) and validation (32 patients, 9 HER-2-positive and 23 HER-2-negative) sets by randomization. All were classified as HER-2 2+ FISH-positive (HER-2-positive) or -negative (HER-2-negative) according to IHC and FISH. The 3D VOI was drawn on MR images by two radiologists. ADC, T2WI, and DCE images were analyzed separately to extract features (n = 1906). L1 regularization, F-test, and other methods were used to reduce dimensionality. Binary radiomics prediction models using features from single or combined imaging sequences were constructed using logistic regression (LR) classifier then and validated on a validation dataset. To build a radiomics nomogram, multivariate LR analysis was conducted to identify independent indicators. An evaluation of the model's predictive efficacy was made using AUC. Results: On the basis of combined ADC, T2WI, and DCE images, ten radiomic features were extracted following feature dimensionality reduction. There was superior diagnostic efficiency of radiomic signature using all three sequences compared to either one or two sequences (AUC for training group: 0.883; AUC for validation group: 0.816). Based on multivariate LR analysis, radiomic signature and peritumoral edema were independent predictors for identifying HER-2 2 +. In both training and validation datasets, nomograms combining peritumoral edema and radiomics signature demonstrated an effective discrimination (AUCs were respectively 0.966 and 0. 884). Conclusion: The nomogram that incorporated peritumoral edema and multiparametric MRI-based radiomic signature can be used to effectively predict the HER-2 2+ status of BC.

Ventral Anterior Cingulate Atrophy as a Predisposing Factor for Transient Global Amnesia.

Background and Purpose: This study aimed to evaluate the brain magnetic resonance imaging (MRI) of patients with acute transient global amnesia (TGA) using volumetric analysis to verify whether the brains of TGA patients have pre-existing structural abnormalities. Methods: We evaluated the brain MRI data from 87 TGA patients and 20 age- and sex-matched control subjects. We included brain MRIs obtained from TGA patients within 72 hours of symptom onset to verify the pre-existence of structural change. For voxel-based morphometric analyses, statistical parametric mapping was employed to analyze the structural differences between patients with TGA and control subjects. Results: TGA patients exhibited significant volume reductions in the bilateral ventral anterior cingulate cortices (corrected p<0.05). Conclusions: TGA patients might have pre-existing structural changes in bilateral ventral anterior cingulate cortices prior to TGA attacks.

Feasibility and reproducibility of cardiovascular magnetic resonance-feature tracking for quantitative right atrial function in dilated cardiomyopathy patients.

Background: The importance of right heart assessment in dilated cardiomyopathy (DCM) is increasingly recognized. The development of cardiovascular magnetic resonance-feature tracking (CMR-FT) has provided a novel approach to quantify myocardial deformation and evaluate cardiac function. In this study, we aimed to evaluate the feasibility and reproducibility of CMR-FT for the quantitative derivation of right atrial (RA) strain and strain rate (SR) in patients with DCM. Methods: A total of 68 DCM patients (84% male; aged 50.6±13.2 years) and 58 healthy controls (81% male; aged 48.4±11.2 years) were retrospectively enrolled from September 2018 to August 2022 at the First Affiliated Hospital of Zhejiang Chinese Medical University and Shenzhen Clinical Medical College of Guangzhou University of Chinese Medicine. RA reservoir, conduit, and booster strain (εs, εe, and εa) and peak positive, peak early negative, and peak late negative SR (SRs, SRe, and SRa) were measured using CMR-FT and compared between 2 groups using Student's t-test. Intra- and inter-observer reproducibility was evaluated using intraclass correlation coefficients (ICC) and Bland-Altman plots. Results: Compared to healthy controls, DCM patients showed significantly lower RA strain (εs: 19.7%±9.0% vs. 44.4%±9.7%; εe: 7.9%±5.3% vs. 25.8%±8.6%; εa: 11.8%±6.2% vs. 18.6%±5.1%, all P<0.001) and SR (SRs: 1.17±0.48 vs. 1.92±0.62 s-1; SRe: -0.85±0.56 vs. -1.94±0.63 s-1; SRa: -1.39±0.71 vs. -2.01±0.65 s-1, all P<0.001). There was no significant difference in RA maximum volume index between the 2 groups. Simple linear regression analysis demonstrated a significant correlation between N-terminal B-type natriuretic peptide (NT-proBNP), RA emptying fraction passive (RAEF passive), and RA εe [(NT-proBNP and εe): r=-0.48, P<0.001, 95% confidence interval (CI): -0.64 to -0.26; and (RAEF passive and εe): r=0.41, P=0.001, 95% CI: 0.22 to 0.56, respectively] in DCM patients. Intra- and inter-observer reproducibility was excellent (all ICCs >0.85) for RA deformation measurements. Conclusions: CMR-FT is a promising, noninvasive approach for the quantitative assessment of RA phasic function in patients with DCM. DCM patients exhibit impaired RA reservoir, conduit, and booster pump function prior to visible RA enlargement.

One-stop detection of anterior cruciate ligament injuries on magnetic resonance imaging using deep learning with multicenter validation.

Background: Anterior cruciate ligament (ACL) injuries are closely associated with knee osteoarthritis (OA). However, diagnosing ACL injuries based on knee magnetic resonance imaging (MRI) has been subjective and time-consuming for clinical doctors. Therefore, we aimed to devise a deep learning (DL) model leveraging MRI to enable a comprehensive and automated approach for the detection of ACL injuries. Methods: A retrospective study was performed extracting data from the Osteoarthritis Initiative (OAI). A total of 1,589 knees (comprising 1,443 intact, 90 with partial tears, and 56 with full tears) were enrolled to construct the classification model. This one-stop detection pipeline was developed using a tailored YOLOv5m architecture and a ResNet-18 convolutional neural network (CNN) to facilitate tasks based on sagittal 2-dimensional (2D) intermediate-weighted fast spin-echo sequence at 3.0T. To ensure the reliability and robustness of the classification system, it was subjected to external validation across 3 distinct datasets. The accuracy, sensitivity, specificity, and the mean average precision (mAP) were utilized as the evaluation metric for the model performance by employing a 5-fold cross-validation approach. The radiologist's interpretations were employed as the reference for conducting the evaluation. Results: The localization model demonstrated an accuracy of 0.89 and a sensitivity of 0.93, achieving a mAP score of 0.96. The classification model demonstrated strong performance in detecting intact, partial tears, and full tears at the optimal threshold on the internal dataset, with sensitivities of 0.941, 0.833, and 0.929, specificities of 0.925, 0.947, and 0.991, and accuracies of 0.940, 0.941, and 0.989, respectively. In comparison, on a subset consisting of 171 randomly selected knees from the OAI, the radiologists demonstrated a sensitivity ranging between 0.660 and 1.000, specificity ranging between 0.691 and 1.000, and accuracy ranging between 0.689 and 1.000. On a subset consisting of 170 randomly selected knees from the Chinese dataset, the radiologists exhibited a sensitivity ranging between 0.711 and 0.948, specificity ranging between 0.768 and 0.977, and accuracy ranging between 0.683 and 0.917. After retraining, the model achieved sensitivities ranging between 0.630 and 0.961, specificities ranging between 0.860 and 0.961, and accuracies ranging between 0.832 and 0.951, respectively, on the external validation dataset. Conclusions: The proposed model utilizing knee MRI showcases robust performance in the domains of ACL localization and classification.

Spiral time-of-flight magnetic resonance angiography for intracranial vascular imaging: performance compared to conventional Cartesian angiogram.

Background: Computed tomography angiography (CTA) and digital subtraction angiography (DSA) usually raise the risk of potential malignancies with cumulative radiation doses. Current time-of-flight magnetic resonance angiography (TOF-MRA) (dubbed as cTOF), which is based on Cartesian sampling mode, may show limited diagnostic conspicuity at sinuous or branching regions. It is also prone to relatively high false positive diagnoses and undesirable display of distal intracranial vessels. This study aimed to use spiral TOF-MRA (sTOF) as a noninvasive alternative to explore possible improvement, such that the application of magnetic resonance angiography (MRA) can be extended to facilitate clinical examination or cerebrovascular disease diagnosis and follow-up studies. Methods: Initially, 37 patients with symptoms of dizziness or transient ischemic attack were consecutively recruited for suspected intracranial vascular disease examination from Zhongshan Hospital of Xiamen University between July 2020 and April 2021 in this cross-sectional prospective study. After excluding 1 patient with severe scanning artifacts, 1 patient whose scanning scope did not meet the requirement, and 1 patient with confounding tumor lesions, a total of 34 participants were included according to the inclusion and exclusion criteria. Each participant underwent intracranial vascular imaging with both sTOF and cTOF sequences on a 3.0 T MR scanner with a conventional head-neck coil of 16 channels. Contrast CTA or DSA was also performed for 15 patients showing pathology. Qualitative comparisons in terms of image quality and diagnostic efficacy ratings, quantitative comparisons in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), vessel length, and sharpness were evaluated. Pair-wise Wilcoxon test was performed to evaluate the imaging quality derived from cTOF and sTOF acquisitions and weighted Cohen's Kappa was conducted to assess the rating consistency between different physicians. Results: Compared to cTOF, sTOF showed better performance with fewer artifacts. It can effectively alleviate false positives of normal vessels being misdiagnosed as aneurysm or stenosis. Improved conspicuity was observed in cerebral distal regions with more clearly identifiable vasculature at finer scales. Quantitative comparisons in selected regions revealed significant improvement of sTOF in SNR (P<0.01 or P<0.001), CNR (P<0.001), vessel length (P<0.001), and sharpness (P<0.001) as compared to cTOF. Besides, sTOF can depict details of M1 and M2 segments of middle cerebral artery (MCA) at metallic implant region, showing its resistance to magnetic susceptibility. Conclusions: The sTOF shows higher imaging quality and lesion detectability with reduced artifacts and false positives, representing a potentially feasible surrogate in intracranial vascular imaging for future clinic routines.

Association between quadriceps fat pad edema and patellofemoral osteoarthritis: a quantitative Q-Dixon-based magnetic resonance imaging (MRI) analysis.

Background: Anterior knee pain (AKP) is a common symptom of patellofemoral osteoarthritis (PFOA). There is limited prospective evidence supporting the relationships between patellofemoral maltracking parameters, AKP, and PFOA. Thus, this prospective cross-sectional study aimed to determine the association between quadriceps fat pad (QFP) edema and patellofemoral maltracking in patients with chronic AKP and to evaluate the feasibility and diagnostic performance of a PFOA assessment using fat fraction (FF) and T2* based on Q-Dixon. Methods: This was a cross-sectional study with prospective data collection. Patients with chronic AKP were recruited from an orthopedic outpatient magnetic resonance imaging (MRI) waiting room at Shanghai Tongren Hospital between November 1, 2022, and April, 30, 2023. Exclusion criteria included age of <18 years, knee trauma, major internal derangement, prior surgery/arthroscopy, pre-existing joint diseases, and contraindications to MRI. MRI was performed using a 3.0-T instrument, and patellofemoral maltracking parameters were measured. Patellofemoral feature-relevant items, including patellar cartilage defects, patellar bone marrow lesions (BMLs), patellar osteophytes, anterior femoral osteophytes, Hoffa synovitis, and synovitis-effusion, from the semi-quantitative MRI Osteoarthritis Knee Score (MOAKS) were measured. The Anterior Knee Pain Scale (AKPS) was used to assess pain and function. FF/T2* measurement differences between groups and their associations with maltracking metrics, osteoarthritis grading based on the Iwano grading system, MOAKS, and AKPS, were investigated. Based on Iwano grading, the participants were categorized as having no-PFOA (n=40), mild PFOA (n=40), and advanced PFOA (n=40). Chi-squared and one-way analysis of variance were used to assess potential differences between the groups. Spearman's correlation test was used to analyze the correlation between the morphological parameters, AKPS, Iwano grade, MOAKS, and MRI quantitative values. Receiver operating characteristic (ROC) curves assessed the area under the curve (AUC), sensitivity, and specificity of quantitative values for distinguishing PFOA from no-PFOA. Results: Among the 120 included patients, those in the mild (86.2±8.5) and advanced (83.9±9.5) PFOA groups had significantly lower AKPS scores than those in the no-PFOA group (88.8±7.3) (P=0.03). The mean FF and T2* values of the QFP were significantly higher in the no-PFOA group than those in the mild and advanced PFOA groups (P<0.001 for FF and P=0.02 for T2*). Quantitative data on the QFP and patellofemoral maltracking parameters showed no association. FF (r=-0.686, P<0.001) and T2* (r=-0.314, P=0.008) showed a negative correlation with the Iwano grade. The AUCs for PFOA diagnosis were 0.906 [95% confidence interval (CI), 0.853-0.960] (FF) and 0.744 (95% CI, 0.657-0.831) (T2*). Conclusions: QFP FF and T2* were not associated with patellofemoral maltracking parameters but with increased PFOA in patients with AKP, suggesting that QFP abnormalities play a role in PFOA. Therefore, a quantitative QFP assessment (FF and T2*) based on Q-Dixon technology could be a convenient and reliable new imaging biomarker for PFOA severity during clinical diagnosis, treatment, and follow-up.

Free-breathing motion corrected magnetic resonance elastography of the abdomen.

Background: Magnetic resonance elastography (MRE) is a non-invasive method to measure the viscoelastic properties of tissue and has been applied in multiple abdominal organs. However, abdominal MRE suffers from detrimental breathing motion causing misalignment of structures between repeated acquisitions for different MRE dimensions (e.g., motion encoding directions and wave phase offsets). This study investigated motion correction strategies to resolve all breathing motion on sagittal free-breathing MRE acquisitions in a phantom, in healthy volunteers and showed feasibility in patients. Methods: First, in silico experiments were performed on a static phantom dataset with simulated motion. Second, eight healthy volunteers underwent two sagittal MRE acquisitions in the pancreas and right kidney. The multi-frequency free-breathing spin-echo echo-planar-imaging (SE-EPI) MRE consisted of four frequencies (30, 40, 50, 60 Hz), eight wave-phase offsets, with 3 mm3 isotropic voxel size. Following data re-sorting in different number of motion states (4 till 12) based on respiratory waveform signal, three intensity-based registration methods (monomodal, multimodal, and phase correlation) and non-rigid local registration were compared. A ranking method was used to determine the best registration method, based on seven signal-to-noise and image quality measures. Repeatability was assessed for no motion correction (Original) and the best performing method (Best) using Bland-Altman analysis. Lastly, the best motion correction method was compared to no motion correction on patient MRE data [pancreatic ductal adenocarcinoma (PDAC, n=5) and metabolic dysfunction-associated steatotic liver disease (MASLD) (n=1)]. Results: In silico experiments showed a deviation of shear wave speed (SWS) with simulated motion to the ground truth, which was (partially) resolved using motion correction. In healthy volunteers ranking resulted in the best motion correction method of monomodal registration using nine motion states, while no motion correction was ranked last. Limits of agreement were (-0.18, 0.14), and (-0.25, 0.18) m/s for Best and Original, respectively. Using motion correction in patients resulted in a significant increase in SWS in the pancreas (Original: 1.39±0.10 and Best: 1.50±0.17 m/s). After motion correction PDAC had a mean SWS of 1.56±0.27 m/s (Original: 1.42±0.25 m/s). The fibrotic liver mean SWS was 2.07±0.20 m/s (Original: 2.12±0.18 m/s). Conclusions: Motion correction in sagittal free-breathing abdominal MRE results in improved data quality, inversion precision, repeatability, and is feasible in patients.

Association of vulnerable plaques with white matter hyperintensities on high-resolution magnetic resonance imaging.

Background: One of the widespread manifestations of cerebral small vessel disease (CSVD) of the brain parenchyma is white matter lesion, which appears as white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI). Previous studies have illustrated that large artery atherosclerosis is related to CSVD, but the precise progress of pathogenesis remains unknown. High-resolution MRI (HR-MRI) has the ability to delineate intracranial vascular walls, enabling a thorough exploration of the structure and composition of unstable plaques. This study aimed to apply HR-MRI to characterize the wall changes and plaque characteristics of middle cerebral arteries in patients with WMHs and to investigate the correlation between plaque vulnerability parameters and different degrees of WMHs. Methods: In this study, 138 patients with acute ischemic stroke at Harbin Medical University's First Clinical Hospital (May 2021 to October 2023) were cross-sectionally reviewed and underwent conventional brain and HR-MRI using T1-weighted 3D volumetric isotropic turbo spin echo acquisition (T1W-3D-VISTA) of the unilateral middle cerebral artery (MCA). According to Fazekas grade (0-6), the patients were divided into two groups: Fazekas score 0-2, no-or-mild WMHs; and Fazekas 3-6, moderate-to-severe WMHs. The intraplaque hemorrhage, plaque distribution, plaque enhancement, plaque load, remodeling pattern, and stenosis of the two groups were measured. Binary logistic regression analysis was conducted to evaluate the relationship between vulnerable plaques and WMHs. Results: Of the participants who were initially considered for inclusion, 71 were deemed eligible, among whom 34 were placed in the no-or-mild WMH group and 37 in the moderate-to-severe WMH group. Between the two groups, there were significant differences in intraplaque hemorrhage (P=0.01), a wide distribution (P=0.02), and plaque enhancement (P=0.02). Univariate analysis showed that WMHs were associated with age [odds ratio (OR) =1.080; 95% confidence interval (CI): 1.020-1.144; P=0.008], hypertension (OR =3.500; 95% CI: 1.276-9.597; P=0.01), intraplaque hemorrhage (OR =3.955; 95% CI: 1.247-12.538; P=0.02), a wide distribution (OR =3.067; 95% CI: 1.159-8.115; P=0.02), and significant plaque enhancement (OR =4.372; 95% CI: 1.101-17.358; P=0.03); however, the multivariate results showed that the only independent factors associated with WMHs were age (OR =1.095; 95% CI: 1.019-1.176; P=0.01) and intraplaque hemorrhage (OR =5.88; 95% CI: 1.466-23.592; P=0.01). Conclusions: Our findings suggest that age and intraplaque hemorrhage may be associated with more severe WMHs in patients with acute ischemic stroke, which may be helpful for further clinical examination and intervention treatment.

Left atrial and left ventricular strain in feature-tracking cardiac magnetic resonance for predicting patients at high risk of sudden cardiac death in hypertrophic cardiomyopathy.

Background: Sudden cardiac death (SCD) represents the most severe complication of hypertrophic cardiomyopathy (HCM). The risk stratification of SCD in patients with HCM remains a subject of ongoing debate, and the utility of left atrial (LA) and left ventricular (LV) myocardial strain for risk stratification of also SCD remains uncertain. Through use of feature-tracking cardiac magnetic resonance (FT-CMR), this study aimed to investigate the attenuation of LA and LV strain in HCM and to assess their predictive value in SCD. Methods: This retrospective and cross-sectional study included patients with HCM who underwent 3.0 T cardiac magnetic resonance (CMR) at a single institution. Feature-tracking strain analysis was conducted to obtain the strain rate (SR) and LV strain and to evaluate LV function. LA strain was measured during different functional phases including left atrial reservoir strain (LARS), LA conduit strain (LACS), and LA booster strain. All patients were categorized into high- and low-risk groups for SCD as defined by the 2020 American Heart Association/American College HCM implantable cardioverter defibrillator class of recommendation algorithm. Comparison between the two groups was conducted using the independent samples t test and the nonparametric rank sum test. Multivariate logistic regression analysis was performed to further identify the factors influencing SCD risk in HCM. Results: Compared with those in the low-risk group, patients in the high-risk group had lower left ventricular ejection fraction (LVEF), LV stroke volume index (LVSVI), and LA stroke volume index (LASVI) but a higher LV end-systolic volume index (LVESVI), LV maximum wall thickness, and late gadolinium enhancement (LGE) (P<0.001). LV strain, SR, and LA strain all showed significant differences between the high- and low-risk groups (LARS: P=0.04; LACS: P=0.02; all other P values <0.001). The LV global circumferential strain (LVGCS) had a strong negative correlation with LVEF in patients with HCM (r=-0.76; P<0.001). Multivariate analysis showed that LV global radial strain (LVGRS) and LARS could be used for categorizing the patients into the high-risk group [LVGRS: odds ratio (OR) =0.69; 95% confidence interval (CI): 0.55-0.87, P<0.001; LARS: OR =1.39; 95% CI: 1.02-1.90, P=0.03]. The combined LVGRS-LARS model exhibited a superior diagnostic value for high risk of SCD [area under the curve (AUC) =0.95; 95% CI: 0.90-1.00; P<0.001] compared to LARS alone (AUC =0.63; 95% CI: 0.51-0.76; P=0.04). Conclusions: LA and LV strain measured by FT-CMR can accurately identify those patients with HCM at a high risk of SCD. This approach may prove considerably value in guiding early therapeutic intervention with implantable cardioverter-defibrillators (ICDs) to prevent adverse clinical outcomes.

Multiparametric magnetic resonance imaging-based assessment of the effect of adenomyosis on determining the depth of myometrial invasion in endometrial cancer.

Background: Accurate preoperative diagnosis of endometrial cancer (EC) with deep myometrial invasion (DMI) is critical to deciding whether to perform lymphadenectomy. However, the presence of adenomyosis makes distinguishing DMI from superficial myometrial invasion (SMI) on magnetic resonance imaging (MRI) challenging. We aimed to evaluate the accuracy of multiparametric MRI (mpMRI) in diagnosing DMI in EC coexisting with adenomyosis (EC-A) compared with EC without coexisting adenomyosis and to evaluate the effect of different adenomyosis subtypes on myometrial invasion (MI) depth in EC. Methods: Patients with histologically confirmed International Federation of Gynecology and Obstetrics (FIGO) stage I EC who underwent preoperative MRI were consecutively included in this 2-center retrospective study. Institution 1 was searched from January 2017 to November 2022 and institution 2 was searched from June 2017 to March 2021. Patients were divided into 2 groups: group A, patients with EC-A; group B, EC patients without coexisting adenomyosis, matched 1:2 according to age ±5 years and tumor grade. A senior radiologist assessed the MRI adenomyosis classification in group A. Then, 2 radiologists (R1/R2) independently interpreted T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), T1-weighted contrast-enhanced (T1CE), and a combination of all images (mpMRI) respectively, and then assessed MI depth. Accuracy, sensitivity, specificity, and the areas under the receiver operating curve (AUC) were calculated. The chi-square test was used to compare the accuracy of diagnosing DMI. Interobserver agreement was evaluated using the Kappa test. Results: A total of 70 cases in group A and 140 cases in group B were included. The accuracy, sensitivity, and specificity of consensus were 94.3% [95% confidence interval (CI): 88.9-99.7%] vs. 92.1% (95% CI: 87.7-96.6%), 60.0% (95% CI: 17-92.7%) vs. 86.7% (95% CI: 68.4-95.6%), and 96.9% (95% CI: 88.4-95.5%) vs. 93.6% (95% CI: 86.8-97.2%) (group A vs. group B, respectively). There was no significant difference in the diagnostic accuracy of DMI on each sequence between the groups (Reviewer 1/Reviewer 2): PT2WI=0.14/0.17, PDWI=0.50/0.33, PT1CE=0.90/0.18, PmpMRI=0.50/0.37. The AUC for T2WI, DWI, T1CE, and mpMRI (Reviewer 1/Reviewer 2), respectively, were 0.54 (95% CI: 0.42-0.66)/0.78 (95% CI: 0.67-0.87), 0.63 (95% CI: 0.50-0.74)/0.77 (95% CI: 0.65-0.86), 0.69 (95% CI: 0.57-0.80)/0.79 (95% CI: 0.68-0.88), and 0.91 (95% CI: 0.82-0.97)/0.89 (95% CI: 0.79-0.95) (group A) and 0.83 (95% CI: 0.76-0.89)/0.85 (95% CI: 0.78-0.90), 0.83 (95% CI: 0.76-0.89)/0.86 (95% CI: 0.79-0.91), 0.88 (95% CI: 0.82-0.93)/0.86 (95% CI: 0.80-0.92), and 0.91 (95% CI: 0.85-0.95)/0.87 (95% CI: 0.80-0.92) (group B). Interobserver agreement was highest with mpMRI [κ=0.387/0.695 (case/control)]. The consensus results of MRI categorization of adenomyosis revealed no significant difference in the accuracy of diagnosing DMI by adenomyosis subtype (Pspatial relationship>0.99, Paffected area=0.52, Paffected pattern=0.58, Paffected size>0.99). Conclusions: The presence of adenomyosis or adenomyosis subtype had no significant effect on the interpretation of the depth of MI. T1CE can increase the contrast between adenomyosis and cancer foci; therefore, the information provided by T1CE should be valued.

Deep learning image reconstruction of diffusion-weighted imaging in evaluation of prostate cancer focusing on its clinical implications.

Background: Image-based assessment of prostate cancer (PCa) is increasingly emphasized in the diagnostic workflow for selecting biopsy targets and possibly predicting clinically significant prostate cancer (csPCa). Assessment is based on Prostate Imaging-Reporting and Data System (PI-RADS) which is largely dependent on T2-weighted image (T2WI) and diffusion weighted image (DWI). This study aims to determine whether deep learning reconstruction (DLR) can improve the image quality of DWI and affect the assessment of PI-RADS ≥4 in patients with PCa. Methods: In this retrospective study, 3.0T post-biopsy prostate magnetic resonance imaging (MRI) of 70 patients with PCa in Korea University Ansan Hospital from November 2021 to July 2022 was reconstructed with and without using DLR. Four DWI image sets were made: (I) conventional DWI (CDWI): DWI with acceleration factor 2 and conventional parallel imaging reconstruction, (II) DL1: DWI with acceleration factor 2 using DLR, (III) DL2: DWI with acceleration factor 3 using DLR, and (IV) DL3: DWI with acceleration factor 3 and half average b-value using DLR. Apparent diffusion coefficient (ADC) value, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured by one reviewer, while two reviewers independently assessed overall image quality, noise, and lesion conspicuity using a four-point visual scoring system from each DWI image set. Two reviewers also performed PI-RADSv2.1 scoring on lesions suspected of malignancy. Results: A total of 70 patients (mean age, 70.8±9.7 years) were analyzed. The image acquisition time was 4:46 min for CDWI and DL1, 3:40 min for DL2, and 2:00 min for DL3. DL1 and DL2 images resulted in better lesion conspicuity compared to CDWI images assessed by both readers (P<0.05). DLR resulted in a significant increase in SNR, from 38.4±14.7 in CDWI to 56.9±21.0 in DL1. CNR increased from 25.1±11.5 in CDWI to 43.1±17.8 in DL1 (P<0.001). PI-RADS v2.1 scoring for PCa lesions was more agreeable with the DL1 reconstruction method than with CDWI (κ value CDWI, DL1; 0.40, 0.61, respectively). A statistically significant number of lesions were upgraded from PI-RADS <4 in CDWI image to PI-RADS ≥4 in DL1 images for both readers (P<0.05). Most of the PI-RADS upgraded lesions were from higher than unfavorable intermediate-risk groups according to the 2023 National Comprehensive Cancer Network guidelines with statistically significant difference of marginal probability in DL1 and DL2 for both readers (P<0.05). Conclusions: DLR in DWI for PCa can provide options for improving image quality with a significant impact on PI-RADS evaluation or about a 23% reduction in acquisition time without compromising image quality.

Magnetic resonance imaging-based prediction models for differentiating intraspinal schwannomas from meningiomas: classification and regression tree and random forest analysis.

Background: Due to the variations in surgical approaches and prognosis between intraspinal schwannomas and meningiomas, it is crucial to accurately differentiate between the two prior to surgery. Currently, there is limited research exploring the implementation of machine learning (ML) methods for distinguishing between these two types of tumors. This study aimed to establish a classification and regression tree (CART) model and a random forest (RF) model for distinguishing schwannomas from meningiomas. Methods: We retrospectively collected 88 schwannomas (52 males and 36 females) and 51 meningiomas (10 males and 41 females) who underwent magnetic resonance imaging (MRI) examinations prior to the surgery. Simple clinical data and MRI imaging features, including age, sex, tumor location and size, T1-weighted images (T1WI) and T2-weighted images (T2WI) signal characteristics, degree and pattern of enhancement, dural tail sign, ginkgo leaf sign, and intervertebral foramen widening (IFW), were reviewed. Finally, a CART model and RF model were established based on the aforementioned features to evaluate their effectiveness in differentiating between the two types of tumors. Meanwhile, we also compared the performance of the ML models to the radiologists. The receiver operating characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the models and clinicians' discrimination performance. Results: Our investigation reveals significant variations in ten out of 11 variables in the training group and five out of 11 variables in the test group when comparing schwannomas and meningiomas (P<0.05). Ultimately, the CART model incorporated five variables: enhancement pattern, the presence of IFW, tumor location, maximum diameter, and T2WI signal intensity (SI). The RF model combined all 11 variables. The CART model, RF model, radiologist 1, and radiologist 2 achieved an area under the curve (AUC) of 0.890, 0.956, 0.681, and 0.723 in the training group, and 0.838, 0.922, 0.580, and 0.659 in the test group, respectively. Conclusions: The RF prediction model exhibits more exceptional performance than an experienced radiologist in discriminating intraspinal schwannomas from meningiomas. The RF model seems to be better in discriminating the two tumors than the CART model.

Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time.

Background: Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics. Methods: In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS). The images were reconstructed with and without DLR in super-resolution mode. The image quality was rated in six domains. Signal-to-noise ratio (SNR), and image sharpness were measured. Results: The mean acquisition time was 281±23 s for the standard and 140±12 s for the short acquisition (P<0.0001). DLR images had higher sharpness compared to non-DLR (P<0.001). Short and short-DLR had lower SNR than the standard and standard-DLR (P<0.001). The perceived image quality of short-DLR was rated better in all categories compared to the standard sequence (P<0.001 to P=0.004). All domains of subjective evaluation were correlated with measured image sharpness (P<0.001). Conclusions: T2-TSE acquisition of the prostate accelerated using CS combined with DLR reconstruction provides images with increased sharpness that have a superior quality as perceived by human readers compared to standard T2-TSE. The perceived image quality is correlated with measured image contrast.

Human Brain Magnetic Resonance Imaging Studies for Psychiatric Disorders: The Current Progress and Future Directions.

With the prevalence of psychiatric disorders and the limitations of the diagnostic scheme and treatment options of these disorders, magnetic resonance imaging (MRI) studies play a significant role in uncovering the pathological basis of psychiatric disorders and potentially using biological markers in clinical settings. The use of MRI in clinical research has grown over the past three decades, and current MRI research continues to provide an avenue to guide the development of diagnostic approaches and therapeutic solutions. However, the current shortcomings of MRI studies derive not only from technical limitations (i.e., the range of contrasts that MRI probes or sensors can create) but also from confounding factors in the current methodological approaches of case-control studies for psychiatric disorders. Thus, by reviewing the recent literature on MRI research on psychiatric disorders, we explain the current progress and limitations of brain MRI methodologies used to study psychiatric disorders. We consider the growing use of cross-disorder methods to identify shared and disease-specific pathological features across psychiatric disorders. In addition, we need to outline healthy developmental and aging changes of the brain and investigate the disorder difference as a deviation of the trajectory. Although these methods have provided us with new insights, the demarcation between psychiatric disorders based on a definitive set of pathologies remains limited. This challenge of disease stratification is further complicated by the presence of multiple different sets of disorder pathologies within a single disorder and the different progressive timelines of different disorders. As such, we introduce the ongoing research projects in Japan, namely, the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) and the Strategic International Brain Science Research Promotion Program (Brain/MINDS Beyond). These collaborative research initiatives across Japan use neuroimaging and travel-subject harmonization to conduct nationwide MRI studies capable of providing large-scale coherent results, which may address the current limitations of MRI psychiatric disorder research.

Outcomes of Surgical Management of Turf Toe: 12-Year Results.

Background "Turf toe" is a classical capsuloligamentous injury to the plantar surface of the metatarsophalangeal (MTP) joint of the great toe. The name is synonymous with injuries sustained on artificial turf or hard grounds. The classical injury pattern is a hyperdorsiflexion injury with an axial load. The outcomes of these injuries are unpredictable and there are no clear guidelines for the management of these injuries. These injuries are debilitating and can lead to long-term problems and inability to return to pre-injury activity level if missed. We present a long-term surgical follow-up of severe grade 3 turf toe injuries. Methods In the period from 2011 to 2022, we treated 20 patients with turf toe/MTP joint instability. There were 10 football injuries (50%), six running injuries (30%), two gymnastic injuries (10%), one motorcycle injury (0.5%), and one was a ballet dancer (0.5%). All the grade 1 and 2 injuries were treated conservatively with rest, ice application, and splinting of the toe. Grade 3 injuries were treated surgically and strict rehabilitation protocol was followed. Results The mean age at surgery was 32.7 years and the average patient follow-up was 7.5 months after surgery. The Manchester-Oxford Foot Questionnaire (MOXFQ) score showed a statistically significant improvement from a mean of 73.0 (median = 75) preoperatively to 28.1 (median = 28.6) postoperatively (median improvement = 46.4, P = 0.022). Similarly, there was a significant improvement in pain score, which showed an improvement from a mean of 72.9 (median = 70.0) preoperatively to a mean of 22.9 (median = 25.0) postoperatively (median improvement = 51.3, P = 0.022). Conclusion Turf toe is a serious injury that may prevent a high percentage of patients from resuming their previous physical activities. The correct identification, classification, and grading of the first MTP joint instability helps in decision-making and achieving good surgical outcomes.

Radiological Insights From Two Distinct Cases of Pineal Region Meningioma: A Case Report.

This case report delineates the radiological evaluation and diagnostic intricacies of two unique cases of pineal region meningioma, underscoring the pivotal role of advanced imaging techniques in the accurate diagnosis and management of such rare tumors. Pineal region tumors represent a heterogeneous group of neoplasms, with meningiomas in this location being particularly uncommon, thereby posing significant challenges in diagnosis and therapeutic decision-making. The first case involves a 40-year-old female presenting with progressive headaches and visual disturbances with symptoms of increased intracranial pressure, whereas the second case describes a 30-year-old male presenting with headache, dizziness, difficulty with balance, and cognitive decline. Both patients underwent a comprehensive diagnostic workup, including magnetic resonance imaging (MRI), which revealed tumors in the pineal region exhibiting characteristics suggestive of meningioma. The MRI findings in both cases included well-defined mass lesions showing iso- to hypointense signal on T1-weighted images with robust contrast enhancement. Additionally, the radiological assessment was instrumental in differentiating these meningiomas from other pineal region tumors, such as germinomas or pineocytomas, based on their distinctive imaging features and the absence of dissemination. Surgical resection followed by histopathological examination confirmed the diagnosis of meningioma in both cases. This report highlights the critical role of radiological imaging in the early detection and differentiation of pineal region tumors, emphasizing the need for a multidisciplinary approach to achieve optimal patient outcomes.

Increased brain gyrification and cortical thinning in winter-born patients with schizophrenia spectrum.

Introduction: The findings of epidemiological studies suggest that a relationship exists between the risk of schizophrenia and winter births in the Northern Hemisphere, which may affect the process of fetal neurodevelopment. However, it remains unclear whether birth seasons are associated with the brain morphological characteristics of patients within the schizophrenia spectrum. Methods: The present magnetic resonance imaging study using FreeSurfer software examined the effects of birth seasons (i.e., summer-born vs. winter-born) on the comprehensive brain surface characteristics of 101 patients with schizophrenia (48 summer- and 53 winter-born), 46 with schizotypal disorder (20 summer- and 26 winter-born), and 76 healthy control subjects (28 summer- and 48 winter-born). Results: In comparisons with summer-born patients, winter-born patients, particularly those with schizophrenia, showed significantly increased gyrification mainly in the left lateral occipital and inferior temporal regions and right fronto-parietal region as well as cortical thinning in the right superior frontal region. Birth seasons did not significantly affect the local gyrification index or cortical thickness in healthy controls. Discussion: The present whole-brain surface-based analysis demonstrated that brain morphological characteristics reported in the schizophrenia spectrum were more pronounced in winter-born patients than in summer-born patients, suggesting the contribution of early neurodevelopmental factors associated with birth seasons to the pathophysiology of the schizophrenia spectrum.

Cerebral fat embolism with turbid urine as the initial sign.

BACKGROUND: Cerebral fat embolism (CFE) is a rare but potentially fatal complication that can occur after long bone fractures. It represents one subcategory of fat embolisms (FE). Diagnosing CFE can be challenging due to its variable and nonspecific clinical manifestations. We report a case of CFE initially presenting with turbid urine, highlighting an often neglected sign. CASE PRESENTATION: A 69-year-old male was admitted after a traffic accident resulting in bilateral femoral fractures. Sixteen hours post-admission, grossly turbid urine was noted but received no special attention. Four hours later, he developed rapid deterioration of consciousness and respiratory distress. Neurological examination revealed increased upper limb muscle tone and absent voluntary movements of lower limbs. Brain MRI demonstrated a 'starfield pattern' of diffuse punctate lesions, pathognomonic for CFE. Urine microscopy confirmed abundant fat droplets. Supportive treatment and fracture fixation were performed. The patient regained consciousness after 3 months but had residual dysphasia and limb dyskinesia. CONCLUSION: CFE can present with isolated lipiduria preceding overt neurological or respiratory manifestations. Heightened awareness of this subtle sign in high-risk patients is crucial for early diagnosis and intervention. Prompt urine screening and neuroimaging should be considered when gross lipiduria occurs after long bone fractures.

Collapse or distention of the perioptic space in children - What does it mean to pediatric radiologists? Comprehensive review of perioptic space evaluation.

The perioptic space comprises the subarachnoid space [SAS] of the optic nerve communicating with the SAS of the central nervous system. Pressure variations in the SAS of the central nervous system can be transmitted to the optic papilla through the perioptic space. Variations in the diameter of the perioptic space serve as an important indicator for select intracranial pathologies in the pediatric population. Though the perioptic space can be evaluated using various imaging modalities, MRI is considered highly effective due to its superior soft tissue resolution. With advancement in MR imaging techniques, high-resolution images of the orbits can provide improved visualization of the perioptic space. It is imperative for the pediatric radiologist to routinely assess the perioptic space on brain and orbit MR imaging, as it can prompt exploration for additional features associated with select intracranial pathologies, thus improving diagnostic accuracy. This article reviews basic anatomy of the perioptic space, current understanding of the CSF dynamics between the perioptic space and central nervous system SAS, various imaging modalities utilized in the assessment of the perioptic space, MRI sequences and the optimal parameters of specific sequences, normal appearance of the perioptic space on MR imaging, and various common pediatric pathologies which cause alteration in the perioptic space.

Sensitivity of magnetic resonance elastography to extracellular matrix and cell motility in human prostate cancer cell line-derived xenograft models.

Prostate cancer (PCa) is a significant health problem in the male population of the Western world. Magnetic resonance elastography (MRE), an emerging medical imaging technique sensitive to mechanical properties of biological tissues, detects PCa based on abnormally high stiffness and viscosity values. Yet, the origin of these changes in tissue properties and how they correlate with histopathological markers and tumor aggressiveness are largely unknown, hindering the use of tumor biomechanical properties for establishing a noninvasive PCa staging system. To infer the contributions of extracellular matrix (ECM) components and cell motility, we investigated fresh tissue specimens from two PCa xenograft mouse models, PC3 and LNCaP, using magnetic resonance elastography (MRE), diffusion-weighted imaging (DWI), quantitative histology, and nuclear shape analysis. Increased tumor stiffness and impaired water diffusion were observed to be associated with collagen and elastin accumulation and decreased cell motility. Overall, LNCaP, while more representative of clinical PCa than PC3, accumulated fewer ECM components, induced less restriction of water diffusion, and exhibited increased cell motility, resulting in overall softer and less viscous properties. Taken together, our results suggest that prostate tumor stiffness increases with ECM accumulation and cell adhesion - characteristics that influence critical biological processes of cancer development. MRE paired with DWI provides a powerful set of imaging markers that can potentially predict prostate tumor development from benign masses to aggressive malignancies in patients. STATEMENT OF SIGNIFICANCE: Xenograft models of human prostate tumor cell lines, allowing correlation of microstructure-sensitive biophysical imaging parameters with quantitative histological methods, can be investigated to identify hallmarks of cancer.