Enhancing automatic prediction of clinically significant prostate cancer with deep transfer learning 2.5-dimensional segmentation on bi-parametric magnetic resonance imaging (bp-MRI).

Background: The aggressiveness of prostate cancer (PCa) is crucial in determining treatment method. The purpose of this study was to establish a 2.5-dimensional (2.5D) deep transfer learning (DTL) detection model for the automatic detection of clinically significant PCa (csPCa) based on bi-parametric magnetic resonance imaging (bp-MRI). Methods: A total of 231 patients, including 181 with csPCa and 50 with non-clinically significant PCa (non-csPCa), were enrolled. Stratified random sampling was then employed to divide all participants into a training set [185] and a test set [46]. The DTL model was obtained through image acquisition, image segmentation, and model construction. Finally, the diagnostic performance of the 2.5D and 2-dimensional (2D) models in predicting the aggressiveness of PCa was evaluated and compared using receiver operating characteristic (ROC) curves. Results: DTL models based on 2D and 2.5D segmentation were established and validated to assess the aggressiveness of PCa. The results demonstrated that the diagnostic efficiency of the DTL model based on 2.5D was superior to that of the 2D model, regardless of whether in a single or combined sequence. Particularly, the 2.5D combined model outperformed other models in differentiating csPCa from non-csPCa. The area under the curve (AUC) values for the 2.5D combined model in the training and test sets were 0.960 and 0.949, respectively. Furthermore, the T2-weighted imaging (T2WI) model showed superiority over the apparent diffusion coefficient (ADC) model, but was not as effective as the combined model, whether based on 2.5D or 2D. Conclusions: A DTL model based on 2.5D segmentation was developed to automatically evaluate PCa aggressiveness on bp-MRI, improving the diagnostic performance of the 2D model. The results indicated that the continuous information between adjacent layers can enhance the detection rate of lesions and reduce the misjudgment rate based on the DTL model.

Magnetic resonance imaging-based prognostic model for subsequent distant metastasis in patients with ipsilateral breast tumor recurrence following breast-conserving surgery.

Background: Ipsilateral breast tumor recurrence (IBTR) following breast-conserving surgery (BCS) has been considered a risk factor for distant metastasis (DM). Limited data are available regarding the subsequent outcomes after IBTR. Therefore, this study aimed to determine the clinical course after IBTR and develop a magnetic resonance imaging (MRI)-based predictive model for subsequent DM. Methods: We retrospectively extracted quantitative features from MRI to construct a radiomics cohort, with all eligible patients undergoing preoperative MRI at time of primary tumor and IBTR between 2010 and 2018. Multivariate Cox analysis was performed to identify factors associated with DM. Three models were constructed using different sets of clinicopathological, qualitative, and quantitative MRI features and compared. Additionally, Kaplan-Meier analysis was performed to assess the prognostic value of the optimal model. Results: Among the 183 patients who experienced IBTR, 47 who underwent MRI for both primary and recurrent tumors were enrolled. Multivariate analysis demonstrated that the independent prognostic factors were human epidermal growth factor receptor 2 (HER2) status [hazard ratio (HR) =5.40] and background parenchymal enhancement (BPE) (HR =7.94) (all P values <0.01). Furthermore, four quantitative MRI features of recurrent tumors were selected through the least absolute shrinkage and selection operator (LASSO) method. The combined model exhibited superior performance [concordance index (C-index) 0.77] compared to the clinicoradiological model (C-index 0.71; P=0.006) and radiomics model (C-index 0.70; and P=0.01). Furthermore, the combined model successfully categorized patients into low- and high-risk subgroups with distinct prognoses (P<0.001). Conclusions: The clinicopathological and MRI features of IBTR were associated with secondary events following surgery. Additionally, the MRI-based combined model exhibited the highest predictive efficacy. These findings could be helpful in risk stratification and tailoring follow-up strategies in patients with IBTR.

CMAF-Net: a cross-modal attention fusion-based deep neural network for incomplete multi-modal brain tumor segmentation.

Background: The information between multimodal magnetic resonance imaging (MRI) is complementary. Combining multiple modalities for brain tumor image segmentation can improve segmentation accuracy, which has great significance for disease diagnosis and treatment. However, different degrees of missing modality data often occur in clinical practice, which may lead to serious performance degradation or even failure of brain tumor segmentation methods relying on full-modality sequences to complete the segmentation task. To solve the above problems, this study aimed to design a new deep learning network for incomplete multimodal brain tumor segmentation. Methods: We propose a novel cross-modal attention fusion-based deep neural network (CMAF-Net) for incomplete multimodal brain tumor segmentation, which is based on a three-dimensional (3D) U-Net architecture with encoding and decoding structure, a 3D Swin block, and a cross-modal attention fusion (CMAF) block. A convolutional encoder is initially used to extract the specific features from different modalities, and an effective 3D Swin block is constructed to model the long-range dependencies to obtain richer information for brain tumor segmentation. Then, a cross-attention based CMAF module is proposed that can deal with different missing modality situations by fusing features between different modalities to learn the shared representations of the tumor regions. Finally, the fused latent representation is decoded to obtain the final segmentation result. Additionally, channel attention module (CAM) and spatial attention module (SAM) are incorporated into the network to further improve the robustness of the model; the CAM to help focus on important feature channels, and the SAM to learn the importance of different spatial regions. Results: Evaluation experiments on the widely-used BraTS 2018 and BraTS 2020 datasets demonstrated the effectiveness of the proposed CMAF-Net which achieved average Dice scores of 87.9%, 81.8%, and 64.3%, as well as Hausdorff distances of 4.21, 5.35, and 4.02 for whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset, respectively, outperforming several state-of-the-art segmentation methods in missing modalities situations. Conclusions: The experimental results show that the proposed CMAF-Net can achieve accurate brain tumor segmentation in the case of missing modalities with promising application potential.

Prediction of glypican-3 expression in hepatocellular carcinoma using multisequence magnetic resonance imaging-based histology nomograms.

Background: Hepatocellular carcinoma (HCC) is often associated with the overexpression of multiple proteins and genes. For instance, patients with HCC and a high expression of the glypican-3 (GPC3) gene have a poor prognosis, and noninvasive assessment of GPC3 expression before surgery is helpful for clinical decision-making. Therefore, our primary aim in this study was to develop and validate multisequence magnetic resonance imaging (MRI) radiomics nomograms for predicting the expression of GPC3 in individuals diagnosed with HCC. Methods: We conducted a retrospective analysis of 143 patients with HCC, including 123 cases from our hospital and 20 cases from The Cancer Genome Atlas (TCGA) or The Cancer Imaging Archive (TCIA) public databases. We used preoperative multisequence MRI images of the patients for the radiomics analysis. We extracted and screened the imaging histologic features using fivefold cross-validation, Pearson correlation coefficient, and the least absolute shrinkage and selection operator (LASSO) analysis method. We used logistic regression (LR) to construct a radiomics model, developed nomograms based on the radiomics scores and clinical parameters, and evaluated the predictive performance of the nomograms using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: Our multivariate analysis results revealed that tumor morphology (P=0.015) and microvascular (P=0.007) infiltration could serve as independent predictors of GPC3 expression in patients with HCC. The nomograms integrating multisequence radiomics radiomics score, tumor morphology, and microvascular invasion had an area under the curve (AUC) value of 0.989. This approach was superior to both the radiomics model (AUC 0.979) and the clinical model (AUC 0.793). The sensitivity, specificity, and accuracy of 0.944, 0.800, and 0.913 for the test set, respectively, and the model's calibration curve demonstrated good consistency (Brier score =0.029). The decision curve analysis (DCA) indicated that the nomogram had a higher net clinical benefit for predicting the expression of GPC3. External validation of the model's prediction yielded an AUC value of 0.826. Conclusions: Our study findings highlight the close association of multisequence MRI imaging and radiomic features with GPC3 expression. Incorporating clinical parameters into nomograms can offer valuable preoperative insights into tailoring personalized treatment plans for patients diagnosed with HCC.

Quantification of volumetric thigh and paravertebral muscle fat content: comparison of quantitative Dixon (Q-Dixon) magnetic resonance imaging (MRI) with high-speed T2-corrected multiecho MR spectroscopy.

Background: Muscle fat infiltration (MFI) is increasingly recognized as a critical factor influencing muscle function and metabolic health. Accurate quantification of MFI is essential for diagnosing and monitoring various muscular and metabolic disorders. Quantitative Dixon (Q-Dixon) magnetic resonance imaging (MRI) and high-speed T2-corrected multi-echo (HISTO) magnetic resonance spectroscopy (MRS) are both advanced imaging techniques that offer potential for detailed assessment of MFI. However, the validity and reliability of these methods in measuring volumetric changes in muscle composition, particularly in both thigh and paravertebral muscles, have not been thoroughly compared. This study aims to validate volumetric measurements using Q-Dixon MRI against HISTO MRS in thigh and paravertebral muscles, taking into account the heterogeneity of MFI. Methods: A retrospective study was conducted with 54 subjects [mean age, 60 years; 38 male (M)/16 female (F)] for thigh muscle and 56 subjects (mean age, 50 years; 22 M/34 F) for paravertebral muscle assessment using a 3-Tesla MRI. The proton density fat fraction (PDFF) was measured with Q-Dixon MRI and HISTO MRS within the upper-middle part of quadriceps femoris and paravertebral muscles at L4/5 level in volumes-of-interest (VOIs). The corresponding volumetric Q-Dixon freehand VOI PDFF was measured. Scatterplots, Bland-Altman plots, Spearman correlation coefficients, and Wilcoxon signed rank test with Bonferroni correction were employed. The Kruskal-Wallis H tests followed by Bonferroni-corrected post hoc tests were analyzed to compare parameter differences with visual MFI grades. Results: Q-Dixon cubic VOI PDFF correlated positively with HISTO MRS PDFF in thigh (r=0.96, P<0.001) and paravertebral groups (r=0.98, P<0.001), with insignificant differences (P=0.29, 0.82, respectively). Both PDFF values from cubic VOIs in Q-Dixon and HISTO MRS differed from the freehand Q-Dixon PDFF (all P<0.001). Only for <5% HISTO MRS PDFF, there was a difference between HISTO MRS PDFF and Q-Dixon cubic VOI PDFF (P=0.002). Conclusions: Volumetric Q-Dixon cubic VOI PDFF exhibited good correlation and consistency with HISTO MRS PDFF for quantitative fat assessment in thigh and paravertebral muscles except for muscles with fat fraction <5%, and the Q-Dixon freehand VOI PDFF offers a more comprehensive assessment of the actual MFI compared to cubic VOI.

Nomogram of magnetic resonance imaging (MRI) histogram analysis to predict telomerase reverse transcriptase promoter mutation status in glioblastoma.

Background: Telomerase reverse transcriptase promoter (pTERT) status is a strong biomarker to diagnose and predict the prognosis of glioblastoma (GBM). In this study, we explored the predictive value of preoperative magnetic resonance imaging (MRI) histogram analysis in the form of nomogram for evaluating pTERT mutation status in GBM. Methods: The clinical and imaging data of 181 patients with GBM at our hospital between November 2018 and April 2023 were retrospectively assessed. We used the molecular sequencing results to classify the datasets into pTERT mutations (C228T and C250T) and pTERT-wildtype groups. FireVoxel software was used to extract preoperative T1-weighted contrast-enhanced (T1C) histogram parameters of GBM patients. The T1C histogram parameters were compared between groups. Univariate and multivariate logistic regression analyses were used to construct the nomogram, and the predictive efficacy of model was evaluated using calibration and decision curves. Receiver operating characteristic curve was used to assess model performance. Results: Patient age and percentage of unenhanced tumor area showed statistically significant differences between the pTERT mutation and pTERT-wildtype groups (P<0.001). Among the T1C histogram features, the maximum, standard deviation (SD), variance, coefficient of variation (CV), skewness, 5th, 10th, 25th, 95th and 99th percentiles were statistically significantly different between groups (P=0.000-0.040). Multivariate logistic regression analysis showed that age, percentage of unenhanced tumor area, SD and CV were independent risk factors for predicting pTERT mutation status in GBM patients. The logistic regression model based on these four features showed a better sample predictive performance, and the area under the curve (AUC) [95% confidence interval (CI)], accuracy, sensitivity, specificity were 0.842 (0.767-0.917), 0.796, 0.820, and 0.729, respectively. There were no significant differences in the T1C histogram parameters between the C228T and C250T groups (P=0.055-0.854). Conclusions: T1C histogram parameters can be used to evaluate pTERT mutations status in GBM. A nomogram based on conventional MRI features and T1C histogram parameters is a reliable tool for the pTERT mutation status, allowing for non-invasive radiological prediction before surgery.

Deep learning methods for diagnosis of graves' ophthalmopathy using magnetic resonance imaging.

Background: The effect of diagnosing Graves' ophthalmopathy (GO) through traditional measurement and observation in medical imaging is not ideal. This study aimed to develop and validate deep learning (DL) models that could be applied to the diagnosis of GO based on magnetic resonance imaging (MRI) and compare them to traditional measurement and judgment of radiologists. Methods: A total of 199 clinically verified consecutive GO patients and 145 normal controls undergoing MRI were retrospectively recruited, of whom 240 were randomly assigned to the training group and 104 to the validation group. Areas of superior, inferior, medial, and lateral rectus muscles and all rectus muscles on coronal planes were calculated respectively. Logistic regression models based on areas of extraocular muscles were built to diagnose GO. The DL models named ResNet101 and Swin Transformer with T1-weighted MRI without contrast as input were used to diagnose GO and the results were compared to the radiologist's diagnosis only relying on MRI T1-weighted scans. Results: Areas on the coronal plane of each muscle in the GO group were significantly greater than those in the normal group. In the validation group, the areas under the curve (AUCs) of logistic regression models by superior, inferior, medial, and lateral rectus muscles and all muscles were 0.897 [95% confidence interval (CI): 0.833-0.949], 0.705 (95% CI: 0.598-0.804), 0.799 (95% CI: 0.712-0.876), 0.681 (95% CI: 0.567-0.776), and 0.905 (95% CI: 0.843-0.955). ResNet101 and Swin Transformer achieved AUCs of 0.986 (95% CI: 0.977-0.994) and 0.936 (95% CI: 0.912-0.957), respectively. The accuracy, sensitivity, and specificity of ResNet101 were 0.933, 0.979, and 0.869, respectively. The accuracy, sensitivity, and specificity of Swin Transformer were 0.851, 0.817, and 0.898, respectively. The ResNet101 model yielded higher AUC than models of all muscles and radiologists (0.986 vs. 0.905, 0.818; P<0.001). Conclusions: The DL models based on MRI T1-weighted scans could accurately diagnose GO, and the application of DL systems in MRI may improve radiologists' performance in diagnosing GO and early detection.

Multiparametric quantitative magnetic resonance imaging of uterine fibroids for prediction of growth rate-a pilot study.

Background: Uterine fibroid (UF) growth rate and future morbidity cannot be predicted. This can lead to sub-optimal clinical management, with women being lost to follow-up and later presenting with severe disease that may require hospitalization, transfusions, and urgent surgical interventions. Multi-parametric quantitative magnetic resonance imaging (MRI) could provide a biomarker to predict growth rate facilitating better-informed disease management and better clinical outcomes. We assessed the ability of putative quantitative and qualitative MRI predictive factors to predict UF growth rate. Methods: Twenty women with UFs were recruited and completed baseline and follow-up MRI exams, 1-2.5 years apart. The subjects filled out symptom severity and health-related quality of life questionnaires at each visit. A standard clinical pelvic MRI non-contrast exam was performed at each visit, followed by a contrast-enhanced multi-parametric quantitative MRI (mp-qMRI) exam with T2, T2*, and apparent diffusion coefficient (ADC) mapping and dynamic contrast-enhanced MRI. Up to 3 largest fibroids were identified and outlined on the T2-weighted sequence. Fibroid morphology and enhancement patterns were qualitatively assessed on dynamic contrast-enhanced MRI. The UFs' volumes and average T2, T2*, and ADC values were calculated. Pearson correlation coefficients were calculated between UF growth rate and T2, T2*, ADC, and baseline volume. Multiple logistic regression and receiver operating characteristic (ROC) analysis were performed to predict fast-growing UFs using combinations of up to 2 significant predictors. A significance level of alpha =0.05 was used. Results: Forty-four fibroids in 20 women had growth rate measurement available, and 36 fibroids in 16 women had follow-up quantitative MRI available. The distribution of fibroid growth rate was skewed, with approximately 20% of the fibroids exhibiting fast growth (>10 cc/year). However, there were no significant changes in median baseline and follow-up values of symptom severity and health-related quality of life scores. There was no change in average T2, T2*, and ADC at follow-up exams and there was a moderate to strong correlation to the fibroid growth rate in baseline volume and average T2 and ADC in slow-growing fibroids (<10 cc/year). A multiple logistic regression to identify fast growing UFs (>10 cc/year) achieved an area under the curve (AUC) of 0.80 with specificity of 69% at 100% sensitivity. Conclusions: The mp-qMRI parameters T2, ADC, and UF volume obtained at the time of initial fibroid diagnosis may be able to predict UF growth rate. Mp-qMRI could be integrated into the management of UFs, for individualized care and improved clinical outcomes.

Accelerated cardiac cine magnetic resonance imaging using deep low-rank plus sparse network: validation in patients.

Background: Accurate and reproducible assessment of left ventricular (LV) volumes is important in managing various cardiac conditions. However, patients are required to hold their breath multiple times during data acquisition, which may result in discomfort and restrict cardiac motion, potentially compromising the accuracy of the detected results. Accelerated imaging techniques can help reduce the number of breath holds needed, potentially improving patient comfort and the reliability of the LV assessment. This study aimed to prospectively evaluate the feasibility and accuracy of LV assessment with a model-based low-rank plus sparse network (L+S-Net) for accelerated magnetic resonance (MR) cine imaging. Methods: Fourty-one patients with different cardiac conditions were recruited in this study. Both accelerated MR cine imaging with L+S-Net and traditional electrocardiogram (ECG)-gated segmented cine were performed for each patient. Subjective image quality (IQ) score and quantitative LV volume function parameters were measured and compared between L+S-Net and traditional standards. The IQ score and LV volume measurements of cardiovascular magnetic resonance (CMR) images reconstructed by L+S-Net and standard cine were compared by paired t-test. The acquisition time of the two methods was also calculated. Results: In a quantitative analysis, L+S-Net and standard cine yielded similar measurements for all parameters of LV function (ejection fraction: 35±22 for standard vs. 33±23 for L+S-Net), although L+S-Net had slightly lower IQ scores than standard cine CMR (4.2±0.5 for L+S-Net vs. 4.8±0.4 for standard cine; P<0.001). The mean acquisition time of L+S-Net and standard cine was 0.83±0.08 vs. 6.35±0.78 s per slice (P<0.001). Conclusions: Assessment of LV function with L+S-Net at 3.0 T yields comparable results to the reference standard, albeit with a reduced acquisition time. This feature enhances the clinical applicability of the L+S-Net approach, helping alleviate patient discomfort and motion artifacts that may arise due to prolonged acquisition time.

The Importance of Primary Care Cognitive Evaluation in Detecting an Atypical Presentation of Normal Pressure Hydrocephalus.

Normal pressure hydrocephalus (NPH) is a syndrome that characteristically presents with progressive gait impairment, cognitive deficits, and urinary urgency or incontinence. We present a case of a 54-year-old male with a past medical history of alcohol use and no primary care provider with new-onset auditory hallucinations. The patient was found to have a marked enlargement of the supratentorial and infratentorial ventricles on both computed tomography (CT) and magnetic resonance imaging (MRI) and an opening pressure of 21 on the lumbar puncture, concerning for NPH. Clinically, there were signs of cognitive impairment due to memory and cognitive function loss, but the patient lacked gait disturbances or incontinence. Although not common, NPH may present with auditory hallucinations or delusions, as seen with our patient. In this case report, we emphasize the importance of annual cognitive assessments in order to evaluate atypical psychiatric manifestations of neurological disorders. Because clinical symptoms are more likely to be reversible when recognized early in the clinical course and the progression of these symptoms can be prevented with the placement of a ventriculoperitoneal (VP) shunt, it is of utmost importance to accurately recognize and diagnose NPH as early as possible. We also discuss the less commonly known markers of NPH on MRI.

Assessment of resting cerebral perfusion between methamphetamine-associated psychosis and schizophrenia through arterial spin labeling MRI.

OBJECTIVE: The clinical manifestations of methamphetamine (METH)-associated psychosis (MAP) and acute paranoid schizophrenia (SCZ) are similar. This study aims to assess regional cerebral blood flow (rCBF) in individuals who use METH and in those with SCZ using the MRI arterial spin labeling (ASL) technique. METHODS: We prospectively recruited 68 participants and divided them into four groups: MAP (N = 15), SCZ (N = 13), METH users with no psychosis (MNP; N = 22), and normal healthy controls (CRL; N = 18). We measured rCBF using an MRI three-dimensional pseudo-continuous ASL sequence. Clinical variables were assessed using the Positive and Negative Syndrome Scale (PANSS) and Brief Assessment of Cognition in Schizophrenia (BACS). Group-level rCBF differences were analyzed using a two-sample t-test. RESULTS: Decreased rCBF was found in the precuneus, premotor cortex, caudate nucleus, dorsolateral prefrontal cortex, and thalamus in the MNP group compared with the CRL group. The MAP group had significantly decreased rCBF in the precuneus, hippocampus, anterior insula, inferior temporal gyrus, inferior orbitofrontal gyrus, and superior occipital gyrus compared with the MNP group. Increased rCBF in the precuneus and premotor cortex was seen in the MAP group compared with the SCZ group. rCBF in the precuneus and premotor cortex significantly correlated negatively with the PANSS but correlated positively with BACS scores in the MAP and SCZ groups. CONCLUSION: METH exposure was associated with decreased rCBF in the precuneus and premotor cortex. Patients with MAP exhibited higher rCBF than those with SCZ, implying preserved insight and favorable outcomes. rCBF can therefore potentially serve as a diagnostic approach to differentiate patients with MAP from those with SCZ.

Reversible splenial lesion syndrome in Anti-N-methyl-D-aspartate receptor encephalitis.

Here we described an 18-year-old woman who were initially misdiagnosed as psychiatric disorders in a psychiatric institution. She was transferred to our neurological ward because of impaired consciousness. Neuronal antibody testing confirmed the diagnosis of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis. Cerebral magnetic resonance imaging (MRI) revealed a concomitant disorder named reversible splenial lesion syndrome (RESLES). After administration of combined immunotherapy, the patient recovered completely 3 months after discharge. To our knowledge, co-occurrence of RESLES and anti-NMDAR encephalitis was only described in two patients with teratoma and we provide another case without teratoma. We highlight that anti-NMDAR antibodies can be added to the multiple causes of RESLES. It is therefore imperative for clinicians to detect anti-neuronal antibodies in patients with RESLES to avoid missed diagnosis.

Multivariate analysis of multimodal brain structure predicts individual differences in risk and intertemporal preference.

Large changes to brain structure (e.g., from damage or disease) can explain alterations in behavior. It is therefore plausible that smaller structural differences in healthy samples can be used to better understand and predict individual differences in behavior. Despite the brain's multivariate and distributed structure-to-function mapping, most studies have used univariate analyses of individual structural brain measures. Here we used a multivariate approach in a multimodal data set composed of volumetric, surface-based, diffusion-based, and functional resting-state MRI measures to predict reliable individual differences in risk and intertemporal preferences. We show that combining twelve brain structure measures led to better predictions across tasks than using any individual measure, and by examining model coefficients, we visualize the relative contribution of different brain measures from different brain regions. Using a multivariate approach to brain structure-to-function mapping that combines across many brain structure properties, along with reliably measured behavior phenotypes, may increase out-of-sample prediction accuracies and insight into neural underpinnings. Furthermore, this methodological approach may be useful to improve predictions and neural insight across basic, translational, and clinical research fields.

Diagnostic performance of magnetic resonance imaging features to differentiate adrenal pheochromocytoma from adrenal tumors with positive biochemical testing results.

BACKGROUND: It is extremely essential to accurately differentiate pheochromocytoma from Adrenal incidentalomas (AIs) before operation, especially biochemical tests were inconclusive. We aimed to evaluate the value of magnetic resonance imaging (MRI) features to differentiate pheochromocytomas among adrenal tumors, among which the consequences of biochemical screening tests of catecholamines and/or catecholamine metabolites are positive. METHODS: With institutional review board approval, this study retrospectively compared 35 pheochromocytoma (PHEO) patients with 27 non-pheochromocytoma(non-PHEO) patients between January 2022 to September 2023, among which the consequences of biochemical screening tests of catecholamines and/or catecholamine metabolites are positive. T test was used for the independent continuous data and the chi-square test was used for categorical variables. Univariate and multivariate logistic regression were applied to find the independent variate of the features to differentiate PHEO from non-PHEO and ROC analysis was applied to evaluate the diagnostic value of the independent variate. RESULTS: We found that the T2-weighted (T2W) signal intensity in patients with pheochromocytoma was higher than other adrenal tumors, with greatly significant (p < 0.001). T2W signal intensity ratio (T2W nodule-to-muscle SI ratio) was an independent risk factor for the differential diagnosis of adrenal PHEOs from non-PHEOs. This feature alone had 91.4% sensitivity and 81.5% specificity to rule out pheochromocytoma based on optimal threshold, with an area under the receiver operating characteristics curve (AUC­ROC) of 0.910(95% C I: 0.833-0.987). CONCLUSION: Our study confirms that T2W signal intensity ratio can differentiate PHEO from non-PHEO, among which the consequences of biochemical screening tests of catecholamines and/or catecholamine metabolites are positive.

"Back to Braak": Role of Nucleus Reuniens and Subcortical Pathways in Alzheimer's Disease Progression.

BACKGROUND: Patients with Alzheimer's Disease (AD) exhibit structural alterations of the thalamus that correlate with clinical symptoms. However, given the anatomical complexity of this brain structure, it is still unclear whether atrophy affects specific thalamic nuclei and modulates the clinical progression from a prodromal stage, known as Mild Cognitive Impairment (MCI), to full-fledged AD. OBJECTIVES: To characterize the structural integrity of distinct thalamic nuclei across the AD spectrum, testing whether MCI patients who convert to AD (c-MCI) show a distinctive pattern of thalamic structural alterations compared to patients who remain stable (s-MCI). DESIGN: Investigating between-group differences in the volumetric features of distinct thalamic nuclei across the AD spectrum. SETTING: Prodromal and clinical stages of AD. PARTICIPANTS: We analyzed data from 84 healthy control subjects (HC), 58 individuals with MCI, and 102 AD patients. The dataset was obtained from the AD Neuroimaging Initiative (ADNI-3) database. The MCI group was further divided into two subgroups depending on whether patients remained stable (s-MCI, n=22) or progressed to AD (s-MCI, n=36) in the 48 months following the diagnosis. MEASUREMENTS: A multivariate analysis of variance (MANOVA) assessed group differences in the volumetric features of distinct thalamic nuclei obtained from magnetic resonance (MR) images. A stepwise discriminant function analysis identified which feature most effectively predicted the conversion to AD. The corresponding predictive performance was evaluated through a Receiver Operating Characteristic approach. RESULTS: AD and c-MCI patients showed generalized atrophy of thalamic nuclei compared to HC. In contrast, no significant structural differences were observed between s-MCI and HC subjects. Compared to s-MCI, c-MCI individuals displayed significant atrophy of the nucleus reuniens and a trend toward significant atrophy in the anteroventral and laterodorsal nuclei. The discriminant function analysis confirmed the nucleus reuniens as a significant predictor of AD conversion, with a sensitivity of 0.73 and a specificity of 0.69. CONCLUSIONS: In line with the pathophysiological relevance of the nucleus reuniens proposed by seminal post-mortem studies on patients with AD, we confirm the pivotal role of this nucleus as a critical hub in the clinical progression to AD. We also propose a theoretical model to explain the evolving dysfunction of subcortical brain networks in the disease process.

Pancreatic ductal adenocarcinoma: the latest on diagnosis, molecular profiling, and systemic treatments.

Pancreatic ductal adenocarcinoma (PDAC) is currently the fourth leading cause of death in the United States and is expected to be ranked second in the next 10 years due to poor prognosis and a rising incidence. Distant metastatic PDAC is associated with the worst prognosis among the different phases of PDAC. The diagnostic options for PDAC are convenient and available for staging, tumor response evaluation, and management of resectable or borderline resectable PDAC. However, imaging is crucial in PDAC diagnosis, monitoring, resectability appraisal, and response evaluation. The advancement of medical technologies is evolving, hence the use of imaging in PDAC treatment options has grown as well as the utilization of ctDNA as a tumor marker. Treatment options for metastatic PDAC are minimal with the primary goal of therapy limited to symptom relief or palliation, especially in patients with low functional capacity at the point of diagnosis. Molecular profiling has shown promising potential solutions that would push the treatment boundaries for patients with PDAC. In this review, we will discuss the latest updates from evidence-based guidelines regarding diagnosis, therapy response evaluation, prognosis, and surveillance, as well as illustrating novel therapies that have been recently investigated for PDAC, in addition to discussing the molecular profiling advances in PDAC.

Dosimetry of [64Cu]FBP8: a fibrin-binding PET probe.

Purpose: This study presents the biodistribution, clearance and dosimetry estimates of [64Cu]Fibrin Binding Probe #8 ([64Cu]FBP8) in healthy subjects. Procedures: This prospective study included 8 healthy subjects to evaluate biodistribution, safety and dosimetry estimates of [64Cu]FBP8, a fibrin-binding positron emission tomography (PET) probe. All subjects underwent up to 3 sessions of PET/Magnetic Resonance Imaging (PET/MRI) 0-2 hours, 4h and 24h post injection. Dosimetry estimates were obtained using OLINDA 2.2 software. Results: Subjects were injected with ~400 MBq of [64Cu]FBP8. Subjects did not experience adverse effects due to the injection of the probe. [64Cu]FBP8 PET images demonstrated fast blood clearance (half-life = 67 min) and renal excretion of the probe, showing low background signal across the body. The organs with the higher doses were: the urinary bladder (0.075 vs. 0.091 mGy/MBq for males and females, respectively); the kidneys (0.050 vs. 0.056 mGy/MBq respectively); and the liver (0.027 vs. 0.035 mGy/MBq respectively). The combined mean effective dose for males and females was 0.016 ± 0.0029 mSv/MBq, lower than the widely used [18F]fluorodeoxyglucose ([18F]FDG, 0.020mSv/MBq). Conclusions: This study demonstrates the following properties of the [64Cu]FBP8 probe: low dosimetry estimates; fast blood clearance and renal excretion; low background signal; and whole-body acquisition within 20 minutes in a single session. These properties provide the basis for [64Cu]FBP8 to be an excellent candidate for whole-body non-invasive imaging of fibrin, an important driver/feature in many cardiovascular, oncological and neurological conditions.

Pre-Draft Elbow Magnetic Resonance Imaging in Major League Baseball Pitchers.

BACKGROUND: Prior to the Major League Baseball (MLB) draft, some pitchers undergo pre-draft magnetic resonance imaging (MRI). This study aimed to evaluate pre-draft elbow MRI on baseball pitchers who were entering the MLB draft to determine the presence or absence of pathology, the associations between these pathologies and UCL tears, and inter-observer reliability regarding common MRI pathology. METHODS: Pre-draft elbow MRI performed on prospective MLB pitchers between 2011-2017 were de-identified and then reviewed by two separate authors. The authors graded the MRI on several factors including presence or absence of: UCL ossification, UCL appearance (heterogeneous or not), UCL thickening (and location), UCL tear (partial vs. full thickness and location), muscle strain, flexor tendon tear, posteromedial osteophyte, sublime tubercle enthesophyte, and osseous stress reactions. RESULTS: Overall, 245 pre-draft elbow MRI were reviewed. MRI abnormalities were found in 70% (171/245) of pitchers. UCL thickening was found in 20% (50/245) of pitchers. Regarding UCL tears, 3% had a full thickness tear and 24% had a partial thickness tear. Of full thickness tears, 86% were distal and one was midsubstance. Of partial thickness tears, 41% (24/58) were distal, 12% (7/58) were midsubstance, and 47% (27/58) were proximal. Periligamentous edema was present in 36% of pitchers while 14% had a flexor pronator muscle strain. CONCLUSION: The majority (70%) of pitchers entering the MLB draft had abnormal findings on their MRI, most commonly involving changes to the UCL. Inter-observer reliability was acceptable following the definition of pathology when reading pre-draft elbow MRI on MLB prospects.

3D Printed Drug Delivery Systems in Action-Magnetic Resonance Imaging and Relaxometry for Monitoring Mass Transport Phenomena.

The hypothesis of the study was that (1) 3D printed drug delivery systems (DDS) could be characterized in situ during drug release using NMR/MRI techniques in terms of mass transport phenomena description (interfacial phenomena), particularly for systems dealing with two mobile phases (e.g., water and low molecular weight liquid polymer); (2) consequently, it could be possible to deduce how these interfacial mass transport phenomena influence functional properties of 3D printed DDS. Matrix drug delivery systems, prepared using masked stereolithography (MSLA), containing poly(ethylene glycol) diacrylate (PEGDA) and low molecular weight polyethylene glycol (PEG) with ropinirole hydrochloride (RH) were studied as example formulations. The PEGDA to PEG (mobile phase) concentration ratio influenced drug release. It was reflected in spatiotemporal changes in parametric T2 relaxation time (T2) and amplitude (A) images obtained using magnetic resonance imaging (MRI) and T1-T2 relaxation time correlations obtained using low-field time-domain nuclear magnetic resonance (LF TD NMR) relaxometry during incubation in water. For most of the tested formulations, two signal components related to PEG and water were assessed in the hydrated matrices by MRI relaxometry (parametric T2/A images). The PEG component faded out due to outward PEG diffusion and was gradually replaced by the water component. Both components spatially and temporally changed their parameters, reflecting evolving water-polymer interactions. The study shows that dynamic phenomena related to bidirectional mass transport can be quantified in situ using NMR and MRI techniques to gain insight into drug release mechanisms from 3D printed DDS systems.

Technical Note: Swing golden angle - A navigator-interleaved golden angle trajectory with eddy current suppression - Application in free-running cardiac MRI.

BACKGROUND: Golden angle (GA) radial trajectory is advantageous for dynamic magnetic resonance imaging (MRI). Recently, several advanced algorithms have been developed based on navigator-interleaved GA trajectory to realize free-running cardiac MRI. However, navigator-interleaved GA trajectory suffers from the eddy-current effect, which reduces the image quality. PURPOSE: This work aims to integrate the navigator-interleaved GA trajectory with clinical cardiac MRI acquisition, with the minimum eddy-current artifacts. The ultimate goal is to realize a high-quality free-running cardiac imaging technique. METHODS: In this paper, we propose a new "swing golden angle" (swingGA) radial profile order. SwingGA samples the k-space by rotating back and forth at the generalized golden ratio interval, with smoothly interleaved navigator readouts. The sampling efficiency and angle increment distributions were investigated by numerical simulations. Static phantom imaging experiments were conducted to evaluate the eddy current effect, compared with cartesian, golden angle radial (GA), and tiny golden angle (tGA) trajectories. Furthermore, 12 heart-healthy subjects (aged 21-25 years) were recruited for free-running cardiac imaging with different sampling trajectories. Dynamic images were reconstructed by a low-rank subspace-constrained algorithm. The image quality was evaluated by signal-to-noise-ratio and spectrum analysis in the heart region, and compared with traditional clinical cardiac MRI images. RESULTS: SwingGA pattern achieves the highest sampling efficiency (mSE > 0.925) and the minimum azimuthal angle increment (mAD < 1.05). SwingGA can effectively suppress eddy currents in static phantom images, with the lowest normalized root mean square error (nRMSE) values among radial trajectories. For the in-vivo cardiac images, swingGA enjoys the highest SNR both in the blood pool and myocardium, and contains the minimum level of high-frequency artifacts. The free-running cardiac images have good consistency with traditional clinical cardiac MRI, and the swingGA sampling pattern achieves the best image quality among all sampling patterns. CONCLUSIONS: The proposed swingGA sampling pattern can effectively improve the sampling efficiency and reduce the eddy currents for the navigator-interleaved GA sequence. SwingGA is a promising sampling pattern for free-running cardiac MRI.