Dynamic Contrast-enhanced Research Articles

A timeline study on vascular co-morbidity induced cerebral endothelial dysfunction assessed by perfusion MRI

Endothelial dysfunction is considered a key element in the early pathogenesis of neurodegenerative disorders. Dysfunction of the cerebral endothelial cells can result in dysregulation of cerebral perfusion and disruption of the Blood Brain Barrier (BBB), leading to brain damage, neurodegeneration and cognitive decline. It has been shown that the presence of modifiable risk factors exacerbates endothelial dysfunction. This study primarily aimed to identify which among various perfusion MRI methodologies could be effectively utilized to non-invasively identify early pathological alterations as a result of endothelial dysfunction. We compared these perfusion MRI measurements to invasive immunohistochemistry to detect early pathological alterations in the cerebral vasculature of a rat model of multiple cardiovascular co-morbidities (the ZSF1 Obese rat) at several stages of the cerebrovascular pathology. We observed cerebral hyperperfusion, expressed by increased Cerebral Blood Flow (CBF) and increased BBB permeability in the ZSF1 Obese rats, at an early stage of disease development. The increase in CBF observed with Arterial Spin Labeling (ASL) was lost during later stages of disease progression. These findings are in line with recent clinical findings in early stages of Alzheimer's disease (AD), that also show early increases in CBF.

SwinUNeCCt: bidirectional hash-based agent transformer for cervical cancer MRI image multi-task learning

Cervical cancer is the fourth most common malignant tumor among women globally, posing a significant threat to women’s health. In 2022, approximately 600,000 new cases were reported, and 340,000 deaths occurred due to cervical cancer. Magnetic resonance imaging (MRI) is the preferred imaging method for diagnosing, staging, and evaluating cervical cancer. However, manual segmentation of MRI images is time-consuming and subjective. Therefore, there is an urgent need for automatic segmentation models to identify cervical cancer lesions in MRI scans accurately. All MRIs in our research are from cervical cancer patients diagnosed by pathology at Tongren City People’s Hospital. Strict data selection criteria and clearly defined inclusion and exclusion conditions were established to ensure data consistency and accuracy of research results. The dataset contains imaging data from 122 cervical cancer patients, with each patient having 100 pelvic dynamic contrast-enhanced MRI scans. Annotations were jointly completed by medical professionals from Universiti Putra Malaysia and the Radiology Department of Tongren City People’s Hospital to ensure data accuracy and reliability. Additionally, a novel computer-aided diagnosis model named SwinUNeCCt is proposed. This model incorporates (i) A bidirectional hash-based agent multi-head self-attention mechanism, which optimizes the interaction between local and global features in MRI, aiding in more accurate lesion identification. (ii) Reduced computational complexity of the self-attention mechanism. The effectiveness of the SwinUNeCCt model has been validated through comparisons with state-of-the-art 3D medical models, including nnUnet, TransBTS, nnFormer, UnetR, UnesT, SwinUNetR, and SwinUNeLCsT. In semantic segmentation tasks without a classification module, the SwinUNeCCt model demonstrates excellent performance across multiple key metrics: achieving a 95HD of 6.25, an IoU of 0.669, and a DSC of 0.802, all of which are the best results among the compared models. Simultaneously, SwinUNeCCt strikes a good balance between computational efficiency and model complexity, requiring only 442.7 GFLOPs of computational power and 71.2 M parameters. Furthermore, in semantic segmentation tasks that include a classification module, the SwinUNeCCt model also exhibits powerful recognition capabilities. Although this slightly increases computational overhead and model complexity, its performance surpasses other comparative models. The SwinUNeCCt model demonstrates excellent performance in semantic segmentation tasks, achieving the best results among state-of-the-art 3D medical models across multiple key metrics. It balances computational efficiency and model complexity well, maintaining high performance even with the inclusion of a classification module.

Automatic semantic segmentation of breast cancer in DCE-MRI using DeepLabV3+ with modified ResNet50

Research on breast cancer segmentation is essential due to its high prevalence as the most common cancer in women and its occurrence in men as well. Breast cancer involves abnormal cell growth in the breast, highlighting the importance of advanced imaging. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is an effective technique for this purpose. Deep learning has significantly influenced medical imaging in recent years, especially in accurately segmenting tumors from MRI images. Two techniques have been proposed for breast tumor segmentation: Dilated ResNet50 (RN50D) and Parallel Layers Added ResNet50 (PLA-RN50). RN50D involves altering the dilation factor of the convolution layer within the residual block of ResNet50. PLA-RN50 entails the integration of parallel layers following the final residual block of the ResNet50 architecture. The modified architectures serve as the backbone for the DeepLabV3+ network. The DeepLabV3+ with RN50D or PLA-RN50D is a powerful and effective architecture that integrates deep feature extraction, multiscale spatial information, and precise segmentation to achieve high accuracy in lesion segmentation for breast DCE-MRI images. The proposed technique is tested on a QIN Breast DCE-MRI dataset comprising 233 images sourced from The Cancer Image Archive. The proposed method achieves a dice score of 0.92. The superior segmentation performance of DeepLabV3+ with PLA-RN50, as compared to its counterparts using ResNet18 and ResNet50, highlights the impactful modifications incorporated in PLA-RN50 for optimizing breast tumor segmentation.

MRI-based vector radiomics for predicting breast cancer HER2 status and its changes after neoadjuvant therapy

Purpose: To develop a novel MRI-based vector radiomic approach to predict breast cancer (BC) human epidermal growth factor receptor 2 (HER2) status (zero, low, and positive; task 1) and its changes after neoadjuvant therapy (NAT) (positive-to-positive, positive-to-negative, and positive-to-pathologic complete response; task 2). Materials and Methods: Both dynamic contrast-enhanced (DCE) MRI data and multi-b-value (MBV) diffusion-weighted imaging (DWI) data were acquired in BC patients at two centers. Vector-radiomic and conventional-radiomic features were extracted from both DCE-MRI and MBV-DWI. After feature selection, the following models were built using the retained features and logistic regression: vector model, conventional model, and combined model that integrates the vector-radiomic and conventional-radiomic features. The models’ performances were quantified by the area under the receiver-operating characteristic curve (AUC). Results:The training/external test set (center 1/2) included 483/361 women. For task 1, the vector model (AUCs=0.73∼0.86) was superior to (p<.05) the conventional model (AUCs=0.68∼0.81), and the addition of vector-radiomic features to conventional-radiomic features yielded an incremental predictive value (AUCs=0.80∼0.90, p<.05). For task 2, the combined MBV-DWI model (AUCs=0.85∼0.89) performed better than (p<.05) the conventional MBV-DWI model (AUCs=0.73∼0.82). In addition, for the combined DCE-MRI model and the combined MBV-DWI model, the former (AUCs=0.85∼0.90) outperformed (p<.05) the latter (AUCs=0.80∼0.85) in task 1, whereas the latter (AUCs=0.85∼0.89) outperformed (p<.05) the former (AUCs=0.76∼0.81) in task 2. The above results are true for the training and external test sets. Conclusions:MRI-based vector radiomics may predict BC HER2 status and its changes after NAT and provide significant incremental prediction over and above conventional radiomics.

TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs

Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning models, lack explicit quantification of intricate and subtle parenchymal structures, including fibroglandular tissue. To address this, we propose a novel topological approach that explicitly extracts multi-scale topological structures to better approximate breast parenchymal structures, and then incorporates these structures into a deep-learning-based prediction model via an attention mechanism. Our topology-informed deep learning model, TopoTxR, leverages topology to provide enhanced insights into tissues critical for disease pathophysiology and treatment response. We empirically validate TopoTxR using the VICTRE phantom breast dataset, showing that the topological structures extracted by our model effectively approximate the breast parenchymal structures. We further demonstrate TopoTxR’s efficacy in predicting response to neoadjuvant chemotherapy. Our qualitative and quantitative analyses suggest differential topological behavior of breast tissue in treatment-naïve imaging, in patients who respond favorably to therapy as achieving pathological complete response (pCR) versus those who do not. In a comparative analysis with several baselines on the publicly available I-SPY 1 dataset (N = 161, including 47 patients with pCR and 114 without) and the Rutgers proprietary dataset (N = 120, with 69 patients achieving pCR and 51 not), TopoTxR demonstrates a notable improvement, achieving a 2.6% increase in accuracy and a 4.6% enhancement in AUC compared to the state-of-the-art method.

APOE 𝜀4-related blood-brain barrier breakdown is associated with microstructural abnormalities.

Blood-brain barrier (BBB) dysfunction occurs in Alzheimer's disease (AD). Yet, the stage at which it appears along the AD time course and whether it contributes to neurodegeneration remain unclear. Older adults (61 to 90 years) from cognitively normal (CN) to mildly cognitively impaired (CI), enriched for APOE 𝜀4 and amyloid positivity, underwent dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and diffusion MRI to measure BBB permeability and brain microstructure. Analysis of variance compared BBB permeability according to cognitive status, amyloid beta (Aβ), and APOE4. Linear regressions assessed associations of BBB permeability with brain microstructure and interactions with Aβ and APOE4. BBB permeability was elevated for APOE4 carriers across the cortical gray matter, with the strongest differences among CN amyloid-negative individuals. Associations between entorhinal BBB permeability and microstructure interacted with Aβ and APOE4, with the strongest relationships in amyloid-positive individuals and APOE4 carriers. APOE4 may drive widespread BBB dysfunction in preclinical AD, which may contribute to neurodegenerative changes early along the AD cascade. Gray matter blood-brain barrier (BBB) permeability is elevated for APOE4 carriers. APOE4-related BBB breakdown appears in the absence of cognitive decline or amyloid. BBB leakage correlates with entorhinal cortex microstructural injury. Associations with microstructure are strongest for amyloid-positive APOE4 carriers.

Role of Contrast Perfusion Weighted Magnetic Resonance Imaging in Grading of Brain Tumors with Histopathological Correlation

Background: Traditional MRI sequences provide precise anatomical information about brain tumors. But these sequences can’t quantitatively evaluate vascular physiology, or capture tumor biology at the molecular level; which is important for tumor grading, therapeutic assessment, and prognostication. Furthermore, non-enhancing parts of the tumor affected brain, which generally indicates peri-tumoral brain edema with infiltrative tumor cells, is not visible on conventional MRI sequences. Perfusion weighted Magnetic Resonance Imaging (PW-MRI) techniques such as Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) and Dynamic Susceptibility Contrast Magnetic Resonance Imaging (DSC-MRI) have shown promise as imaging biomarkers for glioma therapy, since these new sequences can also provide information about vascular hemodynamics. Our study was first prospective study from Himalayan belt of India, wherein we tried to assess the role of PW-MRI in the grading of brain tumors, with histopathological correlation. Aim and Objective: To compare tumor perfusion and histopathological tumor grading, in evaluation of brain tumor especially gliomas. Material and Method: 40 patients who were referred to Department of Radiodiagnosis for evaluation of brain tumor, and who gave informed consent, were included in this prospective study, done over a period of 18 months. All patients underwent MRI, followed by surgical resection. Observation and Result: In our study, diagnostic accuracy was 90% for mean rCBV (lesion) (cut off: 7 by ROC), mean rCBF (lesion) (cut off: 13.9 by ROC), and rCBF ratio (cut off: 2.9 by ROC); for differentiating benign versus malignant brain tumor. rCBF ratio was found to be best diagnostic parameter with sensitivity of 96%, specificity of 80%, positive predictive value of 89%, negative predictive value of 92%, diagnostic accuracy of 90%, area under ROC curve=0.909, and p value&lt;0.001. Conclusion: We come to conclusion that high grade brain tumor display higher CBF, than do low grade brain tumor. Three parameters showed good diagnostic accuracy, which includes rCBF ratio, mean rCBF (lesion), and mean rCBV (lesion). These parameters can be used to predict the grade of the brain tumor, with good diagnostic accuracy.

Perfusion MRI-based differentiation between early tumor progression and pseudoprogression in glioblastoma and its use in clinical practice

Abstract Background Early treatment effects in patients with glioblastoma are frequently discussed during multidisciplinary team meetings (MDTM), after which a decision regarding (dis)continuation of tumor-targeted treatment is made. This study examined whether a separate and systematic evaluation of perfusion MRI (pMRI) could impact such treatment decisions in the early stage. Methods This retrospective observational study evaluated the diagnostic accuracy for detecting early tumor progression of 4 different approaches including conventional MRI, pMRI with Arterial Spin Labeling (ASL), and/or Dynamic Susceptibility Contrast (DSC) MRI, and compared those to the MDTM evaluation in clinical practice. Results Sixty-five glioblastoma patients with clinical and radiological data until 9 months after irradiation were included. For all approaches, the sensitivity for detecting early true disease progression was poor to moderate (32%–62%). Area under the curve values were comparable (range 0.63–0.74), but highest for the MDTM evaluation (0.74). In the cases of inconclusive MDTM (26%), systematic pMRI evaluation showed a higher sensitivity compared to conventional MRI (respectively, 36% vs 0%), while the specificity was 100% for all MRI approaches. Multivariable regression analysis showed that a lower KPS score (OR = 0.84 [95% CI: 0.77–0.91]) and pMRI indicative of tumor progression (OR = 0.09 [95% CI: 0.02–0.52]) were independently associated with concluding tumor progression at the MDTM. Conclusion MDTM assessment in daily clinical practice has a higher diagnostic accuracy in distinguishing early tumor progression from pseudoprogression compared to a separate, systematic evaluation of pMRI. Systematic evaluation of pMRI might be helpful if the clinical MDTM assessment is uncertain.

Limited utility of subendometrial enhancement in assessing the interface between the endometrium and myometrium

This study was conducted to re-evaluate the utility of subendometrial enhancement (SEE) in assessing the interface between the endometrium and myometrium. In total, 110 women who underwent pelvic magnetic resonance imaging (MRI) for non-endometrial gynecologic diseases were enrolled in this prospective study. Two radiologists independently assessed the presence or absence of subendometrial enhancement (SEE) on dynamic contrast-enhanced (CE) MRI. A subgroup analysis was performed to evaluate the impact of menstrual status on the detection of SEE. Identified areas of SEE were rated using a 4-point Likert scale to determine their reliability in assessing the interface between the endometrium and myometrium. SEE was identified in 44.5% (49/110) of cases by radiologist 1 and in 39.1% (43/110) by radiologist 2. A subgroup analysis indicated no significant differences in the detection of SEE based on menstrual status. The identified areas of SEE were deemed reliable for assessing the interface between the endometrium and myometrium in 24.5% (12/49) of the cases evaluated by radiologist 1 and in 32.6% (14/43) of those evaluated by radiologist 2. Among the 110 women studied, reliable areas of SEE for assessing the interface between the endometrium and myometrium were observed in 10.9% (n = 12) by radiologist 1 and 12.7% (n = 14) by radiologist 2. The evaluation of SEE using dynamic CE MRI may be limited in its ability to assess the interface between the endometrium and myometrium.

Comprehensive biomarker and modeling approach to support dose finding for BI 836880, a VEGF/Ang-2 inhibitor

BackgroundBI 836880 is a humanized bispecific nanobody® that binds to and blocks vascular endothelial growth factor (VEGF) and angiopoietin-2 (Ang-2). A comprehensive biomarker and modeling approach is presented here that supported dose finding for BI 836880.MethodsTwo Phase I dose-escalation studies (1336.1 [NCT02674152], 1336.6 [NCT02689505]) assessed BI 836880 in adults with confirmed locally advanced or metastatic solid tumors, refractory to standard therapy or for which standard therapy was not reliably effective. Two dosing schedules were investigated, 3 weeks (q3w) or once weekly (qw), starting at a dose of 40 mg. In a comprehensive biomarker approach, soluble pharmacodynamic markers (free and total plasma VEGF-A and Ang-2), as well as circulating angiogenic factors (soluble VEGF3, soluble Tie2 and placenta growth factor, amongst others) were analyzed to assess target engagement in peripheral blood for q3w doses. A Population based pharmacokinetics/pharmacodynamics (PopPK/PD) model was built using the limited Phase I dataset to support dose finding by simulations. In order to demonstrate drug activity in the tumor, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was applied.ResultsDCE-MRI scans supported target engagement in the tumor. Free VEGF-A was depleted at all doses, whereas free Ang-2 decreased dose-dependently, reaching depletion in most patients from 360 mg q3w onwards. While total VEGF-A levels increased in a dose-dependent manner, reaching saturation at 360 mg q3w, total Ang-2 levels increased, but did not plateau. Angiogenic biomarkers showed changes from doses ≥ 360 mg q3w. PopPK/PD modeling showed that doses ≥ 360 mg q3w led to > 90% inhibition of free Ang-2 at steady-state in most patients. By increasing the dose to ≥ 500 mg q3w, > 90% of patients are expected to achieve this level.ConclusionsThe comprehensive analyses of multiple target engagement markers support BI 836880 720 mg q3w as a biologically relevant monotherapy dose schedule.Trial registration: NCT02674152 and NCT02689505.

Dynamic susceptibility contrast‑enhanced perfusion magnetic resonance imaging parameters for predicting MGMT promoter methylation and prognostic value in newly diagnosed patients with glioblastoma.

O6-methylguanine DNA methyltransferase (MGMT) promoter methylation is an important clinical biomarker of newly diagnosed glioblastoma. Previous radiological studies using dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) perfusion have aimed to predict MGMT methylation status non-invasively in gliomas with radiological characteristics. The possibility of predicting MGMT methylation status using DSC-MRI perfusion with a radiological approach remains controversial. The present study aimed to evaluate the usefulness of MRI perfusion parameters as non-invasive markers to predict MGMT methylation status and prognosis in newly diagnosed glioblastoma patients. Thus, 50 patients with histologically confirmed primary glioblastoma, IDH-wildtype who underwent tumor resection at Osaka University Hospital (Suita, Japan) between January 2017 and January 2023 were included in this study. The mean cerebral blood volume (CBV) ratio (rCBV) and cerebral blood flow (CBF) ratio (rCBF) for tumors with MGMT methylation (mean rCBV:2.09 and mean rCBF:3.08) were significantly higher compared with those for tumors without MGMT methylation (mean rCBV:1.33 and mean rCBF:1.85; P<0.05). While patients with MGMT methylation had longer progression-free survival (PFS) compared with those without MGMT methylation (P<0.05), there was no significant difference in OS with or without MGMT methylation (P=0.06). By contrast, there was no association between MRI perfusion parameters and OS or PFS in patients with glioblastoma. Furthermore, the association between CBV, CBF, MGMT promotor methylation status, OS, and PFS were explored. There was no significant prognostic difference between low vascularity tumors (rCBV <1.3 or rCBF <1.8) with or without MGMT methylation. On the other hand, high vascularity tumors (rCBF ≥1.8) with MGMT promotor methylation were associated to longer OS and PFS compared with those without. However, there was no association between MGMT methylation status and OS or PFS in patients with high rCBV (rCBV ≥1.3). The present study indicated that CBV and CBF could be used to predict the MGMT methylation status in glioblastomas. However, the prognostic value of tumor vascularity and MGMT methylation status may be limited.

The peritumoral brain zone in glioblastoma: a review of the pretreatment approach.

Glioblastomas are the most common and aggressive form of malignant primary brain tumors in adults. The standard treatment is surgical resection followed by radiotherapy and chemotherapy. Despite optimal treatment methods, the prognosis for patients remains poor. Preoperative determination of glioblastoma margins remains beneficial for the complete removal of the tumor mass. Radiotherapy is essential for post-surgery treatment, but radioresistance is a significant challenge contributing to high mortality rates. Advanced imaging technologies are used to analyze the changes in the peritumoral brain zone (PTZ). Consequently, they may lead to the development of novel therapeutic options, especially targeting the marginal parts of a tumor, which could improve the prognosis of glioblastoma patients. The clinical presentation of glioblastoma is heterogeneous and mostly depends on the location and size of a tumor. Glioblastomas are characterized by both intratumoral cellular heterogeneity and an extensive, diffuse infiltration into the normal tissue bordering a tumor called the PTZ. Neuroimaging techniques, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), proton magnetic resonance spectroscopy (1H MRS), and chemical exchange saturation transfer (CEST) are useful methods in the evaluation of the tumor infiltration and thus the resection margin.

Oxygen Extraction Fraction Mapping on Admission Magnetic Resonance Imaging May Predict Recovery of Hyperacute Ischemic Brain Lesions After Successful Thrombectomy: A Retrospective Observational Study.

In acute stroke, diffusion-weighted imaging (DWI) is used to assess the ischemic core. Dynamic-susceptibility contrast perfusion magnetic resonance imaging allows an estimation of the oxygen extraction fraction (OEF), but the outcome of DWI lesions with increased OEF postrecanalization is unclear. This study investigated the impact of OEF on the fate of DWI lesions in patients achieving recanalization after thrombectomy. This was a retrospective analysis of the HIBISCUS-STROKE cohort (Cohort of Patients to Identify Biological and Imaging Markers of Cardiovascular Outcomes in Stroke; NCT: 03149705), a single-center observational study that prospectively enrolled patients who underwent magnetic resonance imaging triage for thrombectomy and a day-6 T2-fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging. Automated postprocessing of admission dynamic-susceptibility contrast perfusion magnetic resonance imaging generated OEF maps. At visual analysis, the OEF status within DWI lesions was assessed in comparison to the contralateral side and correlated with volume changes (difference of ischemic lesion between admission DWI and registered day-6 T2-FLAIR). At voxel-based analysis, recovered DWI regions (lesions present on the admission DWI but absent on the registered day-6 T2-FLAIR) and nonrecovered regions were segmented to extract semiquantitative OEF values. Of the participants enrolled from 2016 to 2022, 134 of 321 (41.7%) were included (median age, 71.0 years; 58.2% male; median baseline National Institutes of Health Scale score, 15.0). At visual analysis, 46 of 134 (34.3%) patients had increased OEF within DWI lesions. These patients were more likely to show a reduction in ischemic lesion volumes compared with those without increased OEF (median change, -4.0 versus 4.8 mL; P<0.0001). Multivariable analysis indicated that increased OEF within DWI lesions was associated with a reduction in ischemic lesion volumes from admission DWI to day-6 T2-FLAIR (odds ratio, 0.68 [95% CI, 0.49-0.87]; P=0.008). At voxel-based analysis, recovered DWI regions had increased OEF, while nonrecovered regions had decreased OEF (median, 126.9% versus -27.0%; P<0.0001). Increased OEF within hyperacute DWI lesions was associated with ischemic lesion recovery between admission DWI and day-6 T2-FLAIR in patients achieving recanalization after thrombectomy. URL: https://www.clinicaltrials.gov; Unique identifier: NCT03149705.

Multi-Site Benchmark Study for Standardized Relative Cerebral Blood Volume in Untreated Brain Metastases Using the DSC-MRI Consensus Acquisition Protocol.

A national consensus recommendation for the collection of DSC (dynamic susceptibility contrast) MRI perfusion data, used to create maps of relative cerebral blood volume (rCBV), has been recently established for primary and metastatic brain tumors. The goal was to reduce inter-site variability and improve ease of comparison across time and sites, fostering widespread use of this informative measure. To translate this goal into practice the prospective collection of consensus DSC-MRI data and characterization of derived rCBV maps in brain metastases is needed. The purpose of this multi-site study was to determine rCBV in untreated brain metastases in comparison to glioblastoma and normal appearing brain using the national consensus protocol. Subjects from three sites with untreated enhancing brain metastases underwent DSC-MRI according to a recommended option that uses a mid-range flip angle, GRE-EPI acquisition and the administration of both a pre-load and 2nd DSC-MRI dose of 0.1 mmol/kg GBCA. Quantitative maps of standardized rCBV (sRCBV) were generated and enhancing lesion ROIs determined from post-contrast T1-weighted images alone or calibrated difference maps, termed delta T1 (dT1) maps. Mean sRCBV for metastases were compared to normal appearing white matter (NAWM) and glioblastoma (GBM) from a previous study. Comparisons were performed using either the Wilcoxon signed-rank test for paired comparisons or the Mann-Whitney nonparametric test for unpaired comparisons. 49 patients with a primary histology of lung (n=25), breast (n=6), squamous cell carcinoma (SCC) (n=1), melanoma (n=5), gastrointestinal (GI) (n=3) and genitourinary (GU) (n=9) were included in comparison to GBM (n=31). The mean sRCBV of all metastases (1.83+/-1.05) were significantly lower (p=0.0009) than mean sRCBV for GBM (2.67±1.34) with both statistically greater (p<0.0001) than NAWM (0.68 +/- 0.18). Histologically distinct metastases are each statistically greater than NAWM (p<0.0001) with lung (p=0.0002) and GU (p=.02) sRCBV being significantly different than GBM sRCBV. 49 patients with a primary histology of lung (n=25), breast (n=6), squamous cell carcinoma (SCC) (n=1), melanoma (n=5), gastrointestinal (GI) (n=3) and genitourinary (GU) (n=9) were included in comparison to GBM (n=31). The mean sRCBV of all metastases (1.83+/-1.05) were significantly lower (p=0.0009) than mean sRCBV for GBM (2.67+1.34) with both statistically greater (p<0.0001) than NAWM (0.68 +/- 0.18). Histologically distinct metastases are each statistically greater than NAWM (p<0.0001) with lung (p=0.0002) and GU (p=.02) sRCBV being significantly different than GBM sRCBV. dT1=delta T1; GBCA=gadolinium-based contrast agent; NAWM=normal appearing white matter; normalized relative cerebral blood volume=nRCBV; relative cerebral blood volume=rCBV; standardized relative cerebral blood volume=sRCBV.

Utility of diffusion-weighted imaging in differentiating benign and malignant breast lesions.

Breast cancer presents a significant global health burden. An accurate differentiation between benign and malignant lesions is imperative for timely intervention. While dynamic contrast enhanced MRI (DCE-MRI) is highly sensitive, its specificity is limited. This has led to the exploration of diffusion-weighted imaging (DWI) in distinguishing between benign and malignant breast lesions. The study aimed to explore the diagnostic utility of DWI in distinguishing between benign and malignant breast lesions. Assessment of 38 breast lesions using DWI with a b value of 800 s/mm2, performed with 3 Tesla MRI. The diagnostic performance of two different region of Interest (ROI) placement approaches was compared to obtain a feasible cut-off value of apparent diffusion coefficient (ADC) to differentiate between malignant and benign lesions. The histopathological reports were used as the gold standard. ADC values of malignant lesions were significantly lower than those of benign lesions (0.84 × 10-3 mm2/s vs. 1.54 × 10-3 mm2/s). The average ADC measured using a small-sized 2D ROI including the darkest part in the ADC map, performed better than the large 2D ROI covering the entire lesion. Using a cut-off value of 0.98 × 10-3 mm2/s, ADC obtained high sensitivity (90%) and specificity (88.9%) in distinguishing between benign and malignant breast lesions. Utilising quantitative analysis of DWI with ADC value measurement, reliably distinguished between benign and malignant breast lesions in this cohort, especially when employing a higher b value of 800 s/mm2.

Micro-enema immediately prior to prostate MRI: effects on rectal gas, image quality and PI-QUAL score.

Our aim was to determine whether the administration of a micro-enema immediately prior to prostate MRI is associated with a reduction in rectal gas, gas related artifacts and an improvement in image quality and PI-QUAL score. This retrospective analysis enrolled 171 patients who underwent multiparametric 3T prostate MRI at our institution between January 2021 and September 2022. 86 patients received a micro-enema, and a further 85 patients did not. Two fellowship trained abdominal radiologists were blinded and independently reviewed each prostate MRI, recording scores on a dedicated scoring sheet. The quality of T2 weighted (T2W), diffusion weighted (DWI), and dynamic contrast enhancement (DCE) images were assessed according to standardised scales supported in the literature. In addition, gas related artifacts and rectal gas level were examined. An independent-samples Mann-Whitney U and t-test were performed, comparing both the median and mean score between micro-enema and no micro-enema groups for each reader. Spearman's correlation was used to determine the strength of relationship between variables. A quadratic weighted Cohen's Kappa and percent agreement were used to assess inter-observer agreement. Image quality was improved in those who received the micro-enema compared to those who did not according to the visual grading scale on the DWI sequence for both readers (reader 1: median 4 vs. 4, p < 0.001, mean 4.27 vs. 3.92, p < 0.001; reader 2: median 5 vs. 4, p < 0.001, mean 4.74 vs. 4.14, p < 0.001). PI-QUAL score was significantly improved in the micro-enema group for reader 2 only (reader 1: median 4 vs. 4, p = 0.25, mean 3.99 vs. 4.08, p = 0.21; reader 2: median 5 vs. 5, p = 0.01, mean 4.95 vs. 4.78, p = 0.01). Visual grading score for both the T2W and DCE images was not significantly different. Rectal gas level was lower in patients who received the micro-enema for both readers (reader 1: median 2 vs. 4, p < 0.001, mean 2.12 vs. 3.60, p < 0.001; reader 2: median 1 vs. 2, p < 0.001, mean 1.37 vs. 2.48, p < 0.001), correlating with a lower score for gas-related artifacts (reader 1: median 1 vs. 2, p < 0.001, mean 1.50 vs. 1.92, p < 0.001; reader 2: median 1 vs. 1, p < 0.001, mean 1.16 vs. 1.71, p < 0.001) in this group. Correlation between rectal gas level and gas-related artifacts on DWI regardless of whether a micro-enema was given was strong (rs = 0.71, p < 0.001). Correlation was moderate to strong between rectal gas level and image quality on DWI (rs = -0.63, p < 0.001). There was only 1 (1.2%) borderline diagnostic or non-diagnostic DWI sequence in those who received the micro-enema, compared to 9 (10.6%) in those who did not (p = 0.009). Interobserver agreement was moderate for image quality on DWI, gas related artifacts and rectal gas level (weighted kappa values of 0.52, 0.49 and 0.53 respectively). The administration of a micro-enema immediately prior to prostate MRI is associated with a significant improvement in image quality on the DWI sequence compared to no bowel preparation. This is mediated through a reduction in rectal gas and gas related artifacts. Improvements in PI-QUAL score was mixed between readers.

Retraction: Comparing the predictive value of quantitative magnetic resonance imaging parametric response mapping and conventional perfusion magnetic resonance imaging for clinical outcomes in patients with chronic ischemic stroke.

[This retracts the article DOI: 10.3389/fnins.2023.1177044.].

Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.

The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk breast cancer. Previous studies suggest that pathological complete response (pCR) is a viable indicator of long-term outcomes of neoadjuvant chemotherapy for high-risk breast cancer. While pCR can be assessed during surgery after the chemotherapy, early prediction of pCR before the completion of the chemotherapy may facilitate personalized treatment management to achieve an improved outcome. Notably, the acquisition of dynamic contrast-enhanced magnetic resonance (DCEMR) images at multiple time points during the I-SPY 2 trial opens up the possibility of achieving early pCR prediction. In this study, we investigated the feasibility of the early prediction of pCR to neoadjuvant chemotherapy using multi-time point DCEMR images and clinical data acquired in the I-SPY2 trial. The prediction uncertainty was also quantified to allow physicians to make patient-specific decisions on treatment plans based on the level of associated uncertainty. The dataset used in our study included 624 patients with DCEMR images acquired at 3 time points before the completion of the chemotherapy: pretreatment (T0), after 3 cycles of treatment (T1), and after 12 cycles of treatment (T2). A convolutional long short-term memory (LSTM) network-based deep learning model, which integrated multi-time point deep image representations with clinical data, including tumor subtypes, was developed to predict pCR. The performance of the model was evaluated via the method of nested 5-fold cross validation. Moreover, we also quantified prediction uncertainty for each patient through test-time augmentation. To investigate the relationship between predictive performance and uncertainty, the area under the receiver operating characteristic curve (AUROC) was assessed on subgroups of patients stratified by the uncertainty score. By integrating clinical data and DCEMR images obtained at three-time points before treatment completion, the AUROC reached 0.833 with a sensitivity of 0.723 and specificity of 0.800. This performance was significantly superior (p<0.01) to models using only images (AUROC=0.706) or only clinical data (AUROC=0.746). After stratifying the patients into eight subgroups based on the uncertainty score, we found that group #1, with the lowest uncertainty, had a superior AUROC of 0.873. The AUROC decreased to 0.637 for group #8, which had the highest uncertainty. The results indicate that our convolutional LSTM network-based deep learning model can be used to predict pCR earlier before the completion of chemotherapy. By combining clinical data and multi-time point deep image representations, our model outperforms models built solely on clinical or image data. Estimating prediction uncertainty may enable physicians to prioritize or disregard predictions based on their associated uncertainties. This approach could potentially enhance the personalization of breast cancer therapy.

Dynamic contrast enhanced MRI demonstrate altered placental perfusion in the STOX1A preeclampsia mouse model

IntroductionPreeclampsia, a hypertensive disorder of pregnancy triggered by placental dysfunction, is reproduced in the murine STOX1A model, with hypertension, proteinuria, and abnormalities in umbilical and uterine Dopplers. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an innovative technique that provides insights into tissue perfusion. The present study aims at analyzing placental perfusion using DCE-MRI to further characterize placental defects in the STOX1A model. MethodsTwo study groups were formed: the "TgSTOX13 pregnancy group" from mating TgSTOX13 genotype males with wild-type females, and the "wild-type pregnancy group" from mating wild-type males with wild-type females. Blood pressure, urinary albumin to creatinine ratio, and fetal weights were measured and compared between the groups, while perfusion parameters were analyzed using both conventional compartmental (1C) and free-time point-Hermite (FTPH) models in the DCE analysis. ResultsSeventeen pregnant mice in the "TgSTOX13 pregnancy group" and thirteen in the "wild-type pregnant group" were included in the analysis. During late gestation, the TgSTOX13 pregnancy group exhibited higher blood pressure, elevated albumin/creatinine ratio, and decreased fetal weights compared to the wild-type pregnancy group. In the DCE analysis utilizing the 1C model, blood flow (Fb) was significantly reduced by approximately 31.8 % in the TgSTOX13 pregnancy group compared to the wild-type pregnancy group (p < 0.01), a finding corroborated by the FTPH model with a reduction estimated at 31.5 % (p < 0.01). DiscussionOur investigation successfully utilized DCE MRI to assess placental perfusion in a mouse model of preeclampsia, revealing a significant reduction of approximately 30 % in the preeclamptic mice, mirroring human pathophysiology.

Leveraging Ensemble Models and Follow-up Data for Accurate Prediction of mRS Scores from Radiomic Features of DSC-PWI Images.

Predicting long-term clinical outcomes based on the early DSC PWI MRI scan is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about which imaging features to assess and may require time-consuming postprocessing. This study's goal was to predict multilabel 90-day modified Rankin Scale (mRS) score in acute ischemic stroke patients by combining ensemble models and different configurations of radiomic features generated from Dynamic susceptibility contrast perfusion-weighted imaging. In Follow-up studies, a total of 70 acute ischemic stroke (AIS) patients underwent magnetic resonance imaging within 24hours poststroke and had a follow-up scan. In the single study, 150 DSC PWI Image scans for AIS patients. The DRF are extracted from DSC-PWI Scans. Then Lasso algorithm is applied for feature selection, then new features are generated from initial and follow-up scans. Then we applied different ensemble models to classify between three classes normal outcome (0, 1 mRS score), moderate outcome (2,3,4 mRS score), and severe outcome (5,6 mRS score). ANOVA and post-hoc Tukey HSD tests confirmed significant differences in model style performance across various studies and classification techniques. Stacking models consistently on average outperformed others, achieving an Accuracy of 0.68 ± 0.15, Precision of 0.68 ± 0.17, Recall of 0.65 ± 0.14, and F1 score of 0.63 ± 0.15 in the follow-up time study. Techniques like Bo_Smote showed significantly higher recall and F1 scores, highlighting their robustness and effectiveness in handling imbalanced data. Ensemble models, particularly Bagging and Stacking, demonstrated superior performance, achieving nearly 0.93 in Accuracy, 0.95 in Precision, 0.94 in Recall, and 0.94 in F1 metrics in follow-up conditions, significantly outperforming single models. Ensemble models based on radiomics generated from combining Initial and follow-up scans can be used to predict multilabel 90-day stroke outcomes with reduced subjectivity and user burden.