Standardized evaluation of the extent of resection in glioblastoma with automated early post-operative segmentation.

Standard treatment of patients with glioblastoma includes surgical resection of the tumor. The extent of resection (EOR) achieved during surgery significantly impacts prognosis and is used to stratify patients in clinical trials. In this study, we developed a U-Net-based deep-learning model to segment contrast-enhancing tumor on post-operative MRI exams taken within 72 h of resection surgery and used these segmentations to classify the EOR as either maximal or submaximal. The model was trained on 122 multiparametric MRI scans from our institution and achieved a mean Dice score of 0.52 ± 0.03 on an external dataset (n = 248), a performance -on par with the interrater agreement between expert annotators as reported in literature. We obtained an EOR classification precision/recall of 0.72/0.78 on the internal test dataset (n = 462) and 0.90/0.87 on the external dataset. Furthermore, Kaplan-Meier curves were used to compare the overall survival between patients with maximal and submaximal resection in the internal test dataset, as determined by either clinicians or the model. There was no significant difference between the survival predictions using the model's and clinical EOR classification. We find that the proposed segmentation model is capable of reliably classifying the EOR of glioblastoma tumors on early post-operative MRI scans. Moreover, we show that stratification of patients based on the model's predictions offers at least the same prognostic value as when done by clinicians.

Norwegian radiologists' expectations of artificial intelligence in mammographic screening - A cross-sectional survey.

PURPOSE: To explore Norwegian breast radiologists' expectations of adding artificial intelligence (AI) in the interpretation procedure of screening mammograms. METHODS: All breast radiologists involved in interpretation of screening mammograms in BreastScreen Norway during 2021 and 2022 (n = 98) were invited to take part in this anonymous cross-sectional survey about use of AI in mammographic screening. The questionnaire included background information of the respondents, their expectations, considerations of biases, and ethical and social implications of implementing AI in screen reading. Data was collected digitally and analyzed using descriptive statistics. RESULTS: The response rate was 61% (60/98), and 67% (40/60) of the respondents were women. Sixty percent (36/60) reported ≥10 years' experience in screen reading, while 82% (49/60) reported no or limited experience with AI in health care. Eighty-two percent of the respondents were positive to explore AI in the interpretation procedure in mammographic screening. When used as decision support, 68% (41/60) expected AI to increase the radiologists' sensitivity for cancer detection. As potential challenges, 55% (33/60) reported lack of trust in the AI system and 45% (27/60) reported discrepancy between radiologists and AI systems as possible challenges. The risk of automation bias was considered high among 47% (28/60). Reduced time spent reading mammograms was rated as a potential benefit by 70% (42/60). CONCLUSION: The radiologists reported positive expectations of AI in the interpretation procedure of screening mammograms. Efforts to minimize the risk of automation bias and increase trust in the AI systems are important before and during future implementation of the tool.

Imaging the tumour microenvironment in rectal cancer: Decline in tumour blood flow during radiotherapy predicts good outcome.

Background and purpose: Measuring rectal tumour response to radiation is pivotal to restaging patients and for possibly stratification to a watch-and-wait strategy. Recognizing the importance of the tumour microenvironment, we investigated a less explored quantitative imaging marker assessing tumour blood flow (BF) for its potential to predict overall survival (OS). Materials and methods: 24 rectal cancer patients given curative-intent neoadjuvant radiotherapy underwent a multi-echo dynamic magnetic resonance imaging (MRI) sequence with gadolinium contrast for quantification of tumour BF before either 25x2 Gy (n = 18) with concomitant chemotherapy or 5x5 Gy (n = 6). CD34 staining of excised tumour tissue was performed and baseline blood samples were analysed for lactate dehydrogenase (LDH) and angiopoietin-2 (ANGPT-2). Tumour volumes were measured before and after treatment. After subsequent surgery, ypTN scoring assessed tumour response. Cox regression for 5-year OS analysis and t-test for group comparisons were performed. Results: The change in tumour BF (ΔBF) during neoadjuvant radiotherapy was a significant marker of OS, whereas tumour stage and volume were not related to OS. All patients with >20 % decline in BF were long-term survivors. Separating cases in two groups based on ΔBF revealed that patients with increase or a low decrease had higher baseline LDH (p = 0.032) and ANGPT-2 (p = 0.028) levels. Conclusion: MRI-assessed tumour ΔBF during neoadjuvant treatment is a significant predictor of OS in rectal cancer patients, making ΔBF a potential quantitative imaging biomarker for treatment stratification. Blood LDH and ANGPT-2 indicate that non-responding tumours may have a hypoxic microenvironment resistant to radiotherapy.

Quantification of Tissue Compression Identifies High-Grade Glioma Patients with Reduced Survival.

The compression of peritumoral healthy tissue in brain tumor patients is considered a major cause of the life-threatening neurologic symptoms. Although significant deformations caused by the tumor growth can be observed radiologically, the quantification of minor tissue deformations have not been widely investigated. In this study, we propose a method to quantify subtle peritumoral deformations. A total of 127 MRI longitudinal studies from 23 patients with high-grade glioma were included. We estimate longitudinal displacement fields based on a symmetric normalization algorithm and we propose four biomarkers. We assess the interpatient and intrapatient association between proposed biomarkers and the survival based on Cox analyses, and the potential of the biomarkers to stratify patients according to their survival based on Kaplan−Meier analysis. Biomarkers show a significant intrapatient association with survival (p < 0.05); however, only compression biomarkers show the ability to stratify patients between those with higher and lower overall survival (AUC = 0.83, HR = 6.30, p < 0.05 for CompCH). The compression biomarkers present three times higher Hazard Ratios than those representing only displacement. Our study provides a robust and automated method for quantifying and delineating compression in the peritumoral area. Based on the proposed methodology, we found an association between lower compression in the peritumoral area and good prognosis in high-grade glial tumors.

Automated segmentation of magnetic resonance bone marrow signal: a feasibility study.

BACKGROUND: Manual assessment of bone marrow signal is time-consuming and requires meticulous standardisation to secure adequate precision of findings. OBJECTIVE: We examined the feasibility of using deep learning for automated segmentation of bone marrow signal in children and adolescents. MATERIALS AND METHODS: We selected knee images from 95 whole-body MRI examinations of healthy individuals and of children with chronic non-bacterial osteomyelitis, ages 6-18 years, in a longitudinal prospective multi-centre study cohort. Bone marrow signal on T2-weighted Dixon water-only images was divided into three color-coded intensity-levels: 1 = slightly increased; 2 = mildly increased; 3 = moderately to highly increased, up to fluid-like signal. We trained a convolutional neural network on 85 examinations to perform bone marrow segmentation. Four readers manually segmented a test set of 10 examinations and calculated ground truth using simultaneous truth and performance level estimation (STAPLE). We evaluated model and rater performance through Dice similarity coefficient and in consensus. RESULTS: Consensus score of model performance showed acceptable results for all but one examination. Model performance and reader agreement had highest scores for level-1 signal (median Dice 0.68) and lowest scores for level-3 signal (median Dice 0.40), particularly in examinations where this signal was sparse. CONCLUSION: It is feasible to develop a deep-learning-based model for automated segmentation of bone marrow signal in children and adolescents. Our model performed poorest for the highest signal intensity in examinations where this signal was sparse. Further improvement requires training on larger and more balanced datasets and validation against ground truth, which should be established by radiologists from several institutions in consensus.

Operator dependency of arterial input function in dynamic contrast-enhanced MRI.

OBJECTIVE: To investigate the effect of inter-operator variability in arterial input function (AIF) definition on kinetic parameter estimates (KPEs) from dynamic contrast-enhanced (DCE) MRI in patients with high-grade gliomas. METHODS: The study included 118 DCE series from 23 patients. AIFs were measured by three domain experts (DEs), and a population AIF (pop-AIF) was constructed from the measured AIFs. The DE-AIFs, pop-AIF and AUC-normalized DE-AIFs were used for pharmacokinetic analysis with the extended Tofts model. AIF-dependence of KPEs was assessed by intraclass correlation coefficient (ICC) analysis, and the impact on relative longitudinal change in Ktrans was assessed by Fleiss' kappa (κ). RESULTS: There was a moderate to substantial agreement (ICC 0.51-0.76) between KPEs when using DE-AIFs, while AUC-normalized AIFs yielded ICC 0.77-0.95 for Ktrans, kep and ve and ICC 0.70 for vp. Inclusion of the pop-AIF did not reduce agreement. Agreement in relative longitudinal change in Ktrans was moderate (κ = 0.591) using DE-AIFs, while AUC-normalized AIFs gave substantial (κ = 0.809) agreement. DISCUSSION: AUC-normalized AIFs can reduce the variation in kinetic parameter results originating from operator input. The pop-AIF presented in this work may be applied in absence of a satisfactory measurement.

2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data.

Introduction: Management of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segmentation network for a varying number of available MRI available sequences. Methods: We adapt and evaluate a 2.5D and a 3D convolution neural network trained and tested on a retrospective multinational study from two independent centers, in addition, nnU-Net was adapted as a comparative benchmark. Segmentation and detection performance was evaluated by: (1) the dice similarity coefficient, (2) a per-metastases and the average detection sensitivity, and (3) the number of false positives. Results: The 2.5D and 3D models achieved similar results, albeit the 2.5D model had better detection rate, whereas the 3D model had fewer false positive predictions, and nnU-Net had fewest false positives, but with the lowest detection rate. On MRI data from center 1, the 2.5D, 3D, and nnU-Net detected 79%, 71%, and 65% of all metastases; had an average per patient sensitivity of 0.88, 0.84, and 0.76; and had on average 6.2, 3.2, and 1.7 false positive predictions per patient, respectively. For center 2, the 2.5D, 3D, and nnU-Net detected 88%, 86%, and 78% of all metastases; had an average per patient sensitivity of 0.92, 0.91, and 0.85; and had on average 1.0, 0.4, and 0.1 false positive predictions per patient, respectively. Discussion/Conclusion: Our results show that deep learning can yield highly accurate segmentations of brain metastases with few false positives in multinational data, but the accuracy degrades for metastases with an area smaller than 0.4 cm2.

Noise dependency in vascular parameters from combined gradient-echo and spin-echo DSC MRI.

Dynamic susceptibility contrast (DSC) imaging is a widely used technique for assessment of cerebral blood volume (CBV). With combined gradient-echo and spin-echo DSC techniques, measures of the underlying vessel size and vessel architecture can be obtained from the vessel size index (VSI) and vortex area, respectively. However, how noise, and specifically the contrast-to-noise ratio (CNR), affect the estimations of these parameters has largely been overlooked. In order to address this issue, we have performed simulations to generate DSC signals with varying levels of CNR, defined by the peak of relaxation rate curve divided by the standard deviation of the baseline. Moreover, DSC data from 59 brain cancer patients were acquired at two different 3 T-scanners (N = 29 and N = 30, respectively), where CNR and relative parameter maps were obtained. Our simulations showed that the measured parameters were affected by CNR in different ways, where low CNR led to overestimations of CBV and underestimations of VSI and vortex area. In addition, a higher noise-sensitivity was found in vortex area than in CBV and VSI. Results from clinical data were consistent with simulations, and indicated that CNR < 4 gives highly unreliable measurements. Moreover, we have shown that the distribution of values in the tumour regions could change considerably when voxels with CNR below a given cut off are excluded when generating the relative parameter maps. The widespread use of CBV and attractive potential of VSI and vortex area, makes the noise-sensitivity of these parameters found in our study relevant for further use and development of the DSC imaging technique. Our results suggest that the CNR has considerable impact on the measured parameters, with the potential to affect the clinical interpretation of DSC-MRI, and should therefore be taken into account in the clinical decision-making process.

Sex Differences and Tumor Blood Flow from Dynamic Susceptibility Contrast MRI Are Associated with Treatment Response after Chemoradiation and Long-term Survival in Rectal Cancer.

Background MRI is the standard tool for rectal cancer staging. However, more precise diagnostic tests that can assess biologic tumor features decisive for treatment outcome are necessary. Tumor perfusion and hypoxia are two important features; however, no reference methods that measure these exist in clinical use. Purpose To assess the potential predictive and prognostic value of MRI-assessed rectal cancer perfusion, as a surrogate measure of hypoxia, for local treatment response and survival. Materials and Methods In this prospective observational cohort study, 94 study participants were enrolled from October 2013 to December 2017 (ClinicalTrials.gov: NCT01816607). Participants had histologically confirmed rectal cancer and underwent routine diagnostic MRI, an extended diffusion-weighted sequence, and a multiecho dynamic contrast agent-based sequence. Predictive and prognostic values of dynamic contrast-enhanced, dynamic susceptibility contrast (DSC), and intravoxel incoherent motion MRI were investigated with response to neoadjuvant treatment, progression-free survival, and overall survival as end points. Secondary objectives investigated potential sex differences in MRI parameters and relationship with lymph node stage. Statistical methods used were Cox regression, Student t test, and Mann-Whitney U test. Results A total of 94 study participants (mean age, 64 years ± 11 [standard deviation]; 61 men) were evaluated. Baseline tumor blood flow from DSC MRI was lower in patients who had poor local tumor response to neoadjuvant treatment (96 mL/min/100 g ± 33 for ypT2-4, 120 mL/min/100 g ± 21 for ypT0-1; P = .01), shorter progression-free survival (hazard ratio = 0.97; 95% confidence interval: 0.96, 0.98; P < .001), and shorter overall survival (hazard ratio = 0.98; 95% confidence interval: 0.98, 0.99; P < .001). Women had higher blood flow (125 mL/min/100 g ± 27) than men (74 mL/min/100 g ± 26, P < .001) at stage 4. Volume transfer constant and plasma volume from dynamic contrast-enhanced MRI as well as ΔR2* peak and area under the curve for 30 and 60 seconds from DSC MRI were associated with local malignant lymph nodes (pN status). Median area under the curve for 30 seconds was 0.09 arbitrary units (au) ± 0.03 for pN1-2 and 0.19 au ± 0.12 for pN0 (P = .001). Conclusion Low tumor blood flow from dynamic susceptibility contrast MRI was associated with poor treatment response in study participants with rectal cancer. © RSNA, 2020 Online supplemental material is available for this article.

A Fully Automated Deep Learning Network for Brain Tumor Segmentation.

We developed a fully automated method for brain tumor segmentation using deep learning; 285 brain tumor cases with multiparametric magnetic resonance images from the BraTS2018 data set were used. We designed 3 separate 3D-Dense-UNets to simplify the complex multiclass segmentation problem into individual binary-segmentation problems for each subcomponent. We implemented a 3-fold cross-validation to generalize the network's performance. The mean cross-validation Dice-scores for whole tumor (WT), tumor core (TC), and enhancing tumor (ET) segmentations were 0.92, 0.84, and 0.80, respectively. We then retrained the individual binary-segmentation networks using 265 of the 285 cases, with 20 cases held-out for testing. We also tested the network on 46 cases from the BraTS2017 validation data set, 66 cases from the BraTS2018 validation data set, and 52 cases from an independent clinical data set. The average Dice-scores for WT, TC, and ET were 0.90, 0.84, and 0.80, respectively, on the 20 held-out testing cases. The average Dice-scores for WT, TC, and ET on the BraTS2017 validation data set, the BraTS2018 validation data set, and the clinical data set were as follows: 0.90, 0.80, and 0.78; 0.90, 0.82, and 0.80; and 0.85, 0.80, and 0.77, respectively. A fully automated deep learning method was developed to segment brain tumors into their subcomponents, which achieved high prediction accuracy on the BraTS data set and on the independent clinical data set. This method is promising for implementation into a clinical workflow.

Effect of Drinking Oxygenated Water Assessed by in vivo MRI Relaxometry.

GRANT SUPPORT: This project was funded by the Research Council of Norway. BACKGROUND: Oxygen uptake through the gastrointestinal tract after oral administration of oxygenated water in humans is not well studied and is debated in the literature. Due to the paramagnetic properties of oxygen and deoxyhemoglobin, MRI as a technique might be able to detect changes in relaxometry values caused by increased oxygen levels in the blood. PURPOSE: To assess whether oxygen dissolved in water is absorbed from the gastrointestinal tract and transported into the bloodstream after oral administration. STUDY TYPE: A randomized, double-blinded, placebo-controlled crossover trial. POPULATION/SUBJECTS: Thirty healthy male volunteers age 20-35. FIELD STRENGTH/SEQUENCE: 3T/Modified Look-Locker inversion recovery (MOLLI) T1 -mapping and multi fast field echo (mFFE) T2 *-mapping. ASSESSMENT: Each volunteer was scanned in two separate sessions. T1 and T2 * maps were acquired repeatedly covering the hepatic portal vein (HPV) and vena cava inferior (VCI, control vein) before and after intake of oxygenated or control water. Assessments were done by placing a region of interest in the HPV and VCI. STATISTICAL TEST: A mixed linear model was performed to the compare control vs. oxygen group. RESULTS: Drinking caused a mean 1.6% 95% CI (1.1-2.0% P < 0.001) increase in T1 of HPV blood and water oxygenation attributed another 0.70% 95% confidence interval (CI) (0.07-1.3% P = 0.028) increase. Oxygenation did not change T1 in VCI blood. Mean T2 * increased 9.6% 95% CI (1.7-17.5% P = 0.017) after ingestion of oxygenated water and 1.2% 95% CI (-4.3-6.8% P = 0.661) after ingestion of control water. The corresponding changes in VCI blood were not significant. DATA CONCLUSION: Ingestion of water caused changes in T1 and T2 * of HPV blood compatible with dilution due to water absorption. The effects were enhanced by oxygen. Assessment of oxygen enrichment of HPV blood was not possible due to the dilution effect. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:720-728.

Prediction of survival and progression in glioblastoma patients using temporal perfusion changes during radiochemotherapy.

BACKGROUND: The aim of this study was to investigate changes in structural magnetic resonance imaging (MRI) according to the RANO criteria and perfusion- and permeability related metrics derived from dynamic contrast-enhanced MRI (DCE) and dynamic susceptibility contrast MRI (DSC) during radiochemotherapy for prediction of progression and survival in glioblastoma. METHODS: Twenty-three glioblastoma patients underwent biweekly structural and perfusion MRI before, during, and two weeks after a six weeks course of radiochemotherapy. Temporal trends of tumor volume and the perfusion-derived parameters cerebral blood volume (CBV) and blood flow (CBF) from DSC and DCE, in addition to contrast agent capillary transfer constant (Ktrans) from DCE, were assessed. The patients were separated in two groups by median survival and differences between the two groups explored. Clinical- and MRI metrics were investigated using univariate and multivariate survival analysis and a predictive survival index was generated. RESULTS: Median survival was 19.2 months. A significant decrease in contrast-enhancing tumor size and CBV and CBF in both DCE- and DSC-derived parameters was seen during and two weeks past radiochemotherapy (p < 0.05). A 10%/30% increase in Ktrans/CBF two weeks after finishing radiochemotherapy resulted in significant shorter survival (13.9/16.8 vs. 31.5/33.1 months; p < 0.05). Multivariate analysis revealed an index using change in Ktrans and relative CBV from DSC significantly corresponding with survival time in months (r2 = 0.843; p < 0.001). CONCLUSIONS: Significant temporal changes are evident during radiochemotherapy in tumor size (after two weeks) and perfusion-weighted MRI-derived parameters (after four weeks) in glioblastoma patients. While DCE-based metrics showed most promise for early survival prediction, a multiparametric combination of both DCE- and DSC-derived metrics gave additional information.

The Impact of MRI Features and Observer Confidence on the Treatment Decision-Making for Patients with Untreated Glioma.

In a blind, dual-center, multi-observer setting, we here identify the pre-treatment radiologic features by Magnetic Resonance Imaging (MRI) associated with subsequent treatment options in patients with glioma. Study included 220 previously untreated adult patients from two institutions (94 + 126 patients) with a histopathologically confirmed diagnosis of glioma after surgery. Using a blind, cross-institutional and randomized setup, four expert neuroradiologists recorded radiologic features, suggested glioma grade and corresponding confidence. The radiologic features were scored using the Visually AcceSAble Rembrandt Images (VASARI) standard. Results were retrospectively compared to patient treatment outcomes. Our findings show that patients receiving a biopsy or a subtotal resection were more likely to have a tumor with pathological MRI-signal (by T2-weighted Fluid-Attenuated Inversion Recovery) crossing the midline (Hazard Ratio; HR = 1.30 [1.21-1.87], P < 0.001), and those receiving a biopsy sampling more often had multifocal lesions (HR = 1.30 [1.16-1.64], P < 0.001). For low-grade gliomas (N = 50), low observer confidence in the radiographic readings was associated with less chance of a total resection (P = 0.002) and correlated with the use of a more comprehensive adjuvant treatment protocol (Spearman = 0.48, P < 0.001). This study may serve as a guide to the treating physician by identifying the key radiologic determinants most likely to influence the treatment decision-making process.

Comparison of Intravoxel incoherent motion imaging and multiecho dynamic contrast-based MRI in rectal cancer.

BACKGROUND: Dynamic contrast-based MRI and intravoxel incoherent motion imaging (IVIM) MRI are both methods showing promise as diagnostic and prognostic tools in rectal cancer. Both methods aim at measuring perfusion-related parameters, but the relationship between them is unclear. PURPOSE: To investigate the relationship between perfusion- and permeability-related parameters obtained by IVIM-MRI, T1 -weighted dynamic contrast-enhanced (DCE)-MRI and T2 *-weighted dynamic susceptibility contrast (DSC)-MRI. STUDY TYPE: Prospective. SUBJECTS: In all, 94 patients with histologically confirmed rectal cancer. FIELD STRENGTH/SEQUENCE: Subjects underwent pretreatment 1.5T clinical procedure MRI, and in addition a study-specific diffusion-weighted sequence (b = 0, 25, 50, 100, 500, 1000, 1300 s/mm2 ) and a multiecho dynamic contrast-based echo-planer imaging sequence. ASSESSMENT: Median tumor values were obtained from IVIM (perfusion fraction [f], pseudodiffusion [D*], diffusion [D]), from the extended Tofts model applied to DCE data (Ktrans , kep , vp , ve ) and from model free deconvolution of DSC (blood flow [BF] and area under curve). A subgroup of the excised tumors underwent immunohistochemistry with quantification of microvessel density and vessel size. STATISTICAL TEST: Spearman's rank correlation test. RESULTS: D* was correlated with BF (rs = 0.47, P < 0.001), and f was negatively correlated with kep (rs = -0.31, P = 0.002). BF was correlated with Ktrans (rs = 0.29, P = 0.004), but this correlation varied extensively when separating tumors into groups of low (rs = 0.62, P < 0.001) and high (rs = -0.06, P = 0.68) BF. Ktrans was negatively correlated with vessel size (rs = -0.82, P = 0.004) in the subgroup of tumors with high BF. DATA CONCLUSION: We found an association between D* from IVIM and BF estimated from DSC-MRI. The relationship between IVIM and DCE-MRI was less clear. Comparing parameters from DSC-MRI and DCE-MRI highlights the importance of the underlying biology for the interpretation of these parameters. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1114-1124.

Permeability measurement using dynamic susceptibility contrast magnetic resonance imaging enhances differential diagnosis of primary central nervous system lymphoma from glioblastoma.

OBJECTIVES: To test if adding permeability measurement to perfusion obtained from dynamic susceptibility contrast MRI (DSC-MRI) improves diagnostic performance in the differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma. MATERIALS AND METHODS: DSC-MRI was acquired in 145 patients with pathologically proven glioblastoma (n = 89) or PCNSL (n = 56). The permeability metrics of contrast agent extraction fraction (Ex), apparent permeability (Ka), and leakage-corrected perfusion of normalized cerebral blood volume (nCBVres) and cerebral blood flow (nCBFres) were derived from a tissue residue function. For comparison purposes, the leakage-corrected normalized CBV (nCBV) and relative permeability constant (K2) were also obtained using the established Weisskoff-Boxerman leakage correction method. The area under the receiver operating characteristics curve (AUC) and cross-validation were used to compare the diagnostic performance of the single DSC-MRI parameters with the performance obtained with the addition of permeability metrics. RESULTS: PCNSL demonstrated significantly higher permeability (Ex, p < .001) and lower perfusion (nCBVres, nCBFres, and nCBV, all p < .001) than glioblastoma. The combination of Ex and nCBVres showed the highest performance (AUC, 0.96; 95% confidence interval, 0.92-0.99) for differentiating PCNSL from glioblastoma, which was a significant improvement over the single perfusion (nCBV: AUC, 0.84; nCBVres: AUC, 0.84; nCBFres: AUC, 0.82; all p < .001) or Ex (AUC, 0.80; p < .001) parameters. CONCLUSIONS: Analysis of the combined permeability and perfusion metrics obtained from a single DSC-MRI acquisition improves the diagnostic value for differentiating PCNSL from glioblastoma in comparison with single-parameter nCBV analysis. KEY POINTS: • Permeability measurement can be calculated from DSC-MRI with a tissue residue function-based leakage correction. • Adding Exto CBV aids in the differentiation of PCNSL from glioblastoma. • CBV and Exmeasurements from DSC-MRI were highly reproducible.

Preoperative quantitative dynamic contrast-enhanced MRI and diffusion-weighted imaging predict aggressive disease in endometrial cancer.

Background Quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) may yield preoperative tumor biomarkers relevant for prognosis and therapy in cancer. Purpose To explore the value of preoperative DCE-MRI and DWI for the prediction of aggressive disease in endometrial cancer patients. Material and Methods Preoperative MRI (1.5-T) from 177 patients were analyzed and imaging parameters reflecting tumor microvasculature (from DCE-MRI) and tumor microstructure (from DWI) were estimated. The derived imaging parameters were explored in relation to clinico-pathological stage, histological subtype and grade, molecular markers, and patient outcome. Results Low tumor blood flow (Fb) and low rate constant for contrast agent intravasation (kep) were associated with high-risk histological subtype ( P ≤ 0.04 for both) and tended to be associated with poor prognosis ( P ≤ 0.09). Low tumor apparent diffusion coefficient (ADC) value and large tumor volume were both significantly associated with deep myometrial invasion ( P < 0.001 for both) and were also unfavorable prognostic factors ( P = 0.05 and P < 0.001, respectively). Conclusion DCE-MRI and DWI represent valuable supplements to conventional MRI by providing preoperative imaging biomarkers that predict aggressive disease in endometrial cancer patients.

Signal Enhancement of the Dentate Nucleus at Unenhanced MR Imaging after Very High Cumulative Doses of the Macrocyclic Gadolinium-based Contrast Agent Gadobutrol: An Observational Study.

Purpose To test for measurable visual enhancement of the dentate nucleus (DN) on unenhanced T1-weighted magnetic resonance (MR) images in a cohort of patients with a primary brain tumor who had not received linear gadolinium-based contrast agents (GBCAs) but had received many injections of macrocyclic GBCAs. Materials and Methods Seventeen patients with high-grade gliomas who had received 10-44 administrations of the macrocyclic GBCA gadobutrol (0.1 mmol/kg of body weight) were retrospectively included in this regional ethics committee-approved study. Two neuroradiologists inspected T1-weighted MR images with optimized window settings to visualize small differences in contrast at the baseline and at the last examination for the presence of visual DN signal enhancement. Signal intensity (SI) in the DN was normalized to the SI of the pons, and a one-sample t test was used to test for differences between baseline normalized SI (nSI) in the DN (nSIDN) and the average change in nSIDN of all postbaseline MR imaging sessions (ΔnSIDNavg) or the change in nSIDN from baseline to the last MR imaging session (ΔnSIDN). Linear and quadratic correlation analyses were used to examine the association between the number of macrocyclic GBCA administrations and ΔnSIDN or ΔnSIDNavg. Results The mean ± standard deviation number of macrocyclic GBCA administrations was 22.2 ± 10.6 administered throughout 706 days ± 454. Visually appreciable signal enhancement was observed in two patients who had received 37 and 44 macrocyclic GBCA injections. Mean ΔnSIDN was greater than zero (0.03 ± 0.05; P = .016), and there was a significant linear association between the number of macrocyclic GBCA injections and ΔnSIDN (r = 0.69, P = .002) and ΔnSIDNavg (r = 0.77, P < .001). Conclusion A small but statistically significant dose-dependent T1-weighted signal enhancement was observed in the DN after multiple macrocyclic GBCA injections. Visually appreciable enhancement in the DN was observed on contrast-optimized images in two patients who had received 37 and 44 standard doses of macrocyclic GBCAs. © RSNA, 2017 Online supplemental material is available for this article.

A theoretical framework for determining cerebral vascular function and heterogeneity from dynamic susceptibility contrast MRI.

Mapping the complex heterogeneity of vascular tissue in the brain is important for understanding cerebrovascular disease. In this translational study, we build on previous work using vessel architectural imaging (VAI) and present a theoretical framework for determining cerebral vascular function and heterogeneity from dynamic susceptibility contrast magnetic resonance imaging (MRI). Our tissue model covers realistic structural architectures for vessel branching and orientations, as well as a range of hemodynamic scenarios for blood flow, capillary transit times and oxygenation. In a typical image voxel, our findings show that the apparent MRI relaxation rates are independent of the mean vessel orientation and that the vortex area, a VAI-based parameter, is determined by the relative oxygen saturation level and the vessel branching of the tissue. Finally, in both simulated and patient data, we show that the relative distributions of the vortex area parameter as a function of capillary transit times show unique characteristics in normal-appearing white and gray matter tissue, whereas tumour-voxels in comparison display a heterogeneous distribution. Collectively, our study presents a comprehensive framework that may serve as a roadmap for in vivo and per-voxel determination of vascular status and heterogeneity in cerebral tissue.

Dynamic multi-echo DCE- and DSC-MRI in rectal cancer: Low primary tumor Ktrans and ΔR2* peak are significantly associated with lymph node metastasis.

PURPOSE: To implement a dynamic contrast-based multi-echo MRI sequence in assessment of rectal cancer and evaluate associations between histopathologic data and the acquired dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) -MRI parameters. MATERIALS AND METHODS: This pilot study reports results from 17 patients with resectable rectal cancer. Dynamic contrast-based multi-echo MRI (1.5T) was acquired using a three-dimensional multi-shot EPI sequence, yielding both DCE- and DSC-data following a single injection of contrast agent. The Institutional Review Board approved the study and all patients provided written informed consent. Quantitative analysis was performed by pharmacokinetic modeling on DCE data and tracer kinetic modeling on DSC data. Mann-Whitney U-test and receiver operating characteristics curve statistics was used to evaluate associations between histopathologic data and the acquired DCE- and DSC-MRI parameters. RESULTS: For patients with histologically confirmed nodal metastasis, the primary tumor demonstrated a significantly lower Ktrans and peak change in R2*, R2*-peakenh , than patients without nodal metastasis, showing a P-value of 0.010 and 0.005 for reader 1, and 0.043 and 0.019 for reader 2, respectively. CONCLUSION: This study shows the feasibility of acquiring DCE- and DSC-MRI in rectal cancer by dynamic multi-echo MRI. A significant association was found between both Ktrans and R2*-peakenh in the primary tumor and histological nodal status of the surgical specimen, which may improve stratification of patients to intensified multimodal treatment. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:194-206.