Dynamic Contrast-enhanced Perfusion MRI Research Articles

Conditional diffusion-generated super-resolution for myocardial perfusion MRI

IntroductionMyocardial perfusion MRI is important for diagnosing coronary artery disease, but current clinical methods face challenges in balancing spatial resolution, temporal resolution, and slice coverage. Achieving broader slice coverage and higher temporal resolution is essential for accurately detecting abnormalities across different slice locations but remains difficult due to constraints in acquisition speed and heart rate variability. While techniques like parallel imaging and compressed sensing have significantly advanced perfusion imaging, they still suffer from noise amplification, residual artifacts, and potential temporal blurring due to the rapid transit of dynamic contrast vs. the temporal constraints of the reconstruction.MethodsThis study introduces a conditional diffusion-based generative model for myocardial perfusion MRI super resolution, addressing the trade-offs between spatiotemporal resolution and slice coverage. We adapted Denoising Diffusion Probabilistic Models (DDPM) to enhance low-resolution perfusion images into high-resolution outputs without requiring temporal regularization. The forward diffusion process introduces Gaussian noise incrementally, while the reverse process employs a U-Net architecture to progressively denoise the images, conditioned on the low-resolution input image.ResultsWe trained and validated the model on a retrospective dataset of dynamic contrast-enhanced (DCE) perfusion MRI, consisting of both stress and rest images from 47 patients with heart disease. Our results showed significant image quality improvements, with a 5.1% reduction in nRMSE, a 1.1% increase in PSNR, and a 2.2% boost in SSIM compared to GAN-based super-resolution method (P < 0.05 for all metrics using paired t-test) in retrospective study. For the 9 prospective subjects, we achieved a total nominal acceleration of 8.5-fold across 5–6 slices through a combination of low-resolution acquisition and GRAPPA. PerfGen outperformed GAN-based approach in sharpness (4.36 ± 0.38 vs. 4.89 ± 0.22) and overall image quality (4.14 ± 0.28 vs. 4.89 ± 0.22), as assessed by two experts in a blinded evaluation (P < 0.05) in prospective study.DiscussionThis work demonstrates the capability of diffusion-based generative models in generating high-resolution myocardial perfusion MRI from conditional low-resolution images. This approach has shown the potentials to accelerate myocardial perfusion MRI while enhancing slice coverage and temporal resolution, offering a promising alternative to existing methods.

Longitudinal Evaluation of DCE-MRI as an Early Indicator of Progression after Standard Therapy in Glioblastoma.

Background and Purpose: Distinguishing treatment-induced imaging changes from progressive disease has important implications for avoiding inappropriate discontinuation of a treatment. Our goal in this study is to evaluate the utility of dynamic contrast-enhanced (DCE) perfusion MRI as a biomarker for the early detection of progression. We hypothesize that DCE-MRI may have the potential as an early predictor for the progression of disease in GBM patients when compared to the current standard of conventional MRI. Methods: We identified 26 patients from 2011 to 2023 with newly diagnosed primary glioblastoma by histopathology and gross or subtotal resection of the tumor. Then, we classified them into two groups: patients with progression of disease (POD) confirmed by pathology or change in chemotherapy and patients with stable disease without evidence of progression or need for therapy change. Finally, at least three DCE-MRI scans were performed prior to POD for the progression cohort, and three consecutive DCE-MRI scans were performed for those with stable disease. The volume of interest (VOI) was delineated by a neuroradiologist to measure the maximum values for Ktrans and plasma volume (Vp). A Friedman test was conducted to evaluate the statistical significance of the parameter changes between scans. Results: The mean interval between subsequent scans was 57.94 days, with POD-1 representing the first scan prior to POD and POD-3 representing the third scan. The normalized maximum Vp values for POD-3, POD-2, and POD-1 are 1.40, 1.86, and 3.24, respectively (FS = 18.00, p = 0.0001). It demonstrates that Vp max values are progressively increasing in the three scans prior to POD when measured by routine MRI scans. The normalized maximum Ktrans values for POD-1, POD-2, and POD-3 are 0.51, 0.09, and 0.51, respectively (FS = 1.13, p < 0.57). Conclusions: Our analysis of the longitudinal scans leading up to POD significantly correlated with increasing plasma volume (Vp). A longitudinal study for tumor perfusion change demonstrated that DCE perfusion could be utilized as an early predictor of tumor progression.

Contrast Agent Dynamics Determine Radiomics Profiles in Oncologic Imaging.

The reproducibility of radiomics features extracted from CT and MRI examinations depends on several physiological and technical factors. The aim was to evaluate the impact of contrast agent timing on the stability of radiomics features using dynamic contrast-enhanced perfusion CT (dceCT) or MRI (dceMRI) in prostate and lung cancers. Radiomics features were extracted from dceCT or dceMRI images in patients with biopsy-proven peripheral prostate cancer (pzPC) or biopsy-proven non-small cell lung cancer (NSCLC), respectively. Features that showed significant differences between contrast phases were identified using linear mixed models. An L2-penalized logistic regression classifier was used to predict class labels for pzPC and unaffected prostate regions-of-interest (ROIs). Nine pzPC and 28 NSCLC patients, who were imaged with dceCT and/or dceMRI, were included in this study. After normalizing for individual enhancement patterns by defining seven individual phases based on a reference vessel, 19, 467 and 128 out of 1204 CT features showed significant temporal dynamics in healthy prostate parenchyma, prostate tumors and lung tumors, respectively. CT radiomics-based classification accuracy of healthy and tumor ROIs was highly dependent on contrast agent phase. For dceMRI, 899 and 1027 out of 1118 features were significantly dependent on time after contrast agent injection for prostate and lung tumors. CT and MRI radiomics features in both prostate and lung tumors are significantly affected by interindividual differences in contrast agent dynamics.

PDZK1 is correlated with DCE-MRI perfusion parameters in high-grade glioma

ObjectiveThis study investigated the relationship between PDZK1 expression and Dynamic Contrast-Enhanced MRI (DCE-MRI) perfusion parameters in High-Grade Glioma (HGG). MethodsPreoperative DCE-MRI scanning was performed on 80 patients with HGG to obtain DCE perfusion transfer coefficient (Ktrans), vascular plasma volume fraction (vp), extracellular volume fraction (ve), and reverse transfer constant (kep). PDZK1 in HGG patients was detected, and its correlation with DCE-MRI perfusion parameters was assessed by the Pearson method. An analysis of Cox regression was performed to determine the risk factors affecting survival, while Kaplan-Meier and log-rank tests to evaluate PDZK1′s prognostic significance, and ROC curve analysis to assess its diagnostic value. ResultsPDZK1 was upregulated in HGG patients and predicted poor overall survival and progression-free survival. Moreover, PDZK1 expression distinguished grade III from grade IV HGG. PDZK1 expression was positively correlated with Ktrans 90, and ve_90, and negatively correlated with kep_max, and kep_90. ConclusionPDZK1 is upregulated in HGG, predicts poor survival, and differentiates tumor grading in HGG patients. PDZK1 expression is correlated with DCE-MRI perfusion parameters.

Dynamics of cerebral blood volume during and after middle cerebral artery occlusion in rats – Comparison between ultrafast ultrasound and dynamic susceptibility contrast-enhanced MRI measurements

Tomographic perfusion imaging techniques are integral to translational stroke research paradigms that advance our understanding of the disease. Functional ultrasound (fUS) is an emerging technique that informs on cerebral blood volume (CBV) through ultrasensitive Doppler and flow velocity (CBFv) through ultrafast localization microscopy. It is not known how experimental results compare with a classical CBV-probing technique such as dynamic susceptibility contrast-enhanced perfusion MRI (DSC-MRI). To that end, we assessed hemodynamics based on uUS (n = 6) or DSC-MRI (n = 7) before, during and up to three hours after 90-minute filament-induced middle cerebral artery occlusion (MCAO) in rats. Recanalization was followed by a brief hyperperfusion response, after which CBV and CBFv temporarily normalized but progressively declined after one hour in the lesion territory. DSC-MRI data corroborated the incomplete restoration of CBV after recanalization, which may have been caused by the free-breathing anesthetic regimen. During occlusion, MCAO-induced hypoperfusion was more discrepant between either technique, likely attributable to artefactual signal mechanisms related to slow flow, and processing algorithms employed for either technique. In vivo uUS- and DSC-MRI-derived measures of CBV enable serial whole-brain assessment of post-stroke hemodynamics, but readouts from both techniques need to be interpreted cautiously in situations of very low blood flow.

Correlation of Perfusion Metrics with Ki-67 Proliferation Index and Axillary Involvement as a Prognostic Marker in Breast Carcinoma Cases: A Dynamic Contrast-Enhanced Perfusion MRI Study.

Our study aims to reveal clinically helpful prognostic markers using quantitative radiologic data from perfusion magnetic resonance imaging for patients with locally advanced carcinoma, using the Ki-67 index as a surrogate. Patients who received a breast cancer diagnosis and had undergone dynamic contrast-enhanced magnetic resonance imaging of the breast for pretreatment evaluation and follow-up were searched retrospectively. We evaluated the MRI studies for perfusion parameters and various categories and compared them to the Ki-67 index. Axillary involvement was categorized as low (N0-N1) or high (N2-N3) according to clinical stage. A total sum of 60 patients' data was included in this study. Perfusion parameters and Ki-67 showed a significant correlation with the transfer constant (Ktrans) (ρ = 0.554 p = 0.00), reverse transfer constant (Kep) (ρ = 0.454 p = 0.00), and initial area under the gadolinium curve (IAUGC) (ρ = 0.619 p = 0.00). The IAUGC was also significantly different between axillary stage groups (Z = 2.478 p = 0.013). Outside of our primary hypothesis, associations between axillary stage and contrast enhancement (x2 = 8.023 p = 0.046) and filling patterns (x2 = 8.751 p = 0.013) were detected. In conclusion, these parameters are potential prognostic markers in patients with moderate Ki-67 indices, such as those in our study group. The relationship between axillary status and perfusion parameters also has the potential to determine patients who would benefit from limited axillary dissection.

Delay of Aortic Arterial Input Function Time Improves Detection of Malignant Vertebral Body Lesions on Dynamic Contrast-Enhanced MRI Perfusion.

Dynamic contrast-enhanced MRI (DCE) is an emerging modality in the study of vertebral body malignancies. DCE-MRI analysis relies on a pharmacokinetic model, which assumes that contrast uptake is simultaneous in the feeding of arteries and tissues of interest. While true in the highly vascularized brain, the perfusion of the spine is delayed. This delay of contrast reaching vertebral body lesions can affect DCE-MRI analyses, leading to misdiagnosis for the presence of active malignancy in the bone marrow. To overcome the limitation of delayed contrast arrival to vertebral body lesions, we shifted the arterial input function (AIF) curve over a series of phases and recalculated the plasma volume values (Vp) for each phase shift. We hypothesized that shifting the AIF tracer curve would better reflect actual contrast perfusion, thereby improving the accuracy of Vp maps in metastases. We evaluated 18 biopsy-proven vertebral body metastases in which standard DCE-MRI analysis failed to demonstrate the expected increase in Vp. We manually delayed the AIF curve for multiple phases, defined as the scan-specific phase temporal resolution, and analyzed DCE-MRI parameters with the new AIF curves. All patients were found to require at least one phase-shift delay in the calculated AIF to better visualize metastatic spinal lesions and improve quantitation of Vp. Average normalized Vp values were 1.78 ± 1.88 for zero phase shifts (P0), 4.72 ± 4.31 for one phase shift (P1), and 5.59 ± 4.41 for two phase shifts (P2). Mann-Whitney U tests obtained p-values = 0.003 between P0 and P1, and 0.0004 between P0 and P2. This study demonstrates that image processing analysis for DCE-MRI in patients with spinal metastases requires a careful review of signal intensity curve, as well as a possible adjustment of the phase of aortic AIF to increase the accuracy of Vp.

Advanced imaging of head and neck infections.

When head and neck infection is suspected, appropriate imaging contributes to treatment decisions and prognosis. While contrast-enhanced CT is the standard imaging modality for evaluating head and neck infections, MRI can better characterize the skull base, intracranial involvement, and osteomyelitis, implying that these are complementary techniques for a comprehensive assessment. Both CT and MRI are useful in the evaluation of abscesses and thrombophlebitis, while MRI is especially useful in the evaluation of intracranial inflammatory spread/abscess formation, differentiation of abscess from other conditions, evaluation of the presence and activity of inflammation and osteomyelitis, evaluation of mastoid extension in middle ear cholesteatoma, and evaluation of facial neuritis and labyrinthitis. Apparent diffusion coefficient derived from diffusion-weighted imaging is useful for differential diagnosis and treatment response of head and neck infections in various anatomical sites. Dynamic contrast-enhanced MRI perfusion may be useful in assessing the activity of skull base osteomyelitis. MR bone imaging may be of additional value in evaluating bony structures of the skull base and jaw. Dual-energy CT is helpful in reducing metal artifacts, evaluating deep neck abscess, and detecting salivary stones. Subtraction CT techniques are used to detect progressive bone-destructive changes and to reduce dental amalgam artifacts. This article provides a region-based approach to the imaging evaluation of head and neck infections, using both conventional and advanced imaging techniques.

Fluid Mechanics Approach to Perfusion Quantification: Vasculature Computational Fluid Dynamics Simulation, Quantitative Transport Mapping (QTM) Analysis of Dynamics Contrast Enhanced MRI, and Application in Nonalcoholic Fatty Liver Disease Classification.

We quantify liver perfusion using quantitative transport mapping (QTM) method that is free of arterial input function (AIF). QTM method is validated in a vasculature computational fluid dynamics (CFD) simulation and is applied for processing dynamic contrast enhanced (DCE) MRI images in differentiating liver with nonalcoholic fatty liver disease (NAFLD) from healthy controls using pathology reference in a preclinical rabbit model. QTM method was validated on a liver perfusion simulation based on fluid dynamics using a rat liver vasculature model and the mass transport equation. In the NAFLD grading task, DCE MRI images of 7 adult rabbits with methionine choline-deficient diet-induced nonalcoholic steatohepatitis (NASH), 8 adult rabbits with simple steatosis (SS) were acquired and processed using QTM method and dual-input two compartment Kety's method respectively. Statistical analysis was performed on six perfusion parameters: velocity magnitude | u | derived from QTM, liver arterial blood flow LBFa, liver venous blood flow LBFv, permeability Ktrans, blood volume Vp and extravascular space volume Ve averaged in liver ROI. In the simulation, QTM method successfully reconstructed blood flow, reduced error by 48% compared to Kety's method. In the preclinical study, only QTM |u| showed significant difference between high grade NAFLD group and low grade NAFLD group. QTM postprocesses DCE-MRI automatically through deconvolution in space and time to solve the inverse problem of the transport equation. Comparing with Kety's method, QTM method showed higher accuracy and better differentiation in NAFLD classification task. We propose to apply QTM method in liver DCE MRI perfusion quantification.

DWI and Dynamic Contrast-enhanced Perfusion MRI for Differentiation of Common Skull Base Tumors

HomeRadiology: Imaging CancerVol. 5, No. 2 Previous Research HighlightsDWI and Dynamic Contrast-enhanced Perfusion MRI for Differentiation of Common Skull Base TumorsNahyun JoNahyun JoNahyun JoPublished Online:Mar 31 2023https://doi.org/10.1148/rycan.239009MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In Highlighted ArticleOta Y, Liao E, Capizzano AA, et al. Differentiation of Skull Base Chondrosarcomas, Chordomas, and Metastases: Utility of DWI and Dynamic Contrast-Enhanced Perfusion MR Imaging. AJNR Am J Neuroradiol 2022;43(9):1325–1332. doi: https://doi.org/10.3174/ajnr.a7607 Crossref, Medline, Google ScholarArticle HistoryPublished online: Mar 31 2023 FiguresReferencesRelatedDetailsRecommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 5, No. 2 Metrics Altmetric Score PDF download

Abstract 123: In Vivo Permeability Of The Aging Brain With Cerebral Microbleeds

Introduction: Age-related cerebral microbleeds (CMBs) are vascular changes associated with an increased risk of intracerebral hemorrhage (ICH) and neurocognitive decline. CMBs share a strikingly similar appearance on MRI to cerebral cavernous malformations (CCMs), which are leaky capillary lesions predisposing patients to brain bleeding. Our team had previously shown increased vascular permeability in CCM and in the brain of older subjects, with common circulating biomarkers of angiogenesis and inflammation. We examine here whether vascular permeability is greater in the aging brain harboring CBMs. Methods: Dynamic Contrast-Enhanced Quantitative Perfusion (DCEQP) MRI, which assesses vascular permeability, was acquired for 7 patients with CMBs (&gt;50 years old), 10 old (&gt;50 years old), and 9 young (18-30 years old) healthy subjects without CMBs. Cases with active brain disease, stroke or seizure in the prior year were excluded. DCEQP scans were co-registered onto their respective T1-weighted images, normalized using the Desikan-Killiany atlas, and finally segmented into white and gray matter within frontal, temporal, parietal, and occipital lobes. The mean permeability was calculated within each segmented area, and values were compared between the 3 groups of interest. Results: Increased permeability was observed within the frontal white and gray matter as well as the parietal gray matter of patients with CMBs compared to age matched old healthy controls (both: p&lt;0.05). In addition, greater background brain permeability was also observed in old healthy controls compared to young healthy controls, in the occipital white matter, as well as the white and gray matter in the temporal and parietal lobes (all: p&lt;0.05). Conclusion: We confirm that the human brain exhibits increased vascular permeability with aging, and this is more pronounced in brains harboring CMBs. This may provide insight into the pathogenesis of CMBs, potentially reflecting end stage age-related failure of the blood brain barrier, associated with frank bleeding. These results offer a starting point for the development of diagnostic and prognostic imaging biomarkers of CMBs, which can help stratify patients at higher risk of future ICH and cognitive decline.

Multi-parametric MRI without artificial erection for preoperative assessment of primary penile carcinoma: A pilot study on the correlation between imaging and histopathological findings

PurposeWe aimed to evaluate the diagnostic potential of non-erectile multi-parametric magnetic resonance imaging (mpMRI) for preoperative assessment of primary penile squamous cell carcinoma (SCC). MethodTwenty-five patients who underwent surgery for penile SCC were included. Preoperative mpMRI without artificial erection was performed in all patients. The preoperative MRI protocol consisted of high-resolution morphological and functional sequences (diffusion-weighted imaging and dynamic contrast-enhanced MRI perfusion) covering the penis and lower pelvis. T and N staging, according to the 8th edition of the Union for International Cancer Control TNM classification, as well as the largest diameter and thickness/infiltration depth of the primary lesions were determined in all patients. Imaging data were retrospectively collected and compared with the final histopathology reports. ResultsVery good agreement was observed between MRI and histopathology for the involvement of corpus spongiosum (p = 0.002) and good agreement was observed for the involvement of penile urethra and tunica albuginea/corpus cavernosum (p < 0.001 and p = 0.007, respectively). Good agreement was observed between MRI and histopathology for overall T staging and weaker, but still good agreement was observed for N staging (p < 0.001 and p = 0.002, respectively). A strong and significant correlation was observed between MRI and histopathology for the largest diameter and thickness/infiltration depth of the primary lesions (p < 0.001). ConclusionsGood concordance was observed between MRI and histopathological findings. Our initial findings indicate that non-erectile mpMRI is useful in preoperative assessment of primary penile SCC.

Radiomics-based evaluation and possible characterization of dynamic contrast enhanced (DCE) perfusion derived different sub-regions of Glioblastoma

BackgroundGlioblastoma (GB) is among the most devastative brain tumors, which usually comprises sub-regions like enhancing tumor (ET), non-enhancing tumor (NET), edema (ED), and necrosis (NEC) as described on MRI. Semi-automated algorithms to extract these tumor subpart volumes and boundaries have been demonstrated using dynamic contrast-enhanced (DCE) perfusion imaging. We aim to characterize these sub-regions derived from DCE perfusion MRI using routine 3D post-contrast-T1 (T1GD) and FLAIR images with the aid of Radiomics analysis. We also explored the possibility of separating edema from tumor sub-regions by extracting the most influential radiomics features. MethodsA total of 89 patients with histopathological confirmed IDH wild type GB were considered, who underwent the MR imaging with DCE perfusion-MRI. Perfusion and kinetic indices were computed and further used to segment tumor sub-regions. Radiomics features were extracted from FLAIR and T1GD images with PyRadiomics tool. Statistical analysis of the features was carried out using two approaches as well as machine learning (ML) models were constructed separately, i) within different tumor sub-regions and ii) ED as one category and the remaining sub-regions combined as another category. ML based predictive feature maps was also constructed. ResultsSeven features found to be statistically significant to differentiate tumor sub-regions in FLAIR and T1GD images, with p-value < 0.05 and AUC values in the range of 0.72 to 0.93. However, the edema features stood out in the analysis. In the second approach, the ML model was able to categorize the ED from the rest of the tumor sub-regions in FLAIR and T1GD images with AUC of 0.95 and 0.89 respectively. ConclusionRadiomics-based specific feature values and maps help to characterize different tumor sub-regions. However, the GLDM_DependenceNonUniformity feature appears to be most specific for separating edema from the remaining tumor sub-regions using conventional FLAIR images. This may be of value in the segmentation of edema from tumors using conventional MRI in the future.

NIMG-21. INTERIM ANALYSIS OF A PHASE II STUDY OF MULTIPARAMETRIC MR-GUIDED HIGH-DOSE RESPONSE-ADAPTIVE RADIOTHERAPY WITH CONCURRENT TEMOZOLOMIDE IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA

Abstract BACKGROUND Biologically-informed radiotherapy (RT) targeting an adversely prognostic hypercellular/hyperperfused imaging phenotype in patients with newly diagnosed glioblastoma (GBM) may improve outcomes by identifying emerging regions of treatment resistance associated with survival (OS), and is under investigation in an ongoing Phase II trial (NCT04574856) of individualized, response-adaptive RT. METHODS In this single-arm phase II study, patients with newly diagnosed GBM following resection undergo dose-intensified chemoradiation targeting the residual hypercellular (TVHCV, mean contralateral normal brain+2SD) and hyperperfused tumor volume (TVCBV, contralateral normal frontal grey matter+1SD) identified using high b-value diffusion-weighted and dynamic contrast-enhanced perfusion MRI. The combination of TVHCVCBV (TVHCVCBV) is treated to 50 Gy in 20 fractions (2.5 Gy/fraction), and following mid-RT reassessment, the persistent and developing TVHCVCBV is treated to 30 Gy in 10 fractions (3 Gy/fraction). The primary endpoint is improvement in OS, with planned interim safety analysis. RESULTS Since October 2020, 16 of 30 patients have been enrolled. Median age was 58 years (range, 29-75) and 69% were male. No patient underwent biopsy only, and 50% had gross total resection; 23% had MGMT methylated tumors. Median TVHCV/TVCBV was 6.9 cc (range, 1.9-42.8) pre-RT and 30% (range, 1-72%) was nonenhancing. By mid-RT, TVHCVCBV was reduced to 4.2 cc (range, 0.8-34.3) and 47% (range, 3-74%) was nonenhancing. The TVHCVCBV persisting from pre- to mid-RT was 2.3 cc (range, 0-24.2), with an additional 1.8 cc (range, 0.3-20.6) newly developing outside the initial region. All patients underwent adaptive replanning for boost without interruption. Planned interim analysis determined an acceptable rate of neurologic toxicity and safety to continue enrollment. CONCLUSION Individualized, response adaptive RT using an advanced imaging biomarker to assess emerging and especially non-enhancing regions of treatment resistance in patients with GBM is feasible, with short term safety in an early cohort and longer-term efficacy outcomes anticipated with ongoing accrual.

Normalized Dynamic Contrast-enhanced Perfusion and Diffusion-weighted MRI Parameters Distinguish Head and Neck Cancer Recurrence from Posttreatment Changes.

Normalized Dynamic Contrast-enhanced Perfusion and Diffusion-weighted MRI Parameters Distinguish Head and Neck Cancer Recurrence from Posttreatment Changes.

Prognostic factors in primary central nervous system lymphoma.

Primary central nervous system lymphoma (PCNSL) is a rare and aggressive extranodal diffuse large B cell lymphoma. Despite its apparent immunopathological homogeneity, PCNSL displays a wide variability in outcome. Identifying prognostic factors is of importance for patient stratification and clinical decision-making. The purpose of this review is to focus on the clinical, neuroradiological and biological variables correlated with the prognosis at the time of diagnosis in immunocompetent patients. Age and performance status remain the most consistent clinical prognostic factors. The current literature suggests that neurocognitive dysfunction is an independent predictor of poor outcome. Cumulating data support the prognostic value of increased interleukin-10 level in the cerebrospinal fluid (CSF), in addition to its interest as a diagnostic biomarker. Advances in neuroimaging and in omics have identified several semi-quantitative radiological features (apparent diffusion restriction measures, dynamic contrast-enhanced perfusion MRI (pMRI) pattern and 18F-fluorodeoxyglucose metabolism) and molecular genetic alterations with prognostic impact in PCNSL. Validation of new biologic and neuroimaging markers in prospective studies is required before integrating future prognostic scoring systems. In the era of radiomic, large clinicoradiological and molecular databases are needed to develop multimodal artificial intelligence algorithms for the prediction of accurate outcome.

Reproducibility and Optimal Arterial Input Function Selection in Dynamic Contrast-Enhanced Perfusion MRI in the Healthy Brain.

Dynamic contrast-enhanced MRI (DCE-MRI) has seen increasing use for quantification of low level of blood-brain barrier (BBB) leakage in various pathological disease states and correlations with clinical outcomes. However, currently there exists limited studies on reproducibility in healthy controls, which is important for the establishment of a normality threshold for future research. To investigate the reproducibility of DCE-MRI and to evaluate the effect of arterial input function (AIF) selection and manual region of interests (ROI) delineation vs. automated global segmentation. Prospective. A total of 16 healthy controls; 11 females; mean age 28.7 years (SD 10.1). A 3T; GE DCE; 3D TFE T1WI. 2D TSE T2. The influx constant Ki , a measure of BBB permeability, and Vp , the blood plasma volume, was calculated using the Patlak model. Cerebral blood flow (CBF) was calculated using Tikhonov model free deconvolution. Manual tissue ROIs, drawn by H.J.S. (30+ years of experience), were compared to automatic tissue segmentation. Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) was used to assess reproducibility. Bland-Altman plots were used to evaluate agreement between measurements day 1 vs. day 2, and manual vs. segmentation method. Ki showed excellent reproducibility in both white and gray matter with an ICC between 0.79 and 0.82 and excellent agreement between manual ROI and automatic segmentation, with an ICC of 0.89 for Ki in WM. Furthermore, Ki values in gray and white matter conforms with histological tissue characteristics, where gray matter generally has a 2-fold higher vessel density. The highest reproducibility measures of Ki (ICC=0.83), CBF (ICC=0.77) and Vd (ICC=0.83) was obtained with the AIF sampled in the internal carotid artery (ICA). DCE-MRI shows excellent reproducibility of pharmacokinetic variables derived from healthy controls. 2 TECHNICAL EFFICACY: Stage 2.

NEIM-08 A PHASE II STUDY OF MULTIPARAMETRIC MR-GUIDED HIGH DOSE ADAPTIVE RADIOTHERAPY WITH CONCURRENT TEMOZOLOMIDE IN PATIENTS WITH NEWLY DIAGNOSED GLIOBLASTOMA

BACKGROUNDBiologically-informed radiotherapy (RT) targeting an adversely prognostic hypercellular/hyperperfused imaging phenotype in patients with newly diagnosed glioblastoma (GBM) may improve outcomes by identifying emerging regions of treatment resistance associated with survival (OS), and is under investigation as a target for individualized, adaptive RT in an ongoing Phase II trial (NCT04574856). METHODSIn this single-arm study, patients with newly diagnosed GBM following resection undergo dose-intensified chemoRT targeting the residual hypercellular (TVHCV, mean contralateral normal brain+2SD) and hyperperfused tumor volume (TVCBV, contralateral normal frontal grey matter+1SD) identified using high b-value diffusion-weighted and dynamic contrast-enhanced perfusion MRI. TVHCV/TVCBV is treated to 50 Gy in 20 fractions (2.5 Gy/fraction), and following mid-RT reassessment, the persistent and developing TVHCV/TVCBV is treated to 30 Gy in 10 fractions (3 Gy/fraction). The primary endpoint is improvement in OS, with planned interim safety analysis. RESULTSSince October 2020, 16 of 30 patients have been enrolled. Median age was 58 years (range, 29-75) and 69% were male. No patient underwent biopsy, and 50% had gross total resection; 23% had MGMT methylated tumors, and all except 2 were IDHwt. Median TVHCV/TVCBV was 6.9 cc (range, 1.9-42.8) pre-RT and 30% (range, 1-72%) was nonenhancing. By mid-RT, TVHCV/TVCBV was reduced to 4.2 cc (range, 0.8-34.3) and 47% (range, 3-74%) was nonenhancing. The TVHCV/TVCBV persisting from pre- to mid-RT was 2.3 cc (range, 0-24.2), with an additional 1.8 cc (range, 0.3-20.6) newly developing outside of the initial region. All patients underwent adaptive replanning for boost without interruption. Planned interim analysis determined an acceptable rate of neurologic toxicity and safety to continue enrollment. CONCLUSIONIndividualized, adaptive radiotherapy using an advanced imaging biomarker to assess emerging and especially non-enhancing regions of treatment resistance in patients with GBM is feasible, with short term safety in an early cohort and longer-term efficacy outcomes anticipated with ongoing accrual.

Normalized Parameters of Dynamic Contrast-Enhanced Perfusion MRI and DWI-ADC for Differentiation between Posttreatment Changes and Recurrence in Head and Neck Cancer.

Differentiating recurrence from benign posttreatment changes has clinical importance in the imaging follow-up of head and neck cancer. This study aimed to investigate the utility of normalized dynamic contrast-enhanced MR imaging and ADC for their differentiation. This study included 51 patients with a history of head and neck cancer who underwent follow-up dynamic contrast-enhanced MR imaging with DWI-ADC, of whom 25 had recurrences and 26 had benign posttreatment changes. Quantitative and semiquantitative dynamic contrast-enhanced MR imaging parameters and ADC of the ROI and reference region were analyzed. Normalized dynamic contrast-enhanced MR imaging parameters and normalized DWI-ADC parameters were calculated by dividing the ROI by the reference region. Normalized plasma volume, volume transfer constant between extravascular extracellular space and blood plasma per minute (K trans), area under the curve, and wash-in were significantly higher in patients with recurrence than in those with benign posttreatment change (P = .003 to <.001). The normalized mean ADC was significantly lower in patients with recurrence than in those with benign posttreatment change (P < .001). The area under the receiver operating characteristic curve of the combination of normalized dynamic contrast-enhanced MR imaging parameters with significance (normalized plasma volume, normalized extravascular extracellular space volume per unit tissue volume, normalized K trans, normalized area under the curve, and normalized wash-in) and normalized mean ADC was 0.97 (95% CI, 0.93-1). Normalized dynamic contrast-enhanced MR imaging parameters, normalized mean ADC, and their combination were effective in differentiating recurrence and benign posttreatment changes in head and neck cancer.

Prediction of prostate cancer grade using fractal analysis of perfusion MRI: retrospective proof-of-principle study

ObjectivesMultiparametric MRI has high diagnostic accuracy for detecting prostate cancer, but non-invasive prediction of tumor grade remains challenging. Characterizing tumor perfusion by exploiting the fractal nature of vascular anatomy might elucidate the aggressive potential of a tumor. This study introduces the concept of fractal analysis for characterizing prostate cancer perfusion and reports about its usefulness for non-invasive prediction of tumor grade.MethodsWe retrospectively analyzed the openly available PROSTATEx dataset with 112 cancer foci in 99 patients. In all patients, histological grading groups specified by the International Society of Urological Pathology (ISUP) were obtained from in-bore MRI-guided biopsy. Fractal analysis of dynamic contrast-enhanced perfusion MRI sequences was performed, yielding fractal dimension (FD) as quantitative descriptor. Two-class and multiclass diagnostic accuracy was analyzed using area under the curve (AUC) receiver operating characteristic analysis, and optimal FD cutoffs were established. Additionally, we compared fractal analysis to conventional apparent diffusion coefficient (ADC) measurements.ResultsFractal analysis of perfusion allowed accurate differentiation of non-significant (group 1) and clinically significant (groups 2–5) cancer with a sensitivity of 91% (confidence interval [CI]: 83–96%) and a specificity of 86% (CI: 73–94%). FD correlated linearly with ISUP groups (r2 = 0.874, p < 0.001). Significant groupwise differences were obtained between low, intermediate, and high ISUP group 1–4 (p ≤ 0.001) but not group 5 tumors. Fractal analysis of perfusion was significantly more reliable than ADC in predicting non-significant and clinically significant cancer (AUCFD = 0.97 versus AUCADC = 0.77, p < 0.001).ConclusionFractal analysis of perfusion MRI accurately predicts prostate cancer grading in low-, intermediate-, and high-, but not highest-grade, tumors.Key Points• In 112 prostate carcinomas, fractal analysis of MR perfusion imaging accurately differentiated low-, intermediate-, and high-grade cancer (ISUP grade groups 1–4).• Fractal analysis detected clinically significant prostate cancer with a sensitivity of 91% (83–96%) and a specificity of 86% (73–94%).• Fractal dimension of perfusion at the tumor margin may provide an imaging biomarker to predict prostate cancer grading.