Development and Validation of Automated Magnetic Resonance Parkinsonism Index 2.0 to Distinguish Progressive Supranuclear Palsy-Parkinsonism From Parkinson's Disease.

BACKGROUND: Differentiating progressive supranuclear palsy-parkinsonism (PSP-P) from Parkinson's disease (PD) is clinically challenging. OBJECTIVE: This study aimed to develop an automated Magnetic Resonance Parkinsonism Index 2.0 (MRPI 2.0) algorithm to distinguish PSP-P from PD and to validate its diagnostic performance in two large independent cohorts. METHODS: We enrolled 676 participants: a training cohort (n = 346; 43 PSP-P, 194 PD, and 109 control subjects) from our center and an independent testing cohort (n = 330; 62 PSP-P, 171 PD, and 97 control subjects) from an international research group. We developed a new in-house algorithm for MRPI 2.0 calculation and assessed its performance in distinguishing PSP-P from PD and control subjects in both cohorts using receiver operating characteristic curves. RESULTS: The automated MRPI 2.0 showed excellent performance in differentiating patients with PSP-P from patients with PD and control subjects both in the training cohort (area under the receiver operating characteristic curve [AUC] = 0.93 [95% confidence interval, 0.89-0.98] and AUC = 0.97 [0.93-1.00], respectively) and in the international testing cohort (PSP-P versus PD, AUC = 0.92 [0.87-0.97]; PSP-P versus controls, AUC = 0.94 [0.90-0.98]), suggesting the generalizability of the results. The automated MRPI 2.0 also accurately distinguished between PSP-P and PD in the early stage of the diseases (AUC = 0.91 [0.84-0.97]). A strong correlation (r = 0.91, P < 0.001) was found between automated and manual MRPI 2.0 values. CONCLUSIONS: Our study provides an automated, validated, and generalizable magnetic resonance biomarker to distinguish PSP-P from PD. The use of the automated MRPI 2.0 algorithm rather than manual measurements could be important to standardize measures in patients with PSP-P across centers, with a positive impact on multicenter studies and clinical trials involving patients from different geographic regions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A New MRI Measure to Early Differentiate Progressive Supranuclear Palsy From De Novo Parkinson's Disease in Clinical Practice: An International Study.

BACKGROUND: Enlargement of the third ventricle has been reported in atypical parkinsonism. We investigated whether the measurement of third ventricle width could distinguish Parkinson's disease (PD) from progressive supranuclear palsy (PSP). METHODS: We assessed a new MR T1-weighted measurement (third ventricle width/internal skull diameter) in a training cohort of 268 participants (98 PD, 73 PSP, 98 controls from our center) and in a testing cohort of 291 participants (82 de novo PD patients and 133 controls from the Parkinson's Progression Markers Initiative, 76 early-stage PSP from an international research group). PD diagnosis was confirmed after a 4-year follow-up. Diagnostic performance of the third ventricle/internal skull diameter was assessed using receiver operating characteristic curve with bootstrapping; the area under the curve of the training cohort was compared with the area under the curve of the testing cohort using the De Long test. RESULTS: In both cohorts, third ventricle/internal skull diameter values did not differ between PD and controls but were significantly lower in PD than in PSP patients (P < 0.0001). In PD, third ventricle/internal skull diameter values did not change significantly between baseline and follow-up evaluation. Receiver operating characteristic analysis accurately differentiated PD from PSP in the training cohort (area under the curve, 0.94; 95% CI, 91.1-97.6; cutoff, 5.72) and in the testing cohort (area under the curve, 0.91; 95% CI, 87.0-97.0; cutoff,: 5.88), validating the generalizability of the results. CONCLUSION: Our study provides a new reliable and validated MRI measurement for the early differentiation of PD and PSP. The simplicity and generalizability of this biomarker make it suitable for routine clinical practice and for selection of patients in clinical trials worldwide. © 2020 International Parkinson and Movement Disorder Society.

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI.

Fractal analysis represents a promising new approach to structural neuroimaging data, yet systematic evaluation of the fractal dimension (FD) as a marker of structural brain complexity is scarce. Here we present in-depth methodological assessment of FD estimation in structural brain MRI. On the computational side, we show that spatial scale optimization can significantly improve FD estimation accuracy, as suggested by simulation studies with known FD values. For empirical evaluation, we analyzed two recent open-access neuroimaging data sets (MASSIVE and Midnight Scan Club), stratified by fundamental image characteristics including registration, sequence weighting, spatial resolution, segmentation procedures, tissue type, and image complexity. Deviation analyses showed high repeated-acquisition stability of the FD estimates across both data sets, with differential deviation susceptibility according to image characteristics. While less frequently studied in the literature, FD estimation in T2-weighted images yielded robust outcomes. Importantly, we observed a significant impact of image registration on absolute FD estimates. Applying different registration schemes, we found that unbalanced registration induced (a) repeated-measurement deviation clusters around the registration target, (b) strong bidirectional correlations among image analysis groups, and (c) spurious associations between the FD and an index of structural similarity, and these effects were strongly attenuated by reregistration in both data sets. Indeed, differences in FD between scans did not simply track differences in structure per se, suggesting that structural complexity and structural similarity represent distinct aspects of structural brain MRI. In conclusion, scale optimization can improve FD estimation accuracy, and empirical FD estimates are reliable yet sensitive to image characteristics.

Potential of Gd-EOB-DTPA as an imaging biomarker for liver injury estimation after radiation therapy.

BACKGROUND: Hepatic radiation injury severely restricts irradiation treatment for liver carcinoma. The purpose of this study was to investigate the clinical application of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI (EOB-MRI) in the assessment of liver function after external radiation therapy and to determine the relationship between focal liver reaction (FLR) and liver function. METHODS: A total of 47 patients with liver malignancies who underwent external beam radiation therapy were enrolled. EOB-MRI was performed on each patient at approximately one month post-radiotherapy. The hepatobiliary (HPB) phase images from EOB-MRI were fused with the planning CT images, and the isodose lines from the patients' treatment plans were overlaid onto the fused images. The correlation of the EOB-MR image intensity distribution with the isodose lines was studied. We also compared liver function in patients between pre-treatment and post-treatment. RESULTS: Decreased uptake of Gd-EOB-DTPA, which was manifested by well-demarcated focal hypointensity of the liver parenchyma or FLR to high-dose radiation, was observed in the irradiated areas of 38 patients. The radiotherapy isodose line of decreased uptake area of Gd-EOB-DTPA was 30-46 Gy. The median corresponding dose curve of FLR was 34.4 Gy. Nine patients showed the absence of decreased uptake area of Gd-EOB-DTPA in the irradiated areas. Compared to the 38 patients with the presence of decreased uptake area of Gd-EOB-DTPA, 9 patients with the absence of decreased uptake area of Gd-EOB-DTPA showed significant higher levels of total bile acid, total bilirubin, direct bilirubin and alpha-fetoprotein (P < 0.05). There were no significant differences in alanine transaminase, aspartate aminotransferase, gamma-glutamyl transpeptidase or albumin levels between the two groups (P > 0.05). CONCLUSIONS: Visible uptake of Gd-EOB-DTPA by the liver parenchyma was significantly associated with liver function parameters. EOB-MRI can be a valuable imaging biomarker for the assessment of liver parenchyma function outside of radiation area.

Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis.

Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effective at the early stages of the pathology. Our objective is to investigate the efficiency of a new MR-based hippocampal grading score to detect incident dementia in cognitively intact patients. This new score is based on a pattern recognition strategy, providing a grading measure that reflects the similarity of the anatomical patterns of the subject under study with dataset composed of healthy subjects and patients with AD. Hippocampal grading was evaluated on subjects from the Three-City cohort, with a followup period of 12 years. Experiments demonstrate that hippocampal grading yields prediction accuracy up to 72.5% (P < 0.0001) 7 years before conversion to AD, better than both hippocampal volume (58.1%, P = 0.04) and MMSE score (56.9%, P = 0.08). The area under the ROC curve (AUC) supports the efficiency of imaging biomarkers with a gain of 8.4 percentage points for hippocampal grade (73.0%) over hippocampal volume (64.6%). Adaptation of the proposed framework to clinical score estimation is also presented. Compared with previous studies investigating new biomarkers for AD prediction over much shorter periods, the very long followup of the Three-City cohort demonstrates the important clinical potential of the proposed imaging biomarker. The high accuracy obtained with this new imaging biomarker paves the way for computer-based prognostic aides to help the clinician identify cognitively intact subjects that are at high risk to develop AD.

Anxiety symptoms in amnestic mild cognitive impairment are associated with medial temporal atrophy and predict conversion to Alzheimer disease.

OBJECTIVE: To test the hypothesis that anxiety in amnestic mild cognitive impairment (aMCI) increases rates of conversion to Alzheimer disease (AD) and to identify potential neural mechanisms underlying such an association. METHODS: Participants (N = 376) with aMCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were studied over a median period of 36 months. A Cox proportional-hazards model was used to assess the association between anxiety severity ratings on the Neuropsychiatric Inventory Questionnaire and AD risk. Other variables were depression, memory loss, and MRI-derived AD-related regions of interest (ROIs), including hippocampal, amygdalar, entorhinal cortical (EC) volumes, and EC thickness, In addition, a linear regression model was used to determine the effect of anxiety in aMCI on rates of atrophy within ROIs. RESULTS: Anxiety severity increased rate of aMCI conversion to AD, after controlling for depression and cognitive decline. The association between anxiety and AD remained significant even with inclusion of ROI baseline values or atrophy rates as explanatory variables. Further, anxiety status predicted greater rates of decrease in EC volume. An association between anxiety and EC thickness missed significance. CONCLUSION: Anxiety symptoms in aMCI predict conversion to AD, over and beyond the effects of depression, memory loss, or atrophy within AD neuroimaging biomarkers. These findings, together with the greater EC atrophy rate predicted by anxiety, are compatible with the hypothesis that anxiety is not a prodromal noncognitive feature of AD but may accelerate decline toward AD through direct or indirect effects on EC.

The role and potential of using quantitative MRI biomarkers for imaging guidance in brain cancer radiotherapy treatment planning: A systematic review.

Background and purpose: Improving the accuracy of brain tumour radiotherapy (RT) treatment planning is important to optimise patient outcomes. This systematic review investigates primary studies providing clinical evidence for the integration of quantitative magnetic resonance imaging (qMRI) biomarkers and MRI radiomics to optimise brain tumour RT planning. Materials and methods: PubMed, Scopus, Embase and Web of Science databases were searched for all years until June 21, 2022. The search identified original articles demonstrating clinical evidence for the use of qMRI biomarkers and MRI radiomics for the optimization of brain cancer RT planning. Relevant information was extracted and tabulated, including qMRI metrics and techniques, impact on RT plan optimization and changes in target and normal tissue contouring and dose distribution. Results: Nineteen articles met the inclusion criteria. Studies were grouped according to the qMRI biomarkers into: 1) diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI; five studies); 2) diffusion tensor imaging (DTI; seven studies); and 3) MR spectroscopic imaging (MRSI; seven studies). No relevant MRI-based radiomics studies were identified. Integration of DTI maps offers the potential for improved organs at risk (OAR) sparing. MRSI metabolic maps are a promising technique for improving delineation accuracy in terms of heterogeneity and infiltration, with OAR sparing. No firm conclusions could be drawn regarding the integration of DWI metrics and PWI maps. Conclusions: Integration of qMRI metrics into RT planning offers the potential to improve delineation and OAR sparing. Clinical trials and consensus guidelines are required to demonstrate the clinical benefits of such approaches.

The emerging potential of quantitative MRI biomarkers for the early prediction of brain metastasis response after stereotactic radiosurgery: a scoping review.

Background: At present, the simple prognostic models based on clinical information for predicting the treatment outcomes of brain metastases (BMs) are subjective and delayed. Thus, we performed this systematic review of multiple studies to assess the potential of quantitative magnetic resonance imaging (MRI) biomarkers for the early prediction of treatment outcomes of brain metastases with stereotactic radiosurgery (SRS). Methods: We systematically searched the PubMed, Embase, Cochrane, Web of Science, and Clinical Trials.gov databases for articles published between February 1, 1991, and April 11, 2022, with no language restrictions. We included studies involving patients with BMs receiving SRS; the included patients were required to have definite pathology of a primary tumor and complete imaging data (pre- and post-SRS). We excluded the articles that included patients who had undergone previous surgery and those that did not include regular follow-up or corresponding MRI scans. Results: We identified 2,162 studies, of which 26 were included in our analysis, involving a total of 1,362 participants. All 26 studies explored the relevant MRI parameters to predict the prognosis of patients with BMs who received SRS. The outcomes were generalized according to the relationships between the anatomical/morphological, microstructural, vascular, and metabolic changes and SRS. Generally, with traditional MRI, there are several quantitative prognostic models based on preradiosurgical radiomics that predict the outcome of SRS treatment in local BM control. With the implementation of advanced MRI, the relative apparent diffusion coefficient (ADC), perfusion fraction (f), relative cerebral blood volume (rCBV), relative regional cerebral blood flow (rrCBF), interstitial fluid pressure (IFP), quadratic of time-dependent leakage (Ktrans 2), extracellular extravascular volume (ve), choline/creatine (Cho/Cr), nuclear Overhauser effect (NOE) peak, and intraextracellular water exchange rate constant (kIE ) were confirmed to be indicative of the therapeutic effect of SRS for BMs. Conclusions: Quantitative MRI biomarkers extracted from traditional or advanced MRI at different time points, which can represent the anatomical/morphological, microstructural, vascular, and metabolic changes, respectively, have been proposed as promising markers for the early prediction of SRS response in those with BMs. There are some limitations in this review, including the risk of selection bias, the limited number of study objects, the incomparability of the total data, and the subjectivity of the review process.

Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning.

Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48-95 years) with Alzheimer's disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer's disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer's disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer's disease continuum group (n = 718; consisting of amyloid-positive Alzheimer's disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer's disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer's disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56-0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer's disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer's disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer's disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer's disease.

Predicting progression of Alzheimer's disease using forward-to-backward bi-directional network with integrative imputation.

If left untreated, Alzheimer's disease (AD) is a leading cause of slowly progressive dementia. Therefore, it is critical to detect AD to prevent its progression. In this study, we propose a bidirectional progressive recurrent network with imputation (BiPro) that uses longitudinal data, including patient demographics and biomarkers of magnetic resonance imaging (MRI), to forecast clinical diagnoses and phenotypic measurements at multiple timepoints. To compensate for missing observations in the longitudinal data, we use an imputation module to inspect both temporal and multivariate relations associated with the mean and forward relations inherent in the time series data. To encode the imputed information, we define a modification of the long short-term memory (LSTM) cell by using a progressive module to compute the progression score of each biomarker between the given timepoint and the baseline through a negative exponential function. These features are used for the prediction task. The proposed system is an end-to-end deep recurrent network that can accomplish multiple tasks at the same time, including (1) imputing missing values, (2) forecasting phenotypic measurements, and (3) predicting the clinical status of a patient based on longitudinal data. We experimented on 1,335 participants from The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) challenge cohort. The proposed method achieved a mean area under the receiver-operating characteristic curve (mAUC) of 78% for predicting the clinical status of patients, a mean absolute error (MAE) of 3.5ml for forecasting MRI biomarkers, and an MAE of 6.9ml for missing value imputation. The results confirm that our proposed model outperforms prevalent approaches, and can be used to minimize the progression of Alzheimer's disease.

Temporal muscle thickness and survival in patients with amyotrophic lateral sclerosis.

Temporal muscle thickness (TMT) is a new potential MRI biomarker, which has shown prognostic relevance in neuro-oncology. We aim at investigating the potential prognostic value of TMT in patients with Amyotrophic Lateral Sclerosis (ALS). We retrospectively evaluated 30 ALS patients, whose clinical, Magnetic Resonance Imaging (MRI) and Electrodiagnostic testing (EDX) data were available, in comparison to age-matched 30 healthy subjects. TMT calculated on T1-weighted MR images was significantly lower in ALS patients than in healthy subjects (p < 0.001), correlating with the ALS Functional Rating Scale (FRS) (p:0.018) and compound motor action potential (CMAP) (p:0.012) in the patients group. Multivariate analysis of overall survival (OS) showed that the only parameters that remained significant were TMT (p:0.002, OR 0.45, 95%vCI: 0.28-0.75) and ALS FRS-R (p:0.023, OR: 0.80, 95%CI: 0.67-0.92). TMT seems to be a promising surrogate biomarker of survival and functional status in ALS. Our data deserve further investigations in multicenter and prospective trials.

MRI biomarkers identify the differential response of glioblastoma multiforme to anti-angiogenic therapy.

BACKGROUND: Although anti-angiogenic therapy (AATx) holds great promise for treatment of malignant gliomas, its therapeutic efficacy is not well understood and can potentially increase the aggressive recurrence of gliomas. It is essential to establish sensitive, noninvasive biomarkers that can detect failure of AATx and tumor recurrence early so that timely adaptive therapy can be instituted. We investigated the efficacy of MRI biomarkers that can detect response to different classes of AATxs used alone or in combination with radiation. METHODS: Murine intracranial glioma xenografts (NOD/SCID) were treated with sunitinib, VEGF-trap or B20 (a bevacizumab equivalent) alone or in combination with radiation. MRI images were acquired longitudinally before and after treatment, and various MRI parameters (apparent diffusion coefficient, T1w + contrast, dynamic contrast-enhanced [DCE], initial area under the contrast enhancement curve, and cerebral blood flow) were correlated to tumor cell proliferation, overall tumor growth, and tumor vascularity. RESULTS: Combinatorial therapies reduced tumor growth rate more efficiently than monotherapies. Apparent diffusion coefficient was an accurate measure of tumor cell density. Vascular endothelial growth factor (VEGF)-trap or B20, but not sunitinib, resulted in significant reduction or complete loss of contrast enhancement. This reduction was not due to a reduction in tumor growth or microvascular density, but rather was explained by a reduction in vessel permeability and perfusion. We established that contrast enhancement does not accurately reflect tumor volume or vascular density; however, DCE-derived parameters can be used as efficient noninvasive biomarkers of response to AATx. CONCLUSIONS: MRI parameters following therapy vary based on class of AATx. Validation of clinically relevant MRI parameters for individual AATx agents is necessary before incorporation into routine practice.

Rapid dramatic alterations to the tumor microstructure in pancreatic cancer following irreversible electroporation ablation.

AIM: NanoKnife(®) (Angiodynamics, Inc., NY, USA) or irreversible electroporation (IRE) is a newly available ablation technique to induce the formation of nanoscale pores within the cell membrane in targeted tissues. The purpose of this study was to elucidate morphological alterations following 30 min of IRE ablation in a mouse model of pancreatic cancer. MATERIALS & METHODS: Immunohistochemistry markers were compared with diffusion-weighted MRI apparent diffusion coefficient measurements before and after IRE ablation. RESULTS: Immunohistochemistry apoptosis index measurements were significantly higher in IRE-treated tumors than in controls. Rapid tissue alterations after 30 min of IRE ablation procedures (structural and morphological alterations along with significantly elevated apoptosis markers) were consistently observed and well correlated to apparent diffusion coefficient measurements. DISCUSSION: This imaging assay offers the potential to serve as an in vivo biomarker for noninvasive detection of tumor response following IRE ablation.