Magnetic resonance imaging (MRI) has an established role in the assessment of degenerative musculoskeletal conditions. However, conventional supine MRI findings often correlate poorly with clinical findings. Some patients experience accentuated back pain in the weight-bearing position. Therefore, supine MRI may underestimate the severity of degenerative spine findings. To try and improve the clinical validity of spine imaging, axial loading devices have been used with conventional supine MR imaging to simulate loading of the upright spine. More recently, upright weight-bearing MRI systems (0.25-0.6 T) were introduced, allowing images to be obtained in the standing or seated weight-bearing position and even during upright flexion or extension, rotation, or bending. Some scanners even enable capturing of real-time spinal movement. This review addresses the technical aspects and potential challenges of weight-bearing MRI, both in clinical practice and research.
Weight-bearing MRI
Weight-bearing MRI of the Lumbar Spine: Spinal Stenosis and Spondylolisthesis.
Symptoms of degenerative lumbar spinal stenosis include back pain, radiculopathy, claudication, and muscular fatigue that tend to be predominant in the standing position or during walking. Lumbar spondylolisthesis is also a well-known cause of spinal stenosis, lateral recess, and neural foraminal narrowing that tends to become more severe in the upright position. This indicates a functional positional component of both spinal stenosis and spondylolisthesis. Lumbar spinal stenosis and spondylolisthesis are typically evaluated by magnetic resonance imaging (MRI) performed in the supine position with a pillow under the patient’s lower limbs that slightly flexes the lumbar spine and ameliorates symptoms. Because these two entities tend to be aggravated in the upright position, it seems rational to also consider performing diagnostic imaging in these patients in the upright position. This article reviews the use of weight-bearing MRI for lumbar spinal stenosis and spondylolisthesis.
Role of Artificial Intelligence in Assessment of Peripheral Joint MRI in Inflammatory Arthritis: A Systematic Review and Meta-analysis
Performance of Machine Learning-Augmented Analysis of Radiomics for the Head and Neck Cancer Histopathological Diagnosis: A Systematic Review and Meta-Analysis
The Impact of a Significant Weight Loss on Inflammation Assessed on DCE-MRI and Static MRI in Knee Osteoarthritis: A Prospective Cohort Study
Decision Making in Surveillance of High-Grade Gliomas Using Perfusion MRI as Adjunct to Conventional MRI and Artificial Intelligence.
Copyright © 2019 by American Society of Clinical Oncology
Journal of Clinical Oncology. 2019 May;37(15)_suppl doi: 10.1200/JCO.2019.37.15_suppl.2054
Abstract
BACKGROUND:
Surveillance of High-Grade Gliomas (HGGs) remains a major challenge in clinical neurooncology. Histopathological validation is not an option during the course of disease and imaging surveillance suffers from ambiguous features of both disease progression and treatment related changes. This study aimed to differentiate between Pseudoprogression (PsP) and Progressive Disease (PD) using an artificial intelligence (support vector machine – SVM) classification algorithm.
METHODS:
Two groups of patients with histologically proven HGGs were analysed, a group with a single time point DSC perfusion MRI (45 patients) and a group with multiple time point DSC perfusion MRI (19 patients). Both groups included conventional MRI studies prior and after each perfusion MRI. This study design aimed to replicate decision making in clinical practice including multiple previous studies for each patient. SVM training was performed with all available MRI studies for each group and classification was based on different feature datasets from a single or multiple (subtracted features) time points. Classification accuracy comparisons were performed by calculating prediction error rates for different feature datasets and different time point analyses.
RESULTS:
Our results indicate that the addition of multiple time point perfusion MRI combined with structural (conventional with gadolinium-enhanced sequences) MRI features results in optimal classification performance (median error rate: 0.016, lowest value dispersion). Subtracted feature datasets improved classification performance, more prominently when the final and first perfusion studies were included in the analysis. On the contrary, in the single time point group analysis, structural feature-based classification performed best (median error rate: 0.012).
CONCLUSIONS:
Validation of our results with a larger patient cohort may have significant clinical importance in optimising imaging surveillance and clinical decision making for patients with HGG.
Multi-Parametric MRI as Supplement to mRANO Criteria for Response Assessment to MDNA55 in Adults with Recurrent or Progressive Glioblastoma.
Copyright © 2019 by American Society of Clinical Oncology
Journal of Clinical Oncology. 2019 May;37(15)_suppl doi: 10.1200/JCO.2019.37.15_suppl.e13559
Abstract
BACKGROUND:
Modified response assessment in neuro-oncology (mRANO) criteria are widely used in GBM but seem insufficient to capture Pseudoprogression (PsP), which occurs due to extensive inflammatory infiltration, increased vascular permeability, tumor necrosis and edema. mRANO criteria recommend volumetric response evaluation using contrast-enhanced T1 subtraction maps for identifying PsP. Our approach incorporates multi-parametric MRI biomarkers to unravel the true PsP from recurrence or distinguish Pseudo Response (PsR) – following anti-VEGF agents – from delayed (immuno)response.
METHODS:
Multiple time-points MRI (18-24h after convection-enhance delivery of the anti-IL4-R agent MDNA55, then at 30-day intervals) was utilized to determine response. Multi-parametric MRI biomarkers analyzed included (1) 3D-FLAIR-T2-based tumor volume assessment reflecting edema, necrosis and tumor infiltration; (2) 3D-gadolinium-enhanced-based tumor volume estimation reflecting active tumor infiltration, neo-angiogenesis and disrupted blood brain barrier; (3) Dynamic susceptibility contrast-based relative cerebral blood volume (rCBV) measurements for estimation of the vascular tumour properties; and (4) Diffusion weighted imaging – Apparent diffusion coefficient measurements that assess interstitial edema, tumor cellularity and ischemic injury.
RESULTS:
We demonstrate similar imaging phenotypes on conventional FLAIR-T2- and enhanced T1- MR images among different disease states (PsP vs true progression, PsR vs and immuno-response) and describe the perfusion and diffusion MRI biomarkers that improve response staging including PsP masking true progression, PsP masking clinical response, early progression with delayed response, and differentiation between true and PsR. The results are compared with the mRANO-based assessments for concurrence.
CONCLUSIONS:
Incorporating multi-parametric MRI measurements to determine the complex underlying tissue processes enables a better assessment of PsP, PsR and delayed tumour response, and can supplement mRANO-based response assessments in GBM patients undergoing novel immunotherapies.
Early Response Assessment Through Multiparametric MRI Based Endpoints In A Phase II Multicenter Study Evaluating the Efficacy of DPX-Survivac, Intermittent Low Dose Cyclophosphamide (CPA) and Pembrolizumab Combination Study in Subjects with Solid Tumors.
Copyright © 2019 by American Society of Clinical Oncology
Journal of Clinical Oncology. 2019 May;37(15)_suppl doi: 10.1200/JCO.2019.37.15_suppl.e14245
Abstract
BACKGROUND:
Accurate assessment of tumor response to immunotherapy is challenged by pseudoprogression that mimics true progression. Conventional imaging and RECIST assessment do not adequately distinguish between them given their inability to account for changes in the tumor microenvironment. DPX-Survivac is a novel T cell activating therapy that triggers immune responses against tumors expressing survivin and is being studied in this trial in combination with CPA and pembrolizumab in several solid tumors. Multiparametric MRI approaches – dynamic contrast-enhanced MRI and diffusion-weighted imaging MRI are useful for accurate assessment of structural, perfusion and vascular assessment of the lesion and may identify pseudoprogression and compare to the RECIST-based assessment.
METHODS:
The study will enroll up to 226 evaluable subjects in 5 different cohorts: ovarian cancer, HCC, NSCLC, bladder cancer and MSI-H cancer. These subjects will undergo initial imaging 28 days prior to treatment, to be assessed based on RECIST 1.1, and a pre-treatment tumor biopsy for quantitation of survivin and PD-L1 expression and MSI analyses. Treatment for 35 cycles or until disease progression. All patients will have CT images for RECIST 1.1 and iRECIST assessment. A subset of subjects will undergo mpMRI to calculate advanced imaging biomarkers.
RESULTS:
MRI, clinical and patient-reported outcomes will be analyzed.
CONCLUSIONS:
This study will provide important evidence on the utility of mpMRI + CT-based assessment of response to immunotherapy and use it as an adjunct to the CT-based RECIST criteria by providing insight on how tumor lesions are impacted by treatment.
Radiomics in Clinical Trials – The Rationale, Current Practices, and Future Considerations
Radiomics involves deep quantitative analysis of radiological images for structural and/or functional information. – It is a phenomic assessment of disease to understand lesion microstructure, microenvironment and molecular/cellular function. – In oncology, it helps us accurately classify, stratify and prognosticate tumors based on if, how and when they transform, infiltrate, involute or metastasize, – Utilizing radiomics in clinical trials is exploratory, and not an established end-point. – Integrating radiomics in an imaging-based clinical trials involves a streamlined workflow to handle large datasets, robust platforms to accommodate machine learning calculations, and seamless incorporation of derived insights into outcomes matrix.
Reporter Gene Imaging and its Role in Imaging-Based Drug Development.
This abstract presents how RGI can be used in drug development for pharmacodynamic and pharmacokinetic assessment of cellular, gene, oncolytic viral and immunotherapeutic approaches using MRI, PET, SPECT, Ultrasound, Bioluminescence and Fluoroscence. Some of the teaching points include further insight into RGI imaging probes that can be direct, indirect or activable; range from enzymes, protein receptors and cell membrane transporters and how RGI qualitatively and quantitatively assesses cell targeting, transfection, protein expression and intracellular processes.