scholarly journals Tissue damage detected by quantitative gradient echo MRI correlates with clinical progression in non-relapsing progressive MS

2021 ◽  
Author(s):  
Biao Xiang ◽  
Matthew R. Brier ◽  
Manasa Kanthamneni ◽  
Jie Wen ◽  
Abraham Z. Snyder ◽  
...  
Keyword(s):  
Disease Progression ◽  
Tissue Damage ◽  
Neurological Impairment ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Normal Appearing White Matter ◽  
Relapsing Remitting ◽  
Clinical Impairment ◽  
Cortical Grey Matter ◽  
Healthy Control

Abstract Background: Imaging biomarkers of progressive MS are needed. Quantitative gradient recalled echo (qGRE) MRI technique allows evaluation of tissue damage associated with microstructural damage in multiple sclerosis (MS). Objective: To evaluate qGRE-derived R2t* as an imaging biomarker of MS disease progression as compared to atrophy and lesion burden. Methods: Twenty-three non-relapsing progressive MS (PMS), twenty-two relapsing-remitting MS (RRMS) and eighteen healthy control participants were imaged with qGRE at 3T. PMS subjects were imaged and neurologically assessed every nine months over five sessions. In each imaging session, lesion burden, atrophy and R2t* in cortical grey matter (GM), deep GM, normal-appearing white matter (NAWM) were measured. Results: R2t* reductions correlated with neurological impairment cross-sectionally and longitudinally. PMS patients with clinically defined disease progression showed significantly faster decrease of R2t* in NAWM and deep GM compared with the clinically stable PMS group. Importantly, tissue damage measured by R2t* outperformed lesion burden and atrophy as a biomarker of progression during the study period. Conclusion: Clinical impairment and progression correlated with accumulating R2t*-defined microstructural tissue damage in deep GM and NAWM. qGRE-derived R2t* is a potential imaging biomarker of MS progression.

Interhemispheric transfer evaluation in multiple sclerosis 1The authors would like to thank Claude-Alain Hauert and Christoph Michel for their assistance in the evaluation of motor tapping and Michel Habib for his suggestions and comments. This work was supported by a grant from the Swiss Society of Multiple Sclerosis.

2000 ◽  
Vol 59 (3) ◽  
pp. 150-158 ◽  
Author(s):  
Nadia Ortiz ◽  
Michael Reicherts ◽  
Alan J. Pegna ◽  
Encarni Garran ◽  
Michel Chofflon ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Corpus Callosum ◽  
Lexical Decision ◽  
Dichotic Listening ◽  
Neurological Impairment ◽  
Interhemispheric Transfer ◽  
Decision Task ◽  
Swiss Society ◽  
Listening Test ◽  
Relapsing Remitting

Patients suffering from Multiple Sclerosis (MS) have frequently been found to suffer from damage to callosal fibers. Investigations have shown that this damage is associated with signs of hemisphere disconnections. The aim of our study was to provide evidence for the first signs of interhemispheric dysfunction in a mildly disabled MS population. Therefore, we explored whether the Interhemispheric Transfer (IT) deficit is multi-modal and sought to differentiate two MS evolution forms, on the basis of an interhemispheric disconnection index. Twenty-two patients with relapsing-remitting form of MS (RRMS) and 14 chronic-progressive (CPMS) were compared with matched controls on four tasks: a tachistoscopic verbal and non-verbal decision task, a dichotic listening test, cross tactile finger localization and motor tapping. No overall impairment was seen. The dichotic listening and lexical decision tasks were the most sensitive to MS. In addition, CPMS patients' IT was more impaired and was related to the severity of neurological impairment. The different sizes of the callosal fibers, which determine their vulnerability, may explain the heterogeneity of transfer through the Corpus Callosum. Therefore, evaluation of IT may be of value as an index of evolution in MS.

scholarly journals IMG-18. ASSESSMENT OF SUSPECTED DISEASE PROGRESSION USING MULTIPARAMETRIC 18F-CHOLINE PET/MRI IN CHILDHOOD AND TEENAGE-YOUNG ADULT GLIOMAS

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii358-iii358
Author(s):  
Valentina Ferrazzoli ◽  
Ananth Shankar ◽  
Julia Cockle ◽  
Christine Tang ◽  
Ahmed Al-khayfawee ◽  
...  
Keyword(s):  
Disease Progression ◽  
Posterior Fossa ◽  
Cerebral Blood Volume ◽  
Posterior Fossa Tumour ◽  
Recurrent Tumour ◽  
Normal Appearing White Matter ◽  
Conventional Mri ◽  
Apparent Diffusion ◽  
Teenagers And Young Adults

Abstract OBJECTIVES Evaluation of post-treatment glioma burden remains a significant challenge in children, teenagers and young adults (TYA). The aim of this study was to evaluate the utility of ChoPET/MRI for evaluation of suspected disease progression in childhood and TYA gliomas. METHODS 27 patients (mean age 14 years, range 6–21 years) with suspected glioma disease progression were evaluated with ChoPET/MRI (n=59). Relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC) and maximum standardised uptake values (SUVmax) in enhancing (enh) and non-enhancing (ne) tumour and normal-appearing white matter (wm) were calculated (rCBVenh, rCBVne, rCBVwm, ADCenh, ADCne, ADCwm, SUVenh, SUVne, SUVwm). 2 blinded radiologists scored tumour probability (1 = unlikely; 5 = definitely). Sensitivity and specificity calculated with gold standard histopathology or clinical follow-up. RESULTS Accuracy for the detection of residual/recurrent tumour on conventional MRI was 96.3% (91.7% ≤14 years, 100% ≥15 years) and ChoPET was 73.1% (66.7% ≤14 years, 80.0% ≥15 years). Lack of agreement was observed in 9/27 patients, with ChoPET superior to MRI in 1 case of a posterior fossa tumour. Tumour component analysis demonstrated significantly higher SUVenh and SUVne than SUVwm (SUVenh: p<0.001; SUVne: p=0.004, equivalent to results were observed for ADV and rCBV (ADCenh, ADCne: p<0.001 vs ADCwm; rCBVenh, rCBVne: p<0.001 vs rCBVwm). CONCLUSIONS MRI is more sensitive than ChoPET in the evaluation of suspected disease progression in TYA gliomas. However, quanititative ChoPET is able to detect enhancing and non-enhancing tumour and may be helpful in evaluating posterior fossa disease where MRI is equivocal.

scholarly journals The Constantly Evolving Role of Medical Image Processing in Oncology: From Traditional Medical Image Processing to Imaging Biomarkers and Radiomics

2021 ◽  
Vol 7 (8) ◽  
pp. 124
Author(s):  
Kostas Marias
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Biomarker Discovery ◽  
Medical Image Processing ◽  
Machine Learning Techniques ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Precision Oncology

The role of medical image computing in oncology is growing stronger, not least due to the unprecedented advancement of computational AI techniques, providing a technological bridge between radiology and oncology, which could significantly accelerate the advancement of precision medicine throughout the cancer care continuum. Medical image processing has been an active field of research for more than three decades, focusing initially on traditional image analysis tasks such as registration segmentation, fusion, and contrast optimization. However, with the advancement of model-based medical image processing, the field of imaging biomarker discovery has focused on transforming functional imaging data into meaningful biomarkers that are able to provide insight into a tumor’s pathophysiology. More recently, the advancement of high-performance computing, in conjunction with the availability of large medical imaging datasets, has enabled the deployment of sophisticated machine learning techniques in the context of radiomics and deep learning modeling. This paper reviews and discusses the evolving role of image analysis and processing through the lens of the abovementioned developments, which hold promise for accelerating precision oncology, in the sense of improved diagnosis, prognosis, and treatment planning of cancer.

Importance of incorporating quantitative imaging biomarker technical performance characteristics when estimating treatment effects

2021 ◽  
pp. 174077452098193
Author(s):  
Nancy A Obuchowski ◽  
Erick M Remer ◽  
Ken Sakaie ◽  
Erika Schneider ◽  
Robert J Fox ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Confidence Intervals ◽  
Treatment Effect ◽  
Quantitative Imaging ◽  
Imaging Biomarker ◽  
Imaging Biomarkers ◽  
Measurement Bias ◽  
Technical Performance ◽  
Quantitative Imaging Biomarker ◽  
Performance Validation

Background/aims Quantitative imaging biomarkers have the potential to detect change in disease early and noninvasively, providing information about the diagnosis and prognosis of a patient, aiding in monitoring disease, and informing when therapy is effective. In clinical trials testing new therapies, there has been a tendency to ignore the variability and bias in quantitative imaging biomarker measurements. Unfortunately, this can lead to underpowered studies and incorrect estimates of the treatment effect. We illustrate the problem when non-constant measurement bias is ignored and show how treatment effect estimates can be corrected. Methods Monte Carlo simulation was used to assess the coverage of 95% confidence intervals for the treatment effect when non-constant bias is ignored versus when the bias is corrected for. Three examples are presented to illustrate the methods: doubling times of lung nodules, rates of change in brain atrophy in progressive multiple sclerosis clinical trials, and changes in proton-density fat fraction in trials for patients with nonalcoholic fatty liver disease. Results Incorrectly assuming that the measurement bias is constant leads to 95% confidence intervals for the treatment effect with reduced coverage (<95%); the coverage is especially reduced when the quantitative imaging biomarker measurements have good precision and/or there is a large treatment effect. Estimates of the measurement bias from technical performance validation studies can be used to correct the confidence intervals for the treatment effect. Conclusion Technical performance validation studies of quantitative imaging biomarkers are needed to supplement clinical trial data to provide unbiased estimates of the treatment effect.

Characterization of multiple sclerosis neuroinflammation and neurodegeneration with relaxation and diffusion basis spectrum imaging

2021 ◽  
pp. 135245852110233
Author(s):  
Irene M Vavasour ◽  
Peng Sun ◽  
Carina Graf ◽  
Jackie T Yik ◽  
Shannon H Kolind ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Specific Information ◽  
Axonal Loss ◽  
Normal Appearing White Matter ◽  
Relapsing Remitting ◽  
Tissue Changes ◽  
Spectrum Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
And Diffusion

Background: Advanced magnetic resonance imaging (MRI) methods can provide more specific information about various microstructural tissue changes in multiple sclerosis (MS) brain. Quantitative measurement of T1 and T2 relaxation, and diffusion basis spectrum imaging (DBSI) yield metrics related to the pathology of neuroinflammation and neurodegeneration that occurs across the spectrum of MS. Objective: To use relaxation and DBSI MRI metrics to describe measures of neuroinflammation, myelin and axons in different MS subtypes. Methods: 103 participants (20 clinically isolated syndrome (CIS), 33 relapsing-remitting MS (RRMS), 30 secondary progressive MS and 20 primary progressive MS) underwent quantitative T1, T2, DBSI and conventional 3T MRI. Whole brain, normal-appearing white matter, lesion and corpus callosum MRI metrics were compared across MS subtypes. Results: A gradation of MRI metric values was seen from CIS to RRMS to progressive MS. RRMS demonstrated large oedema-related differences, while progressive MS had the most extensive abnormalities in myelin and axonal measures. Conclusion: Relaxation and DBSI-derived MRI measures show differences between MS subtypes related to the severity and composition of underlying tissue damage. RRMS showed oedema, demyelination and axonal loss compared with CIS. Progressive MS had even more evidence of increased oedema, demyelination and axonal loss compared with CIS and RRMS.

scholarly journals Digital Progression Biomarkers as Novel Endpoints in Clinical Trials: A Multistakeholder Perspective

2021 ◽  
pp. 1-7
Author(s):  
Diane Stephenson ◽  
Reham Badawy ◽  
Soania Mathur ◽  
Maria Tome ◽  
Lynn Rochester
Keyword(s):  
Disease Progression ◽  
Digital Health ◽  
Low Cost ◽  
Unmet Need ◽  
Disease Status ◽  
Standard Test ◽  
Imaging Biomarkers ◽  
Health Technologies ◽  
Therapeutic Development ◽  
The Rich

The burden of Parkinson’s disease (PD) continues to grow at an unsustainable pace particularly given that it now represents the fastest growing brain disease. Despite seminal discoveries in genetics and pathogenesis, people living with PD oftentimes wait years to obtain an accurate diagnosis and have no way to know their own prognostic fate once they do learn they have the disease. Currently, there is no objective biomarker to measure the onset, progression, and severity of PD along the disease continuum. Without such tools, the effectiveness of any given treatment, experimental or conventional cannot be measured. Such tools are urgently needed now more than ever given the rich number of new candidate therapies in the pipeline. Over the last decade, millions of dollars have been directed to identify biomarkers to inform progression of PD typically using molecular, fluid or imaging modalities). These efforts have produced novel insights in our understanding of PD including mechanistic targets, disease subtypes and imaging biomarkers. While we have learned a lot along the way, implementation of robust disease progression biomarkers as tools for quantifying changes in disease status or severity remains elusive. Biomarkers have improved health outcomes and led to accelerated drug approvals in key areas of unmet need such as oncology. Quantitative biomarker measures such as HbA1c a standard test for the monitoring of diabetes has impacted patient care and management, both for the healthcare professionals and the patient community. Such advances accelerate opportunities for early intervention including prevention of disease in high-risk individuals. In PD, progression markers are needed at all stages of the disease in order to catalyze drug development—this allows interventions aimed to halt or slow disease progression, very early, but also facilitates symptomatic treatments at moderate stages of the disease. Recently, attention has turned to the role of digital health technologies to complement the traditional modalities as they are relatively low cost, objective and scalable. Success in this endeavor would be transformative for clinical research and therapeutic development. Consequently, significant investment has led to a number of collaborative efforts to identify and validate suitable digital biomarkers of disease progression.

Spatiotemporal distribution of white matter lesions in relapsing–remitting and secondary progressive multiple sclerosis

2012 ◽  
Vol 18 (11) ◽  
pp. 1577-1584 ◽  
Author(s):  
Lukas Filli ◽  
Louis Hofstetter ◽  
Pascal Kuster ◽  
Stefan Traud ◽  
Nicole Mueller-Lenke ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Disease Progression ◽  
Spatiotemporal Distribution ◽  
Centrum Semiovale ◽  
Cross Sectional ◽  
Secondary Progressive ◽  
Lateral Ventricles ◽  
Relapsing Remitting ◽  
T1 And T2

Background: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. MS lesions show a typical distribution pattern and primarily affect the white matter (WM) in the periventricular zone and in the centrum semiovale. Objective: To track lesion development during disease progression, we compared the spatiotemporal distribution patterns of lesions in relapsing–remitting MS (RRMS) and secondary progressive MS (SPMS). Methods: We used T1 and T2 weighted MR images of 209 RRMS and 62 SPMS patients acquired on two different 1.5 Tesla MR scanners in two clinical centers followed up for 25 (± 1.7) months. Both cross-sectional and longitudinal differences in lesion distribution between RRMS and SPMS patients were analyzed with lesion probability maps (LPMs) and permutation-based inference. Results: MS lesions clustered around the lateral ventricles and in the centrum semiovale. Cross-sectionally, compared to RRMS patients, the SPMS patients showed a significantly higher regional probability of T1 hypointense lesions ( p≤0.03) in the callosal body, the corticospinal tract, and other tracts adjacent to the lateral ventricles, but not of T2 lesions (peak probabilities were RRMS: T1 9%, T2 18%; SPMS: T1 21%, T2 27%). No longitudinal changes of regional T1 and T2 lesion volumes between baseline and follow-up scan were found. Conclusion: The results suggest a particular vulnerability to neurodegeneration during disease progression in a number of WM tracts.

Sign in / Sign up

Export Citation Format

Share Document