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


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.
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.
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).
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.

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.

Augmented Versus Artificial Intelligence for Stratification of Patients with Myositis

With interest we read the recent article by Pinal-Fernandez and Mammen,1 which comments on the paper by Spielmann et al2 and to a lesser extent on the contribution by Mariampillai et al3 4 and raises concerns about the artificial intelligence (AI)-driven approach used to define subgroups of patients with idiopathic inflammatory myopathy (IIM).

To illustrate this, Pinal-Fernandez and Mammen constructed a library of 1000 observations and selected the four variables using a multivariate normal distribution, thus finding a similar clustering as in the original paper by Spielmann et al.2 We share some of the concerns about unsupervised learning techniques raised by Pinal-Fernandez and Mammen.1 In this letter, we would like to highlight several aspects related to AI-driven methodologies.

Machine learning (ML) is a subset of AI that enables a computer to make decisions based on the large dataset. When applied to clustering, it will always give an ‘optimal’ solution for the number of clusters ‘present’ in a dataset. However, it is up to the human user’s discretion to determine whether those clusters exist. An ML algorithm determines a number of clusters by separating the datasets into the subgroups through a process of optimising (1) separation between each cluster to its greatest and (2) ensuring that within a cluster, the distance to the cluster centre for each point is the smallest. Such an algorithm is essentially trying to identify a number of optimal clusters that allow each cluster to be distinct from the others. The goal is to have tight individual clusters that are very distinguishable from the others. In any dataset, the algorithms will present an optimal solution to those or similar criteria, but it does not always mean those clusters are truly significant or meaningful.

Visualising the clusters using dimensionality reduction techniques such as principal component analysis or t-distributed stochastic neighbour embedding is vital for this process, in addition to more quantitative methods such as comparing intracluster variation, intercluster variation and silhouette scoring. That is why researchers using ML should ideally be ‘bilingual’ and understand both the mathematics and algorithms, as well the science and clinical meaning behind the results.

To conclude, we emphasise that, no doubt, ML has the potential to improve the stratification of patients with IIM if certain concepts of data science are followed as also pointed out by a task force of the European League Against Rheumatism for big data and AI.5 ML relies on large, standardised and curated datasets that require large patient cohorts. Due to the rarity of IIM, larger patient cohorts (such as the MyoNet/EuroMyositis)6 are required to generate quality data. Once larger and curated datasets are available, the ML approach is a powerful alternative to human judgement and can improve future classification criteria for IIM.4 7 8Today, we argue for the use of ML alongside expert decision, thus relying on augmented judgement when making the final decision on patient stratification especially when building AI-based models. Augmented intelligence has the potential for improved patient stratification in IIM.

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.

The Role of Advanced MRI in the Development of Treat-to-Target Therapeutic Strategies, Patient Stratification and Phenotyping in Rheumatoid Arthritis

In this commentary we discuss the potential of advanced imaging, particularly Dynamic Contrast Enhanced (DCE) magnetic resonance imaging (MRI) for the objective assessment of disease progression in rheumatoid arthritis (RA). We emphasise the potential DCE-MRI in advancing the field and exploring new areas of research and development in RA. We believe that different grades of bone marrow edema (BME) and synovitis in RA can be examined and monitored in a more sensitive manner with DCE-MRI. Future treatments for RA will be significantly improved by enhanced imaging of BMEs and synovitis. DCE-MRI will also facilitate enhanced stratification and phenotyping of patients enrolled in clinical trials.

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 May 2019; 37(15)_suppl DOI: 10.1200/JCO.2019.37.15_suppl.e13559


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.
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.
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.
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.

Development of a Multi-Modality Imaging Approach to evaluate Lupus Nephritis and initial results.

© Author(s) (or their employer(s)) 2019. Published by BMJ.
Annals of the Rheumatic Diseases. 2019 June;78(2)


Lupus nephritis (LN) remains a significant cause of morbidity and mortality in subjects with Systemic Lupus Erythematosus (SLE). The gold standard for evaluation of LN remains the kidney biopsy, whereas renal function is usually evaluated by eGFR and urinary protein:creatinine ratio. More effective and sensitive methodology is needed to assess LN and also the response to treatment. Functional imaging of the kidney using quantitative techniques has great potential, as it can assess kidney function and pathologic changes non-invasively by evaluating perfusion, oxygenation, cellular density and fibrosis.
To develop a multi-modality imaging approach for the evaluation of the spectrum of pathologic changes in LN.


In this multi-center study, subjects who were having a standard of care renal biopsy for LN were asked to participate in the imaging evaluation. Local Institutional Review Board approval was obtained, and subjects signed an Informed Consent Form. Dynamic contrast enhanced MRI (DCE-MRI) was employed to detect changes in vascularization and perfusion, Diffusion Weighted Imaging (DWI) to assess interstitial diffusion, T2*Map/BOLD – the tissue oxygenation and T1rho to evaluate fibrosis. The imaging scores will be compared to renal biopsy, including ISN/RPS classification of LN, activity index and chronicity index.

Five patients have been evaluated to date and their imaging data assessed for quality. The initial results have demonstrated the feasibility of acquiring multi-modality imaging data, including dynamic imaging sequences, in the multi-center trial setting. Figure 1 illustrates scans from a representative patient. This study will determine whether multi-modality imaging could become an effective, non-invasive tool to assess renal function and pathology in LN.
The initial assessment of 5 LN subjects has established the feasibility of multi-modality imaging as a tool to evaluate LN in a multi-center study. By assessing functional and structural MRI outcomes and correlating them to clinical data, this study will provide essential preliminary evidence on the value of multi-modality imaging in diagnosis and evaluating the response to treatment of LN patients.

Impact of a Magnetic Resonance Imaging-Guided Treat-to-Target Strategy on Disease Activity and Progression in Patients with Rheumatoid Arthritis (The IMAGINE-RA Trial): Study Protocol for a Randomized Controlled Trial.

Copyright © Author(s) (or their employer(s)) 2015.
Trials. 2015 Apr;7(178)_suppl doi: 10.1186/s13063-015-0693-2
Trial registration: identifier: NCT01656278 (5 July 2012)


Rheumatoid arthritis (RA) is a chronic, progressive joint disease, which frequently leads to irreversible joint deformity and severe functional impairment. Although patients are treated according to existing guidelines and reach clinical remission, erosive progression still occurs. This demonstrates that additional methods for prognostication and monitoring of the disease activity are needed. Bone marrow edema (BME) detected by magnetic resonance imaging (MRI) has proved to be an independent predictor of subsequent radiographic progression. Guiding the treatment based on the presence/absence of BME may therefore be clinically beneficial. We present the design of a randomized controlled trial (RCT) aiming to evaluate whether an MRI-guided treatment strategy compared to a conventional treatment strategy in anti-CCP-positive erosive RA is better to prevent progression of erosive joint damage and increase the remission rate in patients with low disease activity or clinical remission.

The study is a non-blinded, multicenter, 2-year RCT with a parallel group design. Two hundred anti-CCP-positive, erosive RA patients characterized by low disease activity or remission, no clinically swollen joints and treatment with synthetic disease-modifying antirheumatic drugs (DMARDs) will be included. Patients will be randomized to either a treatment strategy based on conventional laboratory and clinical examinations (control group) or a treatment strategy based on conventional laboratory and clinical examinations as well as MRI (intervention group). Treatment is intensified according to a predefined treatment algorithm in case of inflammation defined as a disease activity score (DAS28) >3.2 and at least one clinically swollen joint (control and intervention groups) and/or MRI-detected BME (intervention group only). The primary outcome measures are DAS28 remission (DAS28 < 2.6) and radiographic progression (Sharp/vdHeijde score).

The perspectives, strengths and weaknesses of this study are discussed.

Osteoarthritis Phenotypes and Novel Therapeutic Targets.

Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.
Biochemical Pharmacology. 2019 Jul;37 doi: 10.1016/j.bcp.2019.02.037. Epub 2019 Mar 1.


The success of disease-modifying osteoarthritis drug (DMOAD) development is still elusive. While there have been successes in preclinical and early clinical studies, phase 3 clinical trials have failed so far and there is still no approved, widely available DMOAD on the market. The latest research suggests that, among other causes, poor trial outcomes might be explained by the fact that osteoarthritis (OA) is a heterogeneous disease with distinct phenotypes. OA trials might be more successful if they would address and target a specific phenotype. The increasing availability of advanced techniques to detect particular OA characteristics expands the possibilities to distinguish between such potential OA phenotypes. Magnetic resonance imaging is among the key imaging techniques to stratify and monitor patients with changes in bone, cartilage and inflammation. Biochemical markers have mainly used as secondary parameters and could further delineate phenotypes. Moreover, post-hoc analyses of trial data have suggested the existence of distinct pain phenotypes and their relevance in the design of clinical trials. Although ongoing work in the field supports the concept of OA heterogeneity, this has not yet resulted in more effective treatment options. This paper reviews the current knowledge about potential OA phenotypes and suggests that combining patient clinical data, quantitative imaging, biochemical markers and utilizing data-driven approaches in patient selection and efficacy assessment will allow for more successful development of effective DMOADs.