In this prospective study, dual-energy CT and MRI had a similarly high sensitivity and specificity in helping detect radiographically negative wrist fractures. Dual-energy CT had a high sensitivity and a moderate specificity in the detection of bone marrow edema of the wrist. Dual-energy CT had high sensitivity and specificity in depicting fractures of the wrist in patients with suspected wrist fractures and negative findings on radiographs.
IAG, a leading medical imaging company, will work closely with CNS during the Berubicin clinical trials to provide critical imaging services, its proprietary platform DYNAMIKA and imaging data analysis. IAG has deep expertise in partnering with biotech, and specifically oncology companies, to provide a centralized reading and analysis of patient responses in real time. IAG’s scientific and clinical imaging expertise in the field of glioblastoma multiforme (GBM) will be coupled with IAG’s proprietary AI and quantitative image-based assessments to allow CNS to review efficacy assessments, objective responses, and to thoroughly explore the advanced treatment manifestations. GBM therapies often lead to pseudo-progression, a local tissue reaction resulting from immune cell infiltration, inflammation, tumor necrosis and oedema which are often misinterpreted as tumor growth on traditional MRIs. Pseudo-progression is difficult to distinguish from disease progression using routine clinical MRI assessments. IAG and CNS will be utilizing IAG’s advanced Artificial Intelligence (AI)-driven methodologies that provide reliable early efficacy readouts.
“Adding IAG was a key step in preparation for the recently developed clinical trials in Berubicin,” commented John Climaco, CEO of CNS Pharmaceuticals. “IAG has an exemplary track record of partnering closely with companies in the biotech space to provide critical analysis of both efficacy and patient response, which we believe will be pivotal in advancing our Berubicin clinical trials. Furthermore, this was yet another key milestone achieved in our trial preparations as we continue to take all of the necessary steps to ensure a successful and timely launch of our Phase II trials. We look forward to leveraging IAG’s extensive expertise, as we plan to initiate our Phase II clinical trial of Berubicin in adults early next year.”
“We are excited to partner with CNS and bring our expertise to support the optimal trial design, efficient imaging data management and review. Use of the state-of-the-art and IAG’s AI driven methodologies for imaging data review will allow us to comprehensively explore Berubicin’s efficacy and build significant scientific evidence, while reducing the development costs, timelines and uncertainties,” commented Dr. Olga Kubassova, IAG’s CEO and scientific founder.
“Advanced imaging methods and computer aided image analysis is the key to successfully interpret treatment related changes in GBM and identify responders early,” stated Dr. Diana Dupont-Roettger, Chief Scientific Alliance Officer at IAG. “We are excited to partner with CNS Pharmaceuticals in the development of Berubicin.”
About CNS Pharmaceuticals, Inc.
CNS Pharmaceuticals is developing novel treatments for primary and metastatic cancers of the brain and central nervous system. Its lead drug candidate, Berubicin, is proposed for the treatment of glioblastoma multiforme (GBM), an aggressive and incurable form of brain cancer. CNS holds a worldwide exclusive license to the Berubicin chemical compound and has acquired all data and know-how from Reata Pharmaceuticals, Inc. related to a completed Phase 1 clinical trial with Berubicin in malignant brain tumors, which Reata conducted in 2006. In this trial, 44% of patients experienced a statistically significant improvement in clinical benefit. This 44% disease control rate was based on 11 patients (out of 25 evaluable patients) with stable disease, plus responders. One patient experienced a durable complete response and remains cancer-free as of February 20, 2020. These Phase 1 results represent a limited patient sample size and, while promising, are not a guarantee that similar results will be achieved in subsequent trials. By the end of 2020, CNS expects to commence a Phase 2 clinical trial of Berubicin for the treatment of GBM in the U.S., while a sub-licensee partner undertakes a Phase 2 trial in adults and a first-ever Phase 1 trial in pediatric GBM patients in Poland. Its second drug candidate, WP1244, is a novel DNA binding agent that has shown in preclinical studies that it is 500 times more potent than the chemotherapeutic agent daunorubicin in inhibiting tumor cell proliferation. https://cnspharma.com/
Image Analysis Group (IAG) is a unique clinical development partner to life sciences companies. IAG broadly leverages its proprietary image analysis methodologies, power of our cloud platform DYNAMIKA, years of experience in AI and Machine Learning as well as bespoke co-development business models to ensure higher probability for promising therapeutics to reach the patients. Our independent Bio-Partnering division fuses risk-sharing business models and agile culture to accelerate novel drug development.
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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.
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.
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.
IAG & UCL poster for the 2019 ASCO Annual Meeting
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.
© 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.