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.
In suspected and diagnosed rheumatoid arthritis (RA), magnetic resonance imaging (MRI) allows detection of all relevant pathologies, such as synovitis, tenosynovitis, bone marrow edema (osteitis), bone erosion and cartilage damage. MRI is more sensitive than clinical examination for monitoring disease activity (i.e., inflammation) and more sensitive than conventional radiography and ultrasonography for monitoring joint destruction. In suspected RA, MRI bone marrow edema predicts development of RA, and in early RA patients, it predicts subsequent structural damage progression. CT is the standard reference imaging modality for visualizing bone damage, including bone erosions in RA, but lacks sensitivity for soft-tissue changes, including synovitis and tenosynovitis. CT has a minimal role in RA clinical trials and practice, except in selected patients where MRI is contraindicated or not available or if crystal arthritis such as gout or pseudo-gout is suspected. MRI has documented utility in diagnosis, monitoring and prognostication of patients with RA and is increasingly used for these purposes in clinical practice and particularly clinical trials.
Copyright © 2017 © 2017, Shaikh et al.
Cureus. 2017 Jan;9(1) doi: 10.7759/cureus.968
Chronic lymphocytic leukemia (CLL) is a low-grade B-cell proliferative disease with a generally indolent course. In a few cases, it undergoes transformation and becomes a more aggressive malignancy, such as diffuse large B-cell lymphoma (DLBCL). This process, which is called Richter transformation (RT), is often detected too late and is associated with a poor prognosis. There are multiple molecular diagnostic approaches to detect RT in preexisting CLL. Metabolic imaging using 18-fluorine fluorodeoxyglucose positron emission tomography–computed tomography (18F-FDG PET/CT) can be a very useful tool for early detection of RT and which can hence allow for timely intervention, thereby improving the patient’s chances of survival.
To develop a registration framework for correlating positron emission tomography/computed tomography (PET/CT) images with multiparametric MRI (mpMRI) and histology of the prostate, thereby enabling voxel-wise analysis of imaging parameters.
PATIENTS AND METHODS:
In this prospective proof-of-concept study, nine patients scheduled for radical prostatectomy underwent mpMRI and PET/CT imaging prior to surgery. One had PET imaging using 18 F-fluoromethylcholine (FCH), five using 68 Ga-labelled prostate-specific membrane antigen (PSMA)-HBED-CC (PMSA-11) and three using a trial 68 Ga-labelled THP-PSMA tracer. PET/CT data was co-registered with mpMRI via the CT scan and an in vivo 3D T2w MRI, and then co-registered with ground truth histology data using ex vivo MRI of the prostate specimen. Maximum and mean standardised uptake values (SUVmax and SUVmean) were extracted from PET data using tumour annotations from histology, and Kolmogorov-Smirnov tests were carried out to compare between tumour and benign voxel values. Correlation analysis was performed between mpMRI and PET SUV tumour voxels using Pearson’s correlation coefficient and R squared statistics.
PET/CT data from all nine patients were successfully registered with mpMRI and histology data. SUVmax and SUVmean ranged from 2.21 to 12.11 and 1.08 to 4.21, respectively. All patients showed the PET SUV values in benign and tumour voxels were from statistically different distributions. Correlation analysis showed no consistent trend between the T2w or ADC values and PET SUV. However, parameters from DCE MRI including the maximum enhancement (ME), volume transfer constant Ktrans and the initial area under the contrast agent concentration curve (iAUGC60) showed consistent positive correlations with PET SUV. Furthermore, R2* values from BOLD MRI showed consistent negative correlations with PET SUV voxel values.
We have developed a novel framework for registering and correlating PET/CT data at a voxel-level with mpMRI and histology. Despite registration uncertainties, perfusion and oxygenation parameters from DCE MRI and BOLD imaging showed correlations with PET SUV. Further analysis will be performed on a larger patient cohort to quantify these proof-of-concept findings. Improved understanding of the correlation between mpMRI and PET will provide supportive information for focal therapy planning of the prostate.