Predicting High-Grade Glioma Response to Chemoradiation via MRI-Calibrated

https://openworks.mdanderson.org/sumexp21/1085/thumbnail.jp

Emerging Techniques in Breast MRI

As indicated throughout this chapter, there is a constant effort to move to more sensitive, specific, and quantitative methods for characterizing breast tissue via magnetic resonance imaging (MRI). In the present chapter, we focus on six emerging techniques that seek to quantitatively interrogate the physiological and biochemical properties of the breast. At the physiological scale, we present an overview of ultrafast dynamic contrast-enhanced MRI and magnetic resonance elastography which provide remarkable insights into the vascular and mechanical properties of tissue, respectively. Moving to the biochemical scale, magnetization transfer, chemical exchange saturation transfer, and spectroscopy (both “conventional” and hyperpolarized) methods all provide unique, noninvasive, insights into tumor metabolism. Given the breadth and depth of information that can be obtained in a single MRI session, methods of data synthesis and interpretation must also be developed. Thus, we conclude the chapter with an introduction to two very different, though complementary, methods of data analysis: (1) radiomics and habitat imaging, and (2) mechanism-based mathematical modeling

On the Effect of DCE MRI Slice Thickness and Noise on Estimated Pharmacokinetic Biomarkers – A Simulation Study

Simulation of a dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) multiple sclerosis brain dataset is described. The simulated images in the implemented version have 1×1×1mm3 voxel resolution and arbitrary temporal resolution. Addition of noise and simulation of thick-slice imaging is also possible. Contrast agent (Gd-DTPA) passage through tissues is modelled using the extended Tofts-Kety model. Image intensities are calculated using signal equations of the spoiled gradient echo sequence that is typically used for DCE imaging. We then use the simulated DCE images to study the impact of slice thickness and noise on the estimation of both semi- and fully-quantitative pharmacokinetic features. We show that high spatial resolution images allow significantly more accurate modelling than interpolated low resolution DCE images.acceptedVersio

Towards real-time topical detection and characterization of FDG dose infiltration prior to PET imaging

To dynamically detect and characterize 18F-fluorodeoxyglucose (FDG) dose infiltrations and evaluate their effects on positron emission tomography (PET) standardized uptake values (SUV) at the injection site and in control tissue

Sensitivity of MRI Tumor Biomarkers to VEGFR Inhibitor Therapy in an Orthotopic Mouse Glioma Model

MRI biomarkers of tumor edema, vascular permeability, blood volume, and average vessel caliber are increasingly being employed to assess the efficacy of tumor therapies. However, the dependence of these biomarkers on a number of physiological factors can compromise their sensitivity and complicate the assessment of therapeutic efficacy. Here we examine the response of these MRI tumor biomarkers to cediranib, a potent vascular endothelial growth factor receptor (VEGFR) inhibitor, in an orthotopic mouse glioma model. A significant increase in the tumor volume and relative vessel caliber index (rVCI) and a slight decrease in the water apparent diffusion coefficient (ADC) were observed for both control and cediranib treated animals. This contrasts with a clinical study that observed a significant decrease in tumor rVCI, ADC and volume with cediranib therapy. While the lack of a difference between control and cediranib treated animals in these biomarker responses might suggest that cediranib has no therapeutic benefit, cediranib treated mice had a significantly increased survival. The increased survival benefit of cediranib treated animals is consistent with the significant decrease observed for cediranib treated animals in the relative cerebral blood volume (rCBV), relative microvascular blood volume (rMBV), transverse relaxation time (T2), blood vessel permeability (Ktrans), and extravascular-extracellular space (νe). The differential response of pre-clinical and clinical tumors to cediranib therapy, along with the lack of a positive response for some biomarkers, indicates the importance of evaluating the whole spectrum of different tumor biomarkers to properly assess the therapeutic response and identify and interpret the therapy-induced changes in the tumor physiology

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT0104237