Jonathan D. Plasencia

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

Background Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32). Conclusion MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma

Background Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM. Methods We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set. Results We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients). Conclusion Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Total artificial heart in the pediatric patient with biventricular heart failure.

Mechanical circulatory support emerged for the pediatric population in the late 1980s as a bridge to cardiac transplantation. The Total Artificial Heart (TAH-t) (SynCardia Systems Inc., Tuscon, AZ) has been approved for compassionate use by the Food and Drug Administration for patients with end-stage biventricular heart failure as a bridge to heart transplantation since 1985 and has had FDA approval since 2004. However, of the 1,061 patients placed on the TAH-t, only 21 (2%) were under the age 18. SynCardia Systems, Inc. recommends a minimum patient body surface area (BSA) of 1.7 m2, thus, limiting pediatric application of this device. This unique case report shares this pediatric institution’s first experience with the TAH-t. A 14-year-old male was admitted with dilated cardiomyopathy and severe biventricular heart failure. The patient rapidly decompensated, requiring extracorporeal life support. An echocardiogram revealed severe biventricular dysfunction and diffuse clot formation in the left ventricle and outflow tract. The decision was made to transition to biventricular assist device. The biventricular failure and clot formation helped guide the team to the TAH-t, in spite of a BSA (1.5 m2) below the recommendation of 1.7m2. A computed tomography (CT) scan of the thorax, in conjunction with a novel three-dimensional (3D) modeling system and team, assisted in determining appropriate fit. Chest CT and 3D modeling following implantation were utilized to determine all major vascular structures were unobstructed and the bronchi were open. The virtual 3D model confirmed appropriate device fit with no evidence of compression to the left pulmonary veins. The postoperative course was complicated by a left lung opacification. The left lung anomalies proved to be atelectasis and improved with aggressive recruitment maneuvers. The patient was supported for 11 days prior to transplantation. Chest CT and 3D modeling were crucial in assessing whether the device would fit, as well as postoperative complications in this smaller pediatric patient.

Structural alterations of the brainstem in migraine

Atypical brainstem modulation of pain might contribute to changes in sensory processing typical of migraine. The study objective was to investigate whether migraine is associated with brainstem structural alterations that correlate with this altered pain processing. MRI T1-weighted images of 55 migraine patients and 58 healthy controls were used to: (1) create deformable mesh models of the brainstem that allow for shape analyses; (2) calculate volumes of the midbrain, pons, medulla and the superior cerebellar peduncles; (3) interrogate correlations between regional brainstem volumes, cutaneous heat pain thresholds, and allodynia symptoms. Migraineurs had smaller midbrain volumes (healthy controls = 61.28 mm3, SD = 5.89; migraineurs = 58.80 mm3, SD = 6.64; p = 0.038), and significant (p < 0.05) inward deformations in the ventral midbrain and pons, and outward deformations in the lateral medulla and dorsolateral pons relative to healthy controls. Migraineurs had a negative correlation between ASC-12 allodynia symptom severity with midbrain volume (r = − 0.32; p = 0.019) and a positive correlation between cutaneous heat pain thresholds with medulla (r = 0.337; p = 0.012) and cerebellar peduncle volumes (r = 0.435; p = 0.001). Migraineurs with greater symptoms of allodynia have smaller midbrain volumes and migraineurs with lower heat pain thresholds have smaller medulla and cerebellar peduncles. The brainstem likely plays a role in altered sensory processing in migraine and brainstem structure might reflect severity of allodynia and hypersensitivity to pain in migraine.

Spatiotemporal Quantification of Local Drug Delivery Using MRI

Controlled release formulations for local, in vivo drug delivery are of growing interest to device manufacturers, research scientists, and clinicians; however, most research characterizing controlled release formulations occurs in vitro because the spatial and temporal distribution of drug delivery is difficult to measure in vivo. In this work, in vivo magnetic resonance imaging (MRI) of local drug delivery was performed to visualize and quantify the time resolved distribution of MRI contrast agents. Three-dimensional T 1 maps (generated from T 1-weighted images with varied T R) were processed using noise-reducing filtering. A segmented region of contrast, from a thresholded image, was converted to concentration maps using the equation 1/T 1 = 1/T 1,0 + R 1 C, where T 1,0 and T 1 are the precontrast and postcontrast T 1 map values, respectively. In this technique, a uniform estimated value for T 1,0 was used. Error estimations were performed for each step. The practical usefulness of this method was assessed using comparisons between devices located in different locations both with and without contrast. The method using a uniform T 1,0, requiring no registration of pre- and postcontrast image volumes, was compared to a method using either affine or deformation registrations.

Mathematical Analysis of Glioma Growth in a Murine Model

Five immunocompetent C57BL/6-cBrd/cBrd/Cr (albino C57BL/6) mice were injected with GL261-luc2 cells, a cell line sharing characteristics of human glioblastoma multiforme (GBM). The mice were imaged using magnetic resonance (MR) at five separate time points to characterize growth and development of the tumor. After 25 days, the final tumor volumes of the mice varied from 12 mm3 to 62 mm3, even though mice were inoculated from the same tumor cell line under carefully controlled conditions. We generated hypotheses to explore large variances in final tumor size and tested them with our simple reaction-diffusion model in both a 3-dimensional (3D) finite difference method and a 2-dimensional (2D) level set method. The parameters obtained from a best-fit procedure, designed to yield simulated tumors as close as possible to the observed ones, vary by an order of magnitude between the three mice analyzed in detail. These differences may reflect morphological and biological variability in tumor growth, as well as errors in the mathematical model, perhaps from an oversimplification of the tumor dynamics or nonidentifiability of parameters. Our results generate parameters that match other experimental in vitro and in vivo measurements. Additionally, we calculate wave speed, which matches with other rat and human measurements.

Volume curtaining: A focus+context effect for multimodal volume visualization

In surgical preparation, physicians will often utilize multimodal imaging scans to capture complementary information to improve diagnosis and to drive patient-specific treatment. These imaging scans may consist of data from magnetic resonance imaging (MR), computed tomography (CT), or other various sources. The challenge in using these different modalities is that the physician must mentally map the two modalities together during the diagnosis and planning phase. Furthermore, the different imaging modalities will be generated at various resolutions as well as slightly different orientations due to patient placement during scans. In this work, we present an interactive system for multimodal data fusion, analysis and visualization. Developed with partners from neurological clinics, this work discusses initial system requirements and physician feedback at the various stages of component development. Finally, we present a novel focus+context technique for the interactive exploration of coregistered multi-modal data.

Alternative methods for virtual heart transplant-Size matching for pediatric heart transplantation with and without donor medical images available

Listed pediatric heart transplant patients have the highest solid‐organ waitlist mortality rate. The donor‐recipient body weight (DRBW) ratio is the clinical standard for allograft size matching but may unnecessarily limit a patients donor pool. To overcome DRBW ratio limitations, two methods of performing virtual heart transplant fit assessments were developed that account for patient‐specific nuances. Method 1 uses an allograft total cardiac volume (TCV) prediction model informed by patient data wherein a matched allograft 3‐D reconstruction is selected from a virtual library for assessment. Method 2 uses donor images for a direct virtual transplant assessment.

Corrigendum to “Structural alterations of the brainstem in migraine” (Neuroimage Clin. 2017; 13: 223–227)

[This corrects the article DOI: 10.1016/j.nicl.2016.10.023.].

Virtual surgical modification for planning tetralogy of Fallot repair

Goals for treating congenital heart defects are becoming increasingly focused on the long-term, targeting solutions that last into adulthood. Although this shift has motivated the modification of many current surgical procedures, there remains a great deal of room for improvement. We present a new methodological component for tetralogy of Fallot (TOF) repair that aims to improve long-term outcomes. The current gold standard for TOF repair involves the use of echocardiography (ECHO) for measuring the pulmonary valve (PV) diameter. This is then used, along with other factors, to formulate a Z-score that drives surgical preparation. Unfortunately this process can be inaccurate and requires a mid-operative confirmation that the pressure gradient across the PV is not excessive. Ideally, surgeons prefer not to manipulate the PV as this can lead to valve insufficiency. However, an excessive pressure gradient across the valve necessitates surgical action. We propose the use of computational fluid dynamics (CFD) to improve preparation for TOF repair. In our study, pre-operative CT data were segmented and reconstructed, and a virtual surgical operation was then performed to simulate post-operative conditions. The modified anatomy was used to drive CFD simulation. The pressure gradient across the pulmonary valve was calculated to be 9.24mmHg, which is within the normal range. This finding indicates that CFD may be a viable tool for predicting post-operative pressure gradients for TOF repair. Our proposed methodology would remove the need for mid-operative measurements that can be both unreliable and detrimental to the patient.