Automated Lugano Metabolic Response Assessment in 18F-Fluorodeoxyglucose-Avid Non-Hodgkin Lymphoma With Deep Learning on 18F-Fluorodeoxyglucose-Positron Emission Tomography.

PURPOSE: Artificial intelligence can reduce the time used by physicians on radiological assessments. For 18F-fluorodeoxyglucose-avid lymphomas, obtaining complete metabolic response (CMR) by end of treatment is prognostic. METHODS: Here, we present a deep learning-based algorithm for fully automated treatment response assessments according to the Lugano 2014 classification. The proposed four-stage method, trained on a multicountry clinical trial (ClinicalTrials.gov identifier: NCT01287741) and tested in three independent multicenter and multicountry test sets on different non-Hodgkin lymphoma subtypes and different lines of treatment (ClinicalTrials.gov identifiers: NCT02257567, NCT02500407, 20% holdout in ClinicalTrials.gov identifier: NCT01287741), outputs the detected lesions at baseline and follow-up to enable focused radiologist review. RESULTS: The method's response assessment achieved high agreement with the adjudicated radiologic responses (eg, agreement for overall response assessment of 93%, 87%, and 85% in ClinicalTrials.gov identifiers: NCT01287741, NCT02500407, and NCT02257567, respectively) similar to inter-radiologist agreement and was strongly prognostic of outcomes with a trend toward higher accuracy for death risk than adjudicated radiologic responses (hazard ratio for end of treatment by-model CMR of 0.123, 0.054, and 0.205 in ClinicalTrials.gov identifiers: NCT01287741, NCT02500407, and NCT02257567, compared with, respectively, 0.226, 0.292, and 0.272 for CMR by the adjudicated responses). Furthermore, a radiologist review of the algorithm's assessments was conducted. The radiologist median review time was 1.38 minutes/assessment, and no statistically significant differences were observed in the level of agreement of the radiologist with the model's response compared with the level of agreement of the radiologist with the adjudicated responses. CONCLUSION: These results suggest that the proposed method can be incorporated into radiologic response assessment workflows in cancer imaging for significant time savings and with performance similar to trained medical experts.

Diffusion Tensor MRI is sensitive to fibrotic injury in a mouse model of oxalate induced chronic kidney disease.

Chronic kidney disease (CKD) is characterized by inflammation and fibrosis in the kidney. Renal biopsies and estimated glomerular filtration rate (eGFR) remain the standard of care, but these endpoints have limitations in detecting the stage, progression, and spatial distribution of fibrotic pathology in the kidney. MRI diffusion tensor imaging (DTI) has emerged as a promising non-invasive technology to evaluate renal fibrosis in vivo both in clinical and preclinical studies. However, these imaging studies have not systematically identified fibrosis particularly deeper in the kidney where biopsy sampling is limited, or completed an extensive analysis of whole organ histology, blood biomarkers, and gene expression to evaluate the relative strengths and weaknesses of MRI for evaluating renal fibrosis. In this study, we performed DTI in the sodium oxalate mouse model of CKD. The DTI parameters fractional anisotropy, apparent diffusion coefficient, and axial diffusivity were compared between the control and oxalate groups with region-of-interest (ROI) analysis to determine changes in the cortex and medulla. Additionally, voxel-based analysis (VBA) was implemented to systematically identify local regions of injury over the whole kidney. DTI parameters were found to be significantly different in the medulla by both ROI analysis and VBA, which also spatially matched with collagen III IHC. The DTI parameters in this medullary region exhibited moderate to strong correlations with histology, blood biomarkers, hydroxyproline and gene expression. Our results thus highlight the sensitivity of DTI to the heterogeneity of renal fibrosis and importance of whole kidney non-invasive imaging.

Weak to no correlation between quantitative high-resolution computed tomography metrics and lung function change in fibrotic diseases.

Background: Identifying systemic sclerosis (SSc) and idiopathic pulmonary fibrosis (IPF) patients at risk of more rapid forced vital capacity (FVC) decline could improve trial design. The purpose of the present study was to explore the prognostic value of quantitative high-resolution computed tomography (HRCT) metrics derived by Imbio lung texture analysis (LTA) tool in predicting FVC slope. Methods: This retrospective study used data from patients who were not treated with investigational drugs with and without background antifibrotic therapies in tocilizumab phase 3 SSc, lebrikizumab phase 2 IPF, and zinpentraxin alfa phase 2 IPF studies conducted from 2015 to 2021. Controlled HRCT axial volumetric multidetector computed tomography scans were evaluated using the Imbio LTA tool. Associations between HRCT metrics and FVC slope were assessed through the Spearman correlation coefficient and adjusted R2 in a linear regression model adjusted by demographics and baseline clinical characteristics. Results: A total of 271 SSc and IPF patients were analysed. Correlation coefficients of highest magnitude were observed in the SSc study between the extent of ground glass, normal volume, quantification of interstitial lung disease, reticular pattern, and FVC slope (-0.25, 0.28, -0.28, and -0.33, respectively), while the correlation coefficients observed in IPF studies were in general <0.2. The incremental prognostic value of the baseline HRCT metrics was marginal after adjusting baseline characteristics and was inconsistent across study arms. Conclusion: Data from the SSc and IPF studies suggested weak to no and inconsistent correlation between quantitative HRCT metrics derived by the Imbio LTA tool and FVC slope in the studied SSc and IPF population.

Multi-arm U-Net with dense input and skip connectivity for T2 lesion segmentation in clinical trials of multiple sclerosis.

T2 lesion quantification plays a crucial role in monitoring disease progression and evaluating treatment response in multiple sclerosis (MS). We developed a 3D, multi-arm U-Net for T2 lesion segmentation, which was trained on a large, multicenter clinical trial dataset of relapsing MS. We investigated its generalization to other relapsing and primary progressive MS clinical trial datasets, and to an external dataset from the MICCAI 2016 MS lesion segmentation challenge. Additionally, we assessed the model's ability to reproduce the separation of T2 lesion volumes between treatment and control arms; and the association of baseline T2 lesion volumes with clinical disability scores compared with manual lesion annotations. The trained model achieved a mean dice coefficient of ≥ 0.66 and a lesion detection sensitivity of ≥ 0.72 across the internal test datasets. On the external test dataset, the model achieved a mean dice coefficient of 0.62, which is comparable to 0.59 from the best model in the challenge, and a lesion detection sensitivity of 0.68. Lesion detection performance was reduced for smaller lesions (≤ 30 µL, 3-10 voxels). The model successfully maintained the separation of the longitudinal changes in T2 lesion volumes between the treatment and control arms. Such tools could facilitate semi-automated MS lesion quantification; and reduce rater burden in clinical trials.

Preclinical development of ZED8, an 89Zr immuno-PET reagent for monitoring tumor CD8 status in patients undergoing cancer immunotherapy.

BACKGROUND: ZED8 is a novel monovalent antibody labeled with zirconium-89 for the molecular imaging of CD8. This work describes nonclinical studies performed in part to provide rationale for and to inform expectations in the early clinical development of ZED8, such as in the studies outlined in clinical trial registry NCT04029181 [1]. METHODS: Surface plasmon resonance, X-ray crystallography, and flow cytometry were used to characterize the ZED8-CD8 binding interaction, its specificity, and its impact on T cell function. Immuno-PET with ZED8 was assessed in huCD8+ tumor-bearing mice and in non-human primates. Plasma antibody levels were measured by ELISA to determine pharmacokinetic parameters, and OLINDA 1.0 was used to estimate radiation dosimetry from image-derived biodistribution data. RESULTS: ZED8 selectively binds to human CD8α at a binding site approximately 9 Å from that of MHCI making mutual interference unlikely. The equilibrium dissociation constant (KD) is 5 nM. ZED8 binds to cynomolgus CD8 with reduced affinity (66 nM) but it has no measurable affinity for rat or mouse CD8. In a series of lymphoma xenografts, ZED8 imaging was able to identify different CD8 levels concordant with flow cytometry. In cynomolgus monkeys with tool compound 89Zr-aCD8v17, lymph nodes were conspicuous by imaging 24 h post-injection, and the pharmacokinetics suggested a flat-fixed first-in-human dose of 4 mg per subject. The whole-body effective dose for an adult human was estimated to be 0.48 mSv/MBq, comparable to existing 89Zr immuno-PET reagents. CONCLUSION: 89Zr immuno-PET with ZED8 appears to be a promising biomarker of tissue CD8 levels suitable for clinical evaluation in cancer patients eligible for immunotherapy.

Magnetic Resonance Enterography and Histology in Patients With Fibrostenotic Crohn's Disease: A Multicenter Study.

INTRODUCTION: Magnetic resonance enterography (MRE) is useful for detecting bowel strictures, whereas a number of imaging biomarkers may reflect severity of fibrosis burden in Crohn's disease (CD). This study aimed to verify the association of MRE metrics with histologic fibrosis independent of inflammation. METHODS: This prospective European multicenter study performed MRE imaging on 60 patients with CD with bowel strictures before surgical resection. Locations of 61 histological samples were annotated on MRE examinations, followed by central readings using the Chiorean score and measurement of delayed gain of enhancement (DGE), magnetization transfer ratio, T2-weighted MRI sequences (T2R), apparent diffusion coefficient (ADC), and the magnetic resonance index of activity (MaRIA). Correlations of histology and MRE metrics were assessed. Least Absolute Shrinkage and Selection Operator and receiver operator characteristic (ROC) curve analyses were used to select composite MRE scores predictive of histology and to estimate their predictive value. RESULTS: ADC and MaRIA correlated with fibrosis (R = -0.71, P < 0.0001, and 0.59, P < 0.001) and more moderately with inflammation (R = -0.35, P < 0.01, and R = 0.53, P < 0.001). Lower or no correlations of fibrosis or inflammation were found with DGE, magnetization transfer ratio, or T2R. Least Absolute Shrinkage and Selection Operator and ROC identified a composite score of MaRIA, ADC, and DGE as a very good predictor of histologic fibrosis (ROC area under the curve = 0.910). MaRIA alone was the best predictor of histologic inflammation with excellent performance in identifying active histologic inflammation (ROC area under the curve = 0.966). DISCUSSION: MRE-based scores for histologic fibrosis and inflammation may assist in the characterization of CD stenosis and enable development of fibrosis-targeted therapies and clinical treatment of stenotic patients.

Heart and bladder detection and segmentation on FDG PET/CT by deep learning.

PURPOSE: Positron emission tomography (PET)/ computed tomography (CT) has been extensively used to quantify metabolically active tumors in various oncology indications. However, FDG-PET/CT often encounters false positives in tumor detection due to 18fluorodeoxyglucose (FDG) accumulation from the heart and bladder that often exhibit similar FDG uptake as tumors. Thus, it is necessary to eliminate this source of physiological noise. Major challenges for this task include: (1) large inter-patient variability in the appearance for the heart and bladder. (2) The size and shape of bladder or heart may appear different on PET and CT. (3) Tumors can be very close or connected to the heart or bladder. APPROACH: A deep learning based approach is proposed to segment the heart and bladder on whole body PET/CT automatically. Two 3D U-Nets were developed separately to segment the heart and bladder, where each network receives the PET and CT as a multi-modal input. Data sets were obtained from retrospective clinical trials and include 575 PET/CT for heart segmentation and 538 for bladder segmentation. RESULTS: The models were evaluated on a test set from an independent trial and achieved a Dice Similarity Coefficient (DSC) of 0.96 for heart segmentation and 0.95 for bladder segmentation, Average Surface Distance (ASD) of 0.44 mm on heart and 0.90 mm on bladder. CONCLUSIONS: This methodology could be a valuable component to the FDG-PET/CT data processing chain by removing FDG physiological noise associated with heart and/or bladder accumulation prior to image analysis by manual, semi- or automated tumor analysis methods.

Deformation-based morphometry identifies deep brain structures protected by ocrelizumab.

BACKGROUND: Despite advancements in treatments for multiple sclerosis, insidious disease progression remains an area of unmet medical need, for which atrophy-based biomarkers may help better characterize the progressive biology. METHODS: We developed and applied a method of longitudinal deformation-based morphometry to provide voxel-level assessments of brain volume changes and identified brain regions that were significantly impacted by disease-modifying therapy. RESULTS: Using brain MRI data from two identically designed pivotal trials of relapsing multiple sclerosis (total N = 1483), we identified multiple deep brain regions, including the thalamus and brainstem, where volume loss over time was reduced by ocrelizumab (p < 0.05), a humanized anti-CD20 + monoclonal antibody approved for the treatment of multiple sclerosis. Additionally, identified brainstem shrinkage, as well as brain ventricle expansion, was associated with a greater risk for confirmed disability progression (p < 0.05). CONCLUSIONS: The identification of deep brain structures has a strong implication for developing new biomarkers of brain atrophy reduction to advance drug development for multiple sclerosis, which has an increasing focus on targeting the progressive biology.

Joint MRI T1 Unenhancing and Contrast-enhancing Multiple Sclerosis Lesion Segmentation with Deep Learning in OPERA Trials.

Background Deep learning-based segmentation could facilitate rapid and reproducible T1 lesion load assessments, which is crucial for disease management in multiple sclerosis (MS). T1 unenhancing and contrast-enhancing lesions in MS are those that enhance or do not enhance after administration of a gadolinium-based contrast agent at T1-weighted MRI. Purpose To develop deep learning models for automated assessment of T1 unenhancing and contrast-enhancing lesions; to investigate if joint training improved performance; to reproduce a known ocrelizumab treatment response; and to evaluate the association of baseline T1-weighted imaging metrics with clinical outcomes in relapsing MS clinical trials. Materials and Methods Joint and individual deep learning models (U-Nets) were developed retrospectively on multimodal MRI data sets from large multicenter OPERA trials of relapsing MS (August 2011 to May 2015). The joint model included cross-network connections and a combined loss function. Models were trained on OPERA I data sets with three-fold cross-validation. OPERA II data sets were the internal test set. Dice coefficients, lesion true-positive and false-positive rates, and areas under the receiver operating characteristic curve (AUCs) were used to evaluate model performance. Association of baseline imaging metrics with clinical outcomes was assessed with Cox proportional hazards models. Results A total of 796 patients (3030 visits; mean age, 37 years ± 9; 521 women) from the OPERA II trial were evaluated. The joint model achieved a mean Dice coefficient of 0.77 and 0.74, lesion true-positive rate of 0.88 and 0.86, and lesion false-positive rate of 0.04 and 0.19 for T1 contrast-enhancing and T1 unenhancing lesion segmentation, respectively. Joint training improved performance for smaller T1 contrast-enhancing lesions (≤0.06 mL; individual training AUC: 0.75; joint training AUC: 0.87; P < .001). A significant ocrelizumab treatment effect (P < .001) was seen in reducing the mean number of T1 contrast-enhancing lesions at 24, 48, and 96 weeks (manual assessment at 24 weeks: 10 lesions in 366 patients with ocrelizumab, 141 lesions in 355 patients with interferon, 93% reduction; manual assessment at 48 weeks: six lesions in 355 patients with ocrelizumab, 150 lesions in 317 patients with interferon, 96% reduction; manual assessment at 96 weeks: five lesions in 340 patients with ocrelizumab, 157 lesions in 294 patients with interferon, 97% reduction; joint model assessment at 24 weeks: 19 lesions in 365 patients with ocrelizumab, 128 lesions in 354 patients with interferon, 86% reduction; joint model assessment at 48 weeks: 14 lesions in 355 patients with ocrelizumab, 121 lesions in 317 patients with interferon, 90% reduction; joint model assessment at 96 weeks: 10 lesions in 340 patients with ocrelizumab, 144 lesions in 294 patients with interferon, 94% reduction) and the mean number of new T1 unenhancing lesions across all follow-up examinations (manual assessment: 504 lesions in 1060 visits for ocrelizumab-treated patients, 1438 lesions in 965 visits for interferon-treated patients, 68% reduction; joint model assessment: 205 lesions in 1053 visits for ocrelizumab-treated patients, 661 lesions in 957 visits for interferon-treated patients, 78% reduction). Baseline T1 unenhancing total lesion volume was associated with clinical outcomes (manual hazard ratio [HR]: 1.12, P = .02; joint model HR: 1.11, P = .03). Conclusion Joint architecture and training improved segmentation of MRI T1 contrast-enhancing multiple sclerosis lesions, and both deep learning models had sufficiently high performance to detect an ocrelizumab treatment response consistent with manual assessments. ClinicalTrials.gov: NCT01247324 and NCT01412333 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Talbott in this issue.

Tumor Segmentation and Feature Extraction from Whole-Body FDG-PET/CT Using Cascaded 2D and 3D Convolutional Neural Networks.

18F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) is commonly used in clinical practice and clinical drug development to identify and quantify metabolically active tumors. Manual or computer-assisted tumor segmentation in FDG-PET images is a common way to assess tumor burden, such approaches are both labor intensive and may suffer from high inter-reader variability. We propose an end-to-end method leveraging 2D and 3D convolutional neural networks to rapidly identify and segment tumors and to extract metabolic information in eyes to thighs (whole body) FDG-PET/CT scans. The developed architecture is computationally efficient and devised to accommodate the size of whole-body scans, the extreme imbalance between tumor burden and the volume of healthy tissue, and the heterogeneous nature of the input images. Our dataset consists of a total of 3664 eyes to thighs FDG-PET/CT scans, from multi-site clinical trials in patients with non-Hodgkin's lymphoma (NHL) and advanced non-small cell lung cancer (NSCLC). Tumors were segmented and reviewed by board-certified radiologists. We report a mean 3D Dice score of 88.6% on an NHL hold-out set of 1124 scans and a 93% sensitivity on 274 NSCLC hold-out scans. The method is a potential tool for radiologists to rapidly assess eyes to thighs FDG-avid tumor burden.

Phase Ib study of the MEK inhibitor cobimetinib (GDC-0973) in combination with the PI3K inhibitor pictilisib (GDC-0941) in patients with advanced solid tumors.

Purpose We investigated the combination of the MEK inhibitor, cobimetinib, and the pan-PI3K inhibitor, pictilisib, in an open-label, phase Ib study. Experimental Design Patients with advanced solid tumors were enrolled in 3 dose escalation schedules: (1) both agents once-daily for 21-days-on 7-days-off ("21/7"); (2) intermittent cobimetinib and 21/7 pictilisib ("intermittent"); or (3) both agents once-daily for 7-days-on 7-days-off ("7/7"). Starting doses for the 21/7, intermittent, and 7/7 schedules were 20/80, 100/130, and 40/130 mg of cobimetinib/pictilisib, respectively. Nine indication-specific expansion cohorts interrogated the recommended phase II dose and schedule. Results Of 178 enrollees (dose escalation: n = 98), 177 patients were dosed. The maximum tolerated doses for cobimetinib/pictilisib (mg) were 40/100, 125/180, and not reached, for the 21/7, intermittent, and 7/7 schedules, respectively. Six dose-limiting toxicities included grade 3 (G3) elevated lipase, G4 elevated creatine phosphokinase, and G3 events including fatigue concurrent with a serious adverse event (SAE) of diarrhea, decreased appetite, and SAEs of hypersensitivity and dehydration. Common drug-related adverse events included nausea, fatigue, vomiting, decreased appetite, dysgeusia, rash, and stomatitis. Pharmacokinetic parameters of the drugs used in combination were unaltered compared to monotherapy exposures. Confirmed partial responses were observed in patients with BRAF-mutant melanoma (n = 1) and KRAS-mutant endometrioid adenocarcinoma (n = 1). Eighteen patients remained on study ≥6 months. Biomarker data established successful blockade of MAP kinase (MAPK) and PI3K pathways. The metabolic response rate documented by FDG-PET was similar to that observed with cobimetinib monotherapy. Conclusions Cobimetinib and pictilisib combination therapy in patients with solid tumors had limited tolerability and efficacy.

Optimization of 89Zr PET Imaging for Improved Multisite Quantification and Lesion Detection Using an Anthropomorphic Phantom.

Our objective was to harmonize multicenter 89Zr PET imaging for oncology trials and to evaluate lesion detection. Methods: Seven PET scanners were evaluated using a custom chest oncology phantom with 9 spheric lesions 7-20 mm in diameter. A 4:1 signal-to-background ratio simulated a patient dose of 92.5 MBq. Various image reconstructions were evaluated. Images were assessed for lesion detection, and recovery coefficients and background signal variance were measured. Results: Two scanners failed to provide acceptable images and data. Optimal reconstruction algorithms enabling adequate lesion detection and reliable quantification across the other 5 scanners were determined without compromising the data quality. On average, 95% of the 10-mm lesions were detected, and the 7-mm lesion was visualized by only 1 scanner. Background variance was 8.6%-16%. Conclusion: We established multicenter harmonization procedures for 89Zr PET imaging in oncology, optimizing small-lesion (≥10 mm) detectability and accurate quantification.

89Zr-atezolizumab imaging as a non-invasive approach to assess clinical response to PD-L1 blockade in cancer.

Programmed cell death protein-1/ligand-1 (PD-1/PD-L1) blockade is effective in a subset of patients with several tumor types, but predicting patient benefit using approved diagnostics is inexact, as some patients with PD-L1-negative tumors also show clinical benefit1,2. Moreover, all biopsy-based tests are subject to the errors and limitations of invasive tissue collection3-11. Preclinical studies of positron-emission tomography (PET) imaging with antibodies to PD-L1 suggested that this imaging method might be an approach to selecting patients12,13. Such a technique, however, requires substantial clinical development and validation. Here we present the initial results from a first-in-human study to assess the feasibility of imaging with zirconium-89-labeled atezolizumab (anti-PD-L1), including biodistribution, and secondly test its potential to predict response to PD-L1 blockade (ClinicalTrials.gov identifiers NCT02453984 and NCT02478099). We imaged 22 patients across three tumor types before the start of atezolizumab therapy. The PET signal, a function of tracer exposure and target expression, was high in lymphoid tissues and at sites of inflammation. In tumors, uptake was generally high but heterogeneous, varying within and among lesions, patients, and tumor types. Intriguingly, clinical responses in our patients were better correlated with pretreatment PET signal than with immunohistochemistry- or RNA-sequencing-based predictive biomarkers, encouraging further development of molecular PET imaging for assessment of PD-L1 status and clinical response prediction.

Clinical translation of ferumoxytol-based vessel size imaging (VSI): Feasibility in a phase I oncology clinical trial population.

PURPOSE: To assess the feasibility of acquiring vessel size imaging (VSI) metrics using ferumoxytol injections and stock pulse sequences in a multicenter Phase I trial of a novel therapy in patients with advanced metastatic disease. METHODS: Scans were acquired before, immediately after, and 48 h after injection, at screening and after 2 weeks of treatment. ΔR2 , ΔR2*, vessel density (Q), and relative vascular volume fractions (VVF) were estimated in both normal tissue and tumor, and compared with model-derived theoretical and experimental estimates based on preclinical murine xenograft data. RESULTS: R2 and R2* relaxation rates were still significantly elevated in tumors and liver 48 h after ferumoxytol injection; liver values returned to baseline by week 2. Q was relatively insensitive to changes in ΔR2*, indicating lack of dependence on contrast agent concentration. Variability in Q was higher among human tumors compared with xenografts and was mostly driven by ΔR2 . Relative VVFs were higher in human tumors compared with xenografts, while values in muscle were similar between species. CONCLUSION: Clinical ferumoxytol-based VSI is feasible using standard MRI techniques in a multicenter study of patients with lesions outside of the brain. Ferumoxytol accumulation in the liver does not preclude measurement of VSI parameters in liver metastases. Magn Reson Med 77:814-825, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Magnetic resonance imaging-based cerebral tissue classification reveals distinct spatiotemporal patterns of changes after stroke in non-human primates.

BACKGROUND: Spatial and temporal changes in brain tissue after acute ischemic stroke are still poorly understood. Aims of this study were three-fold: (1) to determine unique temporal magnetic resonance imaging (MRI) patterns at the acute, subacute and chronic stages after stroke in macaques by combining quantitative T2 and diffusion MRI indices into MRI 'tissue signatures', (2) to evaluate temporal differences in these signatures between transient (n = 2) and permanent (n = 2) middle cerebral artery occlusion, and (3) to correlate histopathology findings in the chronic stroke period to the acute and subacute MRI derived tissue signatures. RESULTS: An improved iterative self-organizing data analysis algorithm was used to combine T2, apparent diffusion coefficient (ADC), and fractional anisotropy (FA) maps across seven successive timepoints (1, 2, 3, 24, 72, 144, 240 h) which revealed five temporal MRI signatures, that were different from the normal tissue pattern (P < 0.001). The distribution of signatures between brains with permanent and transient occlusions varied significantly between groups (P < 0.001). Qualitative comparisons with histopathology revealed that these signatures represented regions with different histopathology. Two signatures identified areas of progressive injury marked by severe necrosis and the presence of gitter cells. Another signature identified less severe but pronounced neuronal and axonal degeneration, while the other signatures depicted tissue remodeling with vascular proliferation and astrogliosis. CONCLUSION: These exploratory results demonstrate the potential of temporally and spatially combined voxel-based methods to generate tissue signatures that may correlate with distinct histopathological features. The identification of distinct ischemic MRI signatures associated with specific tissue fates may further aid in assessing and monitoring the efficacy of novel pharmaceutical treatments for stroke in a pre-clinical and clinical setting.

STatistically Assigned Response Criteria in Solid Tumors (STARCIST).

BACKGROUND: Several reproducibility studies have established good test-retest reliability of FDG-PET in various oncology settings. However, these studies are based on relatively short inter-scan periods of 1-3 days while, in contrast, response assessments based on FDG-PET in early phase drug trials are typically made over an interval of 2-3 weeks during the first treatment cycle. With focus on longer, on-treatment scan intervals, we develop a data-driven approach to calculate baseline-specific cutoff values to determine patient-level changes in glucose uptake that are unlikely to be explained by random variability. Our method takes into account the statistical nature of natural fluctuations in SUV as well as potential bias effects. METHODS: To assess variability in SUV over clinically relevant scan intervals for clinical trials, we analyzed baseline and follow-up FDG-PET scans with a median scan interval of 21 days from 53 advanced stage cancer patients enrolled in a Phase 1 trial. The 53 patients received a sub-pharmacologic drug dose and the trial data is treated as a 'test-retest' data set. A simulation-based tool is presented which takes as input baseline lesion SUVmax values, the variance of spurious changes in SUVmax between scans, the desired Type I error rate, and outputs lesion and patient based cut-off values. Bias corrections are included to account for variations in tracer uptake time. RESULTS: In the training data, changes in SUVmax follow an approximately zero-mean Gaussian distribution with constant variance across levels of the baseline measurements. Because of constant variance, the coefficient of variation is a decreasing function of the magnitude of baseline SUVmax. This finding is consistent with published results, but our data shows greater variability. Application of our method to NSCLC patients treated with erlotinib produces results distinct from those based on the EORTC criteria. Based on data presented here as well as previous repeatability studies, the proposed method has greater statistical power to detect a significant %-decrease on SUVmax compared to published criteria relying on symmetric thresholds. CONCLUSIONS: Defining patient-specific, baseline dependent cut-off values based on the (null) distribution of naturally occurring fluctuations in glucose uptake enable identification of statistically significant changes in SUVmax. For lower baseline values, the produced cutoff values are notably asymmetric with relatively large changes (e.g. >50 %) required for statistical significance. For use with prospectively defined endpoints, the developed method enables the use of one-armed trials to detect pharmacodynamic drug effects based on FDG-PET. The clinical importance of changes in SUVmax is likely to remain dependent on both tumor biology and the type of treatment.

Validation of High-Resolution Tractography Against In Vivo Tracing in the Macaque Visual Cortex.

Diffusion magnetic resonance imaging (MRI) allows for the noninvasive in vivo examination of anatomical connections in the human brain, which has an important role in understanding brain function. Validation of this technique is vital, but has proved difficult due to the lack of an adequate gold standard. In this work, the macaque visual system was used as a model as an extensive body of literature of in vivo and postmortem tracer studies has established a detailed understanding of the underlying connections. We performed probabilistic tractography on high angular resolution diffusion imaging data of 2 ex vivo, in vitro macaque brains. Comparisons were made between identified connections at different thresholds of probabilistic connection "strength," and with various tracking optimization strategies previously proposed in the literature, and known connections from the detailed visual system wiring map described by Felleman and Van Essen (1991; FVE91). On average, 74% of connections that were identified by FVE91 were reproduced by performing the most successfully optimized probabilistic diffusion MRI tractography. Further comparison with the results of a more recent tracer study ( Markov et al. 2012) suggests that the fidelity of tractography in estimating the presence or absence of interareal connections may be greater than this.

Dynamic Diffusion Magnetic Resonance Imaging of Infarct Formation and Peri-infarct Spreading Depression after Middle Cerebral Artery Occlusion (MCAO) in macacca fasicularis.

Dynamic diffusion MRI was used to visualize hyperacute stroke formation in the brain of a cynomolgus macaque. Under fluoroscopic guidance, a microcatheter was placed into the middle cerebral artery (MCA). The animal was immediately transferred to a 1.5T clinical scanner. Dynamic T2-weighted imaging during bolus injection of Oxygen-17 enriched water through the microcatheter mapped out the territory perfused by the MCA segment. Serial diffusion measurements were made using diffusion-weighted echo-planar imaging, with a temporal resolution of 15 seconds, during injection of a glue embolus into the microcatheter. The apparent diffusion coefficient declined within the lesion core. A wave of transient diffusion decline spread through peripheral uninvolved brain immediately following stroke induction. The propagation speed and pattern is consistent with spreading peri-infarct depolarizations (PID). The detection of PIDs following embolic stroke in a higher nonhuman primate brain supports the hypothesis that spreading depressions may occur following occlusive stroke in humans.

Imaging Stroke Evolution after Middle Cerebral Artery Occlusion in Non-human Primates.

This article reviews imaging approaches applied to the study of stroke in nonhuman primates. We briefly survey the various surgical and minimally invasive experimental stroke models in nonhuman primates, followed by a summary of studies using computed tomography, positron emission tomography and magnetic resonance imaging and spectroscopy to monitor stroke from the hyperacute phase (within minutes of the onset of cerebral ischemia) to the chronic phase (1 month and beyond).