Early detection of tumour immune-rejection using magnetic resonance imaging.
Ontology highlight
ABSTRACT:
SUBMITTER: Hu DE
PROVIDER: S-EPMC2376373 | biostudies-other | 2003 Apr
REPOSITORIES: biostudies-other
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Project description:Cancer cells differ in size from those of their host tissue and are known to change in size during the processes of cell death. A noninvasive method for monitoring cell size would be highly advantageous as a potential biomarker of malignancy and early therapeutic response. This need is particularly acute in brain tumours where biopsy is a highly invasive procedure. Here, diffusion MRI data were acquired in a GL261 glioma mouse model before and during treatment with Temozolomide. The biophysical model VERDICT (Vascular Extracellular and Restricted Diffusion for Cytometry in Tumours) was applied to the MRI data to quantify multi-compartmental parameters connected to the underlying tissue microstructure, which could potentially be useful clinical biomarkers. These parameters were compared to ADC and kurtosis diffusion models, and, measures from histology and optical projection tomography. MRI data was also acquired in patients to assess the feasibility of applying VERDICT in a range of different glioma subtypes. In the GL261 gliomas, cellular changes were detected according to the VERDICT model in advance of gross tumour volume changes as well as ADC and kurtosis models. VERDICT parameters in glioblastoma patients were most consistent with the GL261 mouse model, whilst displaying additional regions of localised tissue heterogeneity. The present VERDICT model was less appropriate for modelling more diffuse astrocytomas and oligodendrogliomas, but could be tuned to improve the representation of these tumour types. Biophysical modelling of the diffusion MRI signal permits monitoring of brain tumours without invasive intervention. VERDICT responds to microstructural changes induced by chemotherapy, is feasible within clinical scan times and could provide useful biomarkers of treatment response.
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Project description:Detecting Alzheimer's disease (AD) at an early stage brings a lot of benefits including disease management and actions to slow the progression of the disease. Here, we demonstrate that reduced creatine chemical exchange saturation transfer (CrCEST) contrast has the potential to serve as a new biomarker for early detection of AD. The results on wild type (WT) mice and two age-matched AD models, namely tauopathy (Tau) and Aβ amyloidosis (APP), indicated that CrCEST contrasts of the cortex and corpus callosum in the APP and Tau mice were significantly reduced compared to WT counterpart at an early stage (6-7 months) (p < 0.011). Two main causes of the reduced CrCEST contrast, i.e. cerebral pH and creatine concentration, were investigated. From phantom and hypercapnia experiments, CrCEST showed excellent sensitivity to pH variations. From MRS results, the creatine concentration in WT and AD mouse brain was equivalent, which suggests that the reduced CrCEST contrast was dominated by cerebral pH change involved in the progression of AD. Immunohistochemical analysis revealed that the abnormal cerebral pH in AD mice may relate to neuroinflammation, a known factor that can cause pH reduction.
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Project description:Background & aimsReinforced hepatocellular carcinoma (HCC) surveillance using magnetic resonance imaging (MRI) could increase early tumour detection but faces cost-effectiveness issues. In this study, we aimed to evaluate the cost-effectiveness of MRI for the detection of very early HCC (Barcelona Clinic Liver Cancer [BCLC] 0) in patients with an annual HCC risk >3%.MethodsFrench patients with compensated cirrhosis included in 4 multicentre prospective cohorts were considered. A scoring system was constructed to identify patients with an annual risk >3%. Using a Markov model, the economic evaluation estimated the costs and life years (LYs) gained with MRI vs. ultrasound (US) monitoring over a 20-year period. The incremental cost-effectiveness ratio (ICER) was calculated by dividing the incremental costs by the incremental LYs.ResultsAmong 2,513 patients with non-viral causes of cirrhosis (n = 840) and/or cured HCV (n = 1,489)/controlled HBV infection (n = 184), 206 cases of HCC were detected after a 37-month follow-up. When applied to training (n = 1,658) and validation (n = 855) sets, the construction of a scoring system identified 33.4% and 37.5% of patients with an annual HCC risk >3% (3-year C-Indexes 75 and 76, respectively). In patients with a 3% annual risk, the incremental LY gained with MRI was 0.4 for an additional cost of €6,134, resulting in an ICER of €15,447 per LY. Compared to US monitoring, MRI detected 5x more BCLC 0 HCC. The deterministic sensitivity analysis confirmed the impact of HCC incidence. At a willingness to pay of €50,000/LY, MRI screening had a 100% probability of being cost-effective.ConclusionsIn the era of HCV eradication/HBV control, patients with annual HCC risk >3% represent one-third of French patients with cirrhosis. MRI is cost-effective in this population and could favour early HCC detection.Lay summaryThe early identification of hepatocellular carcinoma in patients with cirrhosis is important to improve patient outcomes. Magnetic resonance imaging could increase early tumour detection but is more expensive and less accessible than ultrasound (the standard modality for surveillance). Herein, using a simple score, we identified a subgroup of patients with cirrhosis (accounting for >one-third), who were at increased risk of hepatocellular carcinoma and for whom the increased expense of magnetic resonance imaging would be justified by the potential improvement in outcomes.
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Project description:Neuroimaging is crucial for assessing mass effect in brain-injured patients. Transport to an imaging suite, however, is challenging for critically ill patients. We evaluated the use of a low magnetic field, portable MRI (pMRI) for assessing midline shift (MLS). In this observational study, 0.064 T pMRI exams were performed on stroke patients admitted to the neuroscience intensive care unit at Yale New Haven Hospital. Dichotomous (present or absent) and continuous MLS measurements were obtained on pMRI exams and locally available and accessible standard-of-care imaging exams (CT or MRI). We evaluated the agreement between pMRI and standard-of-care measurements. Additionally, we assessed the relationship between pMRI-based MLS and functional outcome (modified Rankin Scale). A total of 102 patients were included in the final study (48 ischemic stroke; 54 intracranial hemorrhage). There was significant concordance between pMRI and standard-of-care measurements (dichotomous, κ = 0.87; continuous, ICC = 0.94). Low-field pMRI identified MLS with a sensitivity of 0.93 and specificity of 0.96. Moreover, pMRI MLS assessments predicted poor clinical outcome at discharge (dichotomous: adjusted OR 7.98, 95% CI 2.07-40.04, p = 0.005; continuous: adjusted OR 1.59, 95% CI 1.11-2.49, p = 0.021). Low-field pMRI may serve as a valuable bedside tool for detecting mass effect.
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Project description:Detecting bacterial cells with high specificity in deep tissues is challenging. Optical probes provide specificity, but are limited by the scattering and absorption of light in biological tissues. Conversely, magnetic resonance imaging (MRI) allows unfettered access to deep tissues, but lacks contrast agents for detecting specific bacterial strains. Here, we introduce a biomolecular platform that combines both capabilities by exploiting the modularity of M13 phage to target bacteria with tunable specificity and allow deep-tissue imaging using T1-weighted MRI. We engineered two types of phage probes: one for detecting the phage's natural host, viz., F-pilus expressing E. coli; and the other for detecting a different (F-negative) bacterial target, V. cholerae. We show that these phage sensors generate 3-9-fold stronger T1 relaxation upon recognizing target cells relative to non-target bacteria. We further establish a preliminary proof-of-concept for in vivo applications, by demonstrating that phage-labeled bacteria can be detected in mice using MRI. The framework developed in this study may have potential utility in a broad range of applications, from basic biomedical research to in situ diagnostics, which require methods to detect and track specific bacteria in the context of intact living systems.
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Project description:Background and aimsEarly detection of perihilar cholangiocarcinoma (CCA) among patients with primary sclerosing cholangitis (PSC) is important to identify more people eligible for curative therapy. While many recommend CCA screening, there are divergent opinions and limited data regarding the use of ultrasound or magnetic resonance imaging (MRI) for early CCA detection, and it is unknown whether there is benefit in testing asymptomatic individuals. Our aims were to assess the diagnostic performances and prognostic implications of ultrasound and MRI-based CCA detection.Approach and resultsThis is a multicenter review of 266 adults with PSC (CCA, n = 120) who underwent both an ultrasound and MRI within 3 months. Images were re-examined by radiologists who were blinded to the clinical information. Respectively, MRI had a higher area under the curve compared with ultrasound for CCA detection: 0.87 versus 0.70 for the entire cohort; 0.81 versus 0.59 for asymptomatic individuals; and 0.88 versus 0.71 for those listed for CCA transplant protocol. The absence of symptoms at CCA diagnosis was associated with improved 5-year outcomes including overall survival (82% vs. 46%, log-rank P < 0.01) and recurrence-free survival following liver transplant (89% vs. 65%, log-rank P = 0.04). Among those with asymptomatic CCA, MRI detection (compared with ultrasound) was associated with reduction in both mortality (hazard ratio, 0.10; 95% confidence interval, 0.01-0.96) and CCA progression after transplant listing (hazard ratio, 0.10; 95% confidence interval, 0.01-0.90). These benefits continued among patients who had annual monitoring and PSC for more than 1 year before CCA was diagnosed.ConclusionsMRI is superior to ultrasound for the detection of early-stage CCA in patients with PSC. Identification of CCA before the onset of symptoms with MRI is associated with improved outcomes.
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Project description:Early detection is critical for effective management of Alzheimer's disease (AD) and screening for mild cognitive impairment (MCI) is common practice. Among several deep-learning techniques that have been applied to assessing structural brain changes on magnetic resonance imaging (MRI), convolutional neural network (CNN) has gained popularity due to its superb efficiency in automated feature learning with the use of a variety of multilayer perceptrons. Meanwhile, ensemble learning (EL) has shown to be beneficial in the robustness of learning-system performance via integrating multiple models. Here, we proposed a classifier ensemble developed by combining CNN and EL, i.e., the CNN-EL approach, to identify subjects with MCI or AD using MRI: i.e., classification between (1) AD and healthy cognition (HC), (2) MCIc (MCI patients who will convert to AD) and HC, and (3) MCIc and MCInc (MCI patients who will not convert to AD). For each binary classification task, a large number of CNN models were trained applying a set of sagittal, coronal, or transverse MRI slices; these CNN models were then integrated into a single ensemble. Performance of the ensemble was evaluated using stratified fivefold cross-validation method for 10 times. The number of the intersection points determined by the most discriminable slices separating two classes in a binary classification task among the sagittal, coronal, and transverse slice sets, transformed into the standard Montreal Neurological Institute (MNI) space, acted as an indicator to assess the ability of a brain region in which the points were located to classify AD. Thus, the brain regions with most intersection points were considered as those mostly contributing to the early diagnosis of AD. The result revealed an accuracy rate of 0.84 ± 0.05, 0.79 ± 0.04, and 0.62 ± 0.06, respectively, for classifying AD vs. HC, MCIc vs. HC, and MCIc vs. MCInc, comparable to previous reports and a 3D deep learning approach (3D-SENet) based on a more state-of-the-art and popular Squeeze-and-Excitation Networks model using channel attention mechanism. Notably, the intersection points accurately located the medial temporal lobe and several other structures of the limbic system, i.e., brain regions known to be struck early in AD. More interestingly, the classifiers disclosed multiple patterned MRI changes in the brain in AD and MCIc, involving these key regions. These results suggest that as a data-driven method, the combined CNN and EL approach can locate the most discriminable brain regions indicated by the trained ensemble model while the generalization ability of the ensemble model was maximized to successfully capture AD-related brain variations early in the disease process; it can also provide new insights into understanding the complex heterogeneity of whole-brain MRI changes in AD. Further research is needed to examine the clinical implication of the finding, capability of the advocated CNN-EL approach to help understand and evaluate an individual subject's disease status, symptom burden and progress, and the generalizability of the advocated CNN-EL approach to locate the most discriminable brain regions in the detection of other brain disorders such as schizophrenia, autism, and severe depression, in a data-driven way.
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2018-12-01 | GSE101908 | GEO
Project description:Although the blood-brain barrier (BBB) was thought to protect the brain from the effects of the immune system, immune cells can nevertheless migrate from the blood to the brain, either as a cause or as a consequence of central nervous system (CNS) diseases, thus contributing to their evolution and outcome. Accordingly, as the interface between the CNS and the peripheral immune system, the BBB is critical during neuroinflammatory processes. In particular, endothelial cells are involved in the brain response to systemic or local inflammatory stimuli by regulating the cellular movement between the circulation and the brain parenchyma. While neuropathological conditions differ in etiology and in the way in which the inflammatory response is mounted and resolved, cellular mechanisms of neuroinflammation are probably similar. Accordingly, neuroinflammation is a hallmark and a decisive player of many CNS diseases. Thus, molecular magnetic resonance imaging (MRI) of inflammatory processes is a central theme of research in several neurological disorders focusing on a set of molecules expressed by endothelial cells, such as adhesion molecules (VCAM-1, ICAM-1, P-selectin, E-selectin, …), which emerge as therapeutic targets and biomarkers for neurological diseases. In this review, we will present the most recent advances in the field of preclinical molecular MRI. Moreover, we will discuss the possible translation of molecular MRI to the clinical setting with a particular emphasis on myeloperoxidase imaging, autologous cell tracking, and targeted iron oxide particles (USPIO, MPIO).
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