Increased levels of hyaluronic acid in bronchoalveolar lavage from patients with interstitial lung diseases, relationship with lung function and inflammatory cells recruitment

Purpose: Interstitial Lung Diseases (ILD) are characterized by inflammation and fibrosis. It described the role of hyaluronic acid (HA) as an immune-regulator. It is not known if HA contributes to the recruitment of inflammatory cells associated with ILD. If this hypothesis was correct, then concentrations of HA in bronchoalveolar lavage (BAL) should correlate with the severity of ILD. Methods: We collected BAL from 22 ILD patients and 15 control subjects. We determined HA and cytokine levels by ELISA. In vitro chemotaxis assays were performed by using a transwell system. Results: We found that ILD patients showed a significant increase in HA, IL-6 levels and the amount of cells in BAL compared to control subjects. We detected a significant positive correlation between HA and IL-6 levels (r = 0.53 and p < 0.001) and an inverse relationship between HA levels and diffusion capacity (r = -0.59, p < 0.01). In vitro, HA induced migration of macrophages and monocytes through a CD44-dependent process. BAL from patients with ILD stimulated macro-phage migration and this was abrogated by hyaluronidase. Conclusions: Our results support the hypothesis that HA contributes to the recruitment of monocytes towards the alveolar space, leading to exacerbation of lung inflammation in ILD patients.Fil: Ernst, Glenda. Ciudad Autónoma de Buenos Aires. Hospital María Ferrer; ArgentinaFil: Jancic, Carolina Cristina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Medicina Experimental. Academia Nacional de Medicina de Buenos Aires. Instituto de Medicina Experimental; ArgentinaFil: Auteri, Santiago. Ciudad Autónoma de Buenos Aires. Hospital María Ferrer; ArgentinaFil: Rodriguez Moncalvo, Juan. Ciudad Autónoma de Buenos Aires. Hospital María Ferrer; ArgentinaFil: Galíndez, Fernando. Ciudad Autónoma de Buenos Aires. Hospital María Ferrer; ArgentinaFil: Grynblat, Pedro. Ciudad Autónoma de Buenos Aires. Hospital María Ferrer; ArgentinaFil: Hajos, Silvia Elvira. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Instituto de Estudios de la Inmunidad Humoral Prof. Ricardo A. Margni; Argentin

The potential impact of CT-MRI matching on tumor volume delineation in advanced head and neck cancer

To study the potential impact of the combined use of CT and MRI scans on the Gross Tumor Volume (GTV) estimation and interobserver variation. Four observers outlined the GTV in six patients with advanced head and neck cancer on CT, axial MRI, and coronal or sagittal MRI. The MRI scans were subsequently matched to the CT scan. The interobserver and interscan set variation were assessed in three dimensions. The mean CT derived volume was a factor of 1.3 larger than the mean axial MRI volume. The range in volumes was larger for the CT than for the axial MRI volumes in five of the six cases. The ratio of the scan set common (i.e., the volume common to all GTVs) and the scan set encompassing volume (i.e., the smallest volume encompassing all GTVs) was closer to one in MRI (0.3-0.6) than in CT (0.1-0.5). The rest volumes (i.e., the volume defined by one observer as GTV in one data set but not in the other data set) were never zero for CT vs. MRI nor for MRI vs. CT. In two cases the craniocaudal border was poorly recognized on the axial MRI but could be delineated with a good agreement between the observers in the coronal/sagittal MRI. MRI-derived GTVs are smaller and have less interobserver variation than CT-derived GTVs. CT and MRI are complementary in delineating the GTV. A coronal or sagittal MRI adds to a better GTV definition in the craniocaudal directio

Accuracy of magnetic resonance imaging to identify pseudocapsule invasion in renal tumors

Purpose: To evaluate accuracy of MRI in detecting renal tumor pseudocapsule (PC) invasion and to propose a classification based on imaging of PC status in patients with renal cell carcinoma. Methods: From January 2017 to June 2018, 58 consecutive patients with localized renal cell carcinoma were prospectively enrolled. MRI was performed preoperatively and PC was classified, according to its features, as follows: MRI-Cap 0 (absence of PC), MRI-Cap 1 (presence of a clearly identifiable PC), MRI-Cap 2 (focally interrupted PC), and MRI-Cap 3 (clearly interrupted and infiltrated PC). A 3D image reconstruction showing MRI-Cap score was provided to both surgeon and pathologist to obtain complete preoperative evaluation and to compare imaging and pathology reports. All patients underwent laparoscopic partial nephrectomy. In surgical specimens, PC was classified according to the renal tumor capsule invasion scoring system (i-Cap). Results: A concordance between MRI-Cap and i-Cap was found in 50/58 (86%) cases. ρ coefficient for each MRI-cap and iCap categories was: MRI-Cap 0: 0.89 (p &lt; 0.0001), MRI-Cap1: 0.75 (p &lt; 0.0001), MRI-Cap 2: 0.76 (p &lt; 0.0001), and MRI-Cap3: 0.87 (p &lt; 0.0001). Sensitivity, specificity, positive predictive value, negative predictive value, and AUC were: MRI-Cap 0: Se 97.87% Spec 83.3%, PPV 95.8%, NPV 90.9%, and AUC 90.9; MRI-Cap 1: Se 77% Spec 95.5%, PPV 83.3%, NPV 93.5%, and AUC 0.86; MRI-Cap 2- iCap 2: Se 88% Spec 90%, PPV 79%, NPV 95%, and AUC 0.89; MRI-Cap 3: Se 94% Spec 95%, PPV 88%, NPV 97%, and AUC 0.94. Conclusions: MRI-Cap classification is accurate in evaluating renal tumor PC features. PC features can provide an imaging-guided landmark to figure out where a minimal margin could be preferable during nephron-sparing surgery

Better than Real: Complex-valued Neural Nets for MRI Fingerprinting

The task of MRI fingerprinting is to identify tissue parameters from complex-valued MRI signals. The prevalent approach is dictionary based, where a test MRI signal is compared to stored MRI signals with known tissue parameters and the most similar signals and tissue parameters retrieved. Such an approach does not scale with the number of parameters and is rather slow when the tissue parameter space is large. Our first novel contribution is to use deep learning as an efficient nonlinear inverse mapping approach. We generate synthetic (tissue, MRI) data from an MRI simulator, and use them to train a deep net to map the MRI signal to the tissue parameters directly. Our second novel contribution is to develop a complex-valued neural network with new cardioid activation functions. Our results demonstrate that complex-valued neural nets could be much more accurate than real-valued neural nets at complex-valued MRI fingerprinting.Comment: Accepted in Proc. IEEE International Conference on Image Processing (ICIP), 201

MRI in multiple myeloma : a pictorial review of diagnostic and post-treatment findings

Magnetic resonance imaging (MRI) is increasingly being used in the diagnostic work-up of patients with multiple myeloma. Since 2014, MRI findings are included in the new diagnostic criteria proposed by the International Myeloma Working Group. Patients with smouldering myeloma presenting with more than one unequivocal focal lesion in the bone marrow on MRI are considered having symptomatic myeloma requiring treatment, regardless of the presence of lytic bone lesions. However, bone marrow evaluation with MRI offers more than only morphological information regarding the detection of focal lesions in patients with MM. The overall performance of MRI is enhanced by applying dynamic contrast-enhanced MRI and diffusion weighted imaging sequences, providing additional functional information on bone marrow vascularization and cellularity. This pictorial review provides an overview of the most important imaging findings in patients with monoclonal gammopathy of undetermined significance, smouldering myeloma and multiple myeloma, by performing a 'total' MRI investigation with implications for the diagnosis, staging and response assessment. Main message aEuro cent Conventional MRI diagnoses multiple myeloma by assessing the infiltration pattern. aEuro cent Dynamic contrast-enhanced MRI diagnoses multiple myeloma by assessing vascularization and perfusion. aEuro cent Diffusion weighted imaging evaluates bone marrow composition and cellularity in multiple myeloma. aEuro cent Combined morphological and functional MRI provides optimal bone marrow assessment for staging. aEuro cent Combined morphological and functional MRI is of considerable value in treatment follow-up

Differential diagnosis of adrenal masses by chemical shift and dynamic gadolinium enhanced MR imaging.

Chemical shift MRI is widely used for identifying adenomas, but it is not a perfect method. We determined whether combined dynamic MRI methods can lead to improved diagnostic accuracy. Fifty-seven adrenal masses were examined by chemical shift and dynamic MR imaging using 2 MR systems. The masses included 38 adenomas and 19 non-adenomas. In chemical shift MRI studies, the signal intensity index (SI) was calculated, and the lesions classified into 5 types in the dynamic MRI studies. Of the 38 adenomas studied, 37 had an SI greater than 0. In the dynamic MRI, 34 of 38 adenomas showed a benign pattern (type 1). If the SI for the adenomas in the chemical shift MRI was considered to be greater than 0, the positive predictive value was 0.9, and the negative predictive value was 0.94 and kappa = 0.79. If type 1 was considered to indicate adenomas in the dynamic MRI, the corresponding values were 0.94, 0.81 and kappa = 0.77 respectively. The results obtained when the 2 methods were combined were 1, 0.95 and kappa = 0.96 respectively. The chemical shift MRI was found to be useful for identifying adenomas in most cases. If the adrenal mass had a low SI (0 &#60; SI &#60; 5), dynamic MRI was also found to be helpful for making a differential diagnosis.</p

A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI

Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indi cations. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by deep learning techniques, some preliminary works have tried to incorporate deep architectures into CS-MRI process. Unfortunately, the convergence issues (due to the experience-based networks) and the robustness (i.e., lack real-world noise modeling) of these deeply trained optimization methods are still missing. In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI. By introducing an optimal condition checking mechanism, we can successfully prove the convergence of our established deep CS-MRI optimization scheme. Furthermore, we explicitly formulate the Rician noise distributions within our framework and obtain an extended CS-MRI network to handle the real-world nosies in the MRI process. Extensive experimental results verify that the proposed paradigm outperforms the existing state-of-the-art techniques both in reconstruction accuracy and efficiency as well as robustness to noises in real scene

Training health professionals in patient-centered communication during magnetic resonance imaging to reduce patients’ perceived anxiety

Objective: We examined how a patient-centered communication training program for magnetic resonance imaging (MRI) affected health professional (HP) practice and patients’ perceived anxiety (PA). Methods: We implemented an intervention program. Six of the 17 eligible HPs completed the study. The proportion of observed desired behaviors (PODBs), including MRI procedure explanation (MRI-PE), communication, and MRI checking procedures was measured using an observation grid. We tested 182 patients (85 pre-, 58 post-, and 39 at follow-up) for PA pre- and post-MRI. Results: The Bayesian ANOVA effect size suggested moderate evidence of improvement in HP PODBs, preto post-intervention. Use of MRI-PE declined between post-intervention and follow-up (6 months later). Observed changes in PA, pre- to post-MRI, could be related to time constraints and perceived pressure to explain the exam in detail once institutional routines are reestablished. Conclusion: In MRI units, time constraints condition the performance of HPs who address patients’ PA. Practice implications: “Real workplace” interventions that promote better patient-centered communication and provide each patient with a comprehensive explanation of MRI procedures also appear to improve HP PODBs