Photoacoustic image patterns of breast carcinoma and comparisons with Magnetic Resonance Imaging and vascular stained histopathology.
Ontology highlight
ABSTRACT:
SUBMITTER: Heijblom M
PROVIDER: S-EPMC4498178 | biostudies-other | 2015
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Computer-aided image analysis (CAI) can help objectively quantify morphologic features of hematoxylin-eosin (HE) histopathology images and provide potentially useful prognostic information on breast cancer. We performed a CAI workflow on 1,150 HE images from 230 patients with invasive ductal carcinoma (IDC) of the breast. We used a pixel-wise support vector machine classifier for tumor nests (TNs)-stroma segmentation, and a marker-controlled watershed algorithm for nuclei segmentation. 730 morphologic parameters were extracted after segmentation, and 12 parameters identified by Kaplan-Meier analysis were significantly associated with 8-year disease free survival (P?<?0.05 for all). Moreover, four image features including TNs feature (HR 1.327, 95%CI [1.001-1.759], P?=?0.049), TNs cell nuclei feature (HR 0.729, 95%CI [0.537-0.989], P?=?0.042), TNs cell density (HR 1.625, 95%CI [1.177-2.244], P?=?0.003), and stromal cell structure feature (HR 1.596, 95%CI [1.142-2.229], P?=?0.006) were identified by multivariate Cox proportional hazards model to be new independent prognostic factors. The results indicated that CAI can assist the pathologist in extracting prognostic information from HE histopathology images for IDC. The TNs feature, TNs cell nuclei feature, TNs cell density, and stromal cell structure feature could be new prognostic factors.
| S-EPMC4448264 | biostudies-literature
Project description:The application of magnetic resonance image (MRI)-guided brachytherapy has demonstrated significant growth during the past 2 decades. Clinical improvements in cervix cancer outcomes have been linked to the application of repeated MRI for identification of residual tumor volumes during radiotherapy. This has changed clinical practice in the direction of individualized dose administration, and resulted in mounting evidence of improved clinical outcome regarding local control, overall survival as well as morbidity. MRI-guided prostate high-dose-rate and low-dose-rate brachytherapies have improved the accuracy of target and organs-at-risk delineation, and the potential exists for improved dose prescription and reporting for the prostate gland and organs at risk. Furthermore, MRI-guided prostate brachytherapy has significant potential to identify prostate subvolumes and dominant lesions to allow for dose administration reflecting the differential risk of recurrence. MRI-guided brachytherapy involves advanced imaging, target concepts, and dose planning. The key issue for safe dissemination and implementation of high-quality MRI-guided brachytherapy is establishment of qualified multidisciplinary teams and strategies for training and education.
| S-EPMC4147854 | biostudies-literature
Project description:Delayed enhancement cardiovascular magnetic resonance (MR) is the gold-standard for non-invasive assessment after myocardial infarction (MI). MR microscopy (MRM) provides a level of detail comparable to the macro objective of light microscopy. We used MRM and correlative histopathology to identify infarct and remote tissue in contrast agent-free multi-sequence MRM in swine MI hearts. One control group (n?=?3 swine) and two experimental MI groups were formed: 90?min of ischemia followed by 1 week (acute MI?=?6 swine) or 1 month (chronic MI?=?5 swine) reperfusion. Representative samples of each heart were analysed by contrast agent-free multi-sequence (T1-weighting, T2-weighting, T2*-weighting, T2-mapping, and T2*-mapping). MRM was performed in a 14-Tesla vertical axis imager (Bruker-AVANCE 600 system). Images from MRM and the corresponding histopathological stained samples revealed differences in signal intensities between infarct and remote areas in both MI groups (p-value?<?0.001). The multivariable models allowed us to precisely classify regions of interest (acute MI: specificity 92% and sensitivity 80%; chronic MI: specificity 100% and sensitivity 98%). Probabilistic maps based on MRM images clearly delineated the infarcted regions. As a proof of concept, these results illustrate the potential of MRM with correlative histopathology as a platform for exploring novel contrast agent-free MR biomarkers after MI.
| S-EPMC6934559 | biostudies-literature
Project description:Background and purposeThe diagnostic accuracy of new imaging techniques requires validation, preferably by histopathological verification. The aim of this study was to develop and present a registration procedure between histopathology and in-vivo magnetic resonance imaging (MRI) of the prostate, to estimate its uncertainty and to evaluate the benefit of adding a contour-correcting registration.Materials and methodsFor twenty-five prostate cancer patients, planned for radical prostatectomy, a 3D-printed prostate mold based on in-vivo MRI was created and an ex-vivo MRI of the specimen, placed inside the mold, was performed. Each histopathology slice was registered to its corresponding ex-vivo MRI slice using a 2D-affine registration. The ex-vivo MRI was rigidly registered to the in-vivo MRI and the resulting transform was applied to the histopathology stack. A 2D deformable registration was used to correct for specimen distortion concerning the specimen's fit inside the mold. We estimated the spatial uncertainty by comparing positions of landmarks in the in-vivo MRI and the corresponding registered histopathology stack.ResultsEighty-four landmarks were identified, located in the urethra (62%), prostatic cysts (33%), and the ejaculatory ducts (5%). The median number of landmarks was 3 per patient. We showed a median in-plane error of 1.8 mm before and 1.7 mm after the contour-correcting deformable registration. In patients with extraprostatic margins, the median in-plane error improved from 2.1 mm to 1.8 mm after the contour-correcting deformable registration.ConclusionsOur registration procedure accurately registers histopathology to in-vivo MRI, with low uncertainty. The contour-correcting registration was beneficial in patients with extraprostatic surgical margins.
| S-EPMC8254194 | biostudies-literature
Project description:Sporadic cerebral small vessel disease (SVD) is a major contributor to vascular cognitive impairment and dementia in the aging human brain. On neuropathology, sporadic SVD is characterized by abnormalities to the small vessels of the brain predominantly in the form of cerebral amyloid angiopathy and arteriolosclerosis. These pathologies frequently coexist with Alzheimer disease changes, such as plaques and tangles, in a single brain. Conversely, during life, magnetic resonance imaging (MRI) only captures the larger manifestations of SVD in the form of parenchymal brain abnormalities. There appears to be a major knowledge gap regarding the underlying neuropathology of individual MRI-detectable SVD abnormalities. Ex vivo MRI in postmortem human brain tissue is a powerful tool to bridge this gap. This review summarizes current insights into the histopathologic correlations of MRI manifestations of SVD, their underlying cause, presumed pathophysiology, and associated secondary tissue injury. Moreover, we discuss the advantages and limitations of ex vivo MRI-guided histopathologic investigations and make recommendations for future studies.
| S-EPMC8830602 | biostudies-literature
Project description:This paper describes an adaptive approach to regularizing model-based reconstructions in magnetic resonance imaging to account for local structure or image content. In conjunction with common models like wavelet and total variation sparsity, this content-aware regularization avoids oversmoothing or compromising image features while suppressing noise and incoherent aliasing from accelerated imaging. To evaluate this regularization approach, the experiments reconstruct images from single- and multi-channel, Cartesian and non-Cartesian, brain and cardiac data. These reconstructions combine common analysis-form regularizers and autocalibrating parallel imaging (when applicable). In most cases, the results show widespread improvement in structural similarity and peak-signal-to-error ratio relative to the fully sampled images. These results suggest that this content-aware regularization can preserve local image structures such as edges while providing denoising power superior to sparsity-promoting or sparsity-reweighted regularization.
| S-EPMC6102097 | biostudies-literature
Project description:According to clinical statistics, the mortality of patients with early brainstem hemorrhage is high. In this study, we established rat models of brainstem hemorrhage by injecting type VII collagenase into the right basotegmental pontine and investigated the pathological changes of early brainstem hemorrhage using multi-sequence magnetic resonance imaging and histopathological methods. We found that brainstem hematoma gradually formed in the injured rats over the first 3 days and then reduced after 7 days. The edema that occurred was mainly of the vasogenic type. No complete myelin sheath structure was found around the focus of the brainstem hemorrhage. The integrity and continuity of nerve fibers gradually deteriorated over the first 7 days. Neuronal degeneration was mild in the first 3 days and then obviously aggravated on the 7th day. Inflammatory cytokines, interleukin-1β, and tumor necrosis factor α appeared on the 1st day after intracerebral hemorrhage, reached peak levels on the 3rd day, and decreased from the 7th day. Our findings show the characteristics of the progression of early brainstem hemorrhage.
| S-EPMC9241409 | biostudies-literature
Project description:BackgroundOur aim of the study is to investigate the feasibility of preoperative prediction for hepatocellular carcinoma (HCC) histological grading using gadoxetic acid-enhanced magnetic resonance imaging (MRI).MethodsThis study included one hundred and fifty-six patients with solitary HCC. Preoperative gadoxetic acid-enhanced MRI findings were retrospectively analyzed. MRI qualitative features such as tumor size, margin, capsule status, signal homogeneity, intratumoral vessels, peritumoral enhancement during mid-arterial phase, peritumoral hypointensity during the hepatobiliary phase (HBP) were investigated. Apparent diffusion coefficients (ADCs), T1 reduction ratio of pre- and post-contrast enhanced images of the tumors were calculated. HCC histological grading in surgical specimens were confirmed by Edmonson's criteria. Correlations between these MRI features and HCC histological grading were analyzed using multivariate logistic regression. The receiver operating characteristic (ROC) curve was used to assess the predictive efficacy of the model.ResultsUnivariate analysis showed that maximum tumor diameter (p = 0.004), tumor margin (p = 0.006), intratumoral vessels (p = 0.001) and peritumoral hypointensity during HBP (p = 0.000), were significantly correlated with HCC histological grading. There was no relationship between capsule, tumor signal, venous thrombosis, peritumoral enhancement during mid-arterial phase, ADC value, T1 reduction ratio, and HCC histological grading. Multivariate logistic regression analysis demonstrated that the maximum tumor diameter (p = 0.012, odds ratio = 1.002, 95% confidence interval: 1.007-1.046)) was an independent risk factor for high grade HCC.ConclusionsGreater tumor size, a more irregular margin, presence of intratumoral vessels, and peritumoral hypointensity during HBP were indicators for high grade HCC. The maximum tumor diameter was an independent risk factor for high grade HCC.
| S-EPMC6472074 | biostudies-literature
Project description:By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T2-weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T2-weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets.
| S-EPMC5099106 | biostudies-literature
Project description:Modern sequences for Magnetic Resonance Imaging (MRI) trade off scan time with computational challenges, resulting in ill-posed inverse problems and the requirement to account for more elaborated signal models. Various deep learning techniques have shown potential for image reconstruction from reduced data, outperforming compressed sensing, dictionary learning and other advanced techniques based on regularization, by characterization of the image manifold. In this work we suggest a framework for reducing a "neural" network to the bare minimum required by the MR physics, reducing the network depth and removing all non-linearities. The networks performed well both on benchmark simulated data and on arterial spin labeling perfusion imaging, showing clear images while preserving sensitivity to the minute signal changes. The results indicate that the deep learning framework plays a major role in MR image reconstruction, and suggest a concrete approach for probing into the contribution of additional elements.
| S-EPMC6925115 | biostudies-literature