Browsing by Subject "histopathology"

Now showing 1 - 4 of 4
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    Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer disease: Phase 3 study
    (Elsevier, 2015) Sabri, Osama; Sabbagh, Marwan N.; Seibyl, John; Barthel, Henryk; Akatsu, Hiroyasu; Ouchi, Yasuomi; Senda, Kohei; Murayama, Shigeo; Ishii, Kenji; Takao, Masaki; Beach, Thomas G.; Rowe, Christopher C.; Leverenz, James B.; Ghetti, Bernardino; Ironside, James W.; Catafau, Ana M.; Stephens, Andrew W.; Mueller, Andre; Koglin, Norman; Hoffman, Anja; Roth, Katrin; Reininger, Cornelia; Schulz-Schaeffer, Walter J.; Department of Pathology and Laboratory Medicine, IU School of Medicine
    Background Evaluation of brain β-amyloid by positron emission tomography (PET) imaging can assist in the diagnosis of Alzheimer disease (AD) and other dementias. Methods Open-label, nonrandomized, multicenter, phase 3 study to validate the 18F-labeled β-amyloid tracer florbetaben by comparing in vivo PET imaging with post-mortem histopathology. Results Brain images and tissue from 74 deceased subjects (of 216 trial participants) were analyzed. Forty-six of 47 neuritic β-amyloid-positive cases were read as PET positive, and 24 of 27 neuritic β-amyloid plaque-negative cases were read as PET negative (sensitivity 97.9% [95% confidence interval or CI 93.8–100%], specificity 88.9% [95% CI 77.0–100%]). In a subgroup, a regional tissue-scan matched analysis was performed. In areas known to strongly accumulate β-amyloid plaques, sensitivity and specificity were 82% to 90%, and 86% to 95%, respectively. Conclusions Florbetaben PET shows high sensitivity and specificity for the detection of histopathology-confirmed neuritic β-amyloid plaques and may thus be a valuable adjunct to clinical diagnosis, particularly for the exclusion of AD.
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    Histopathology of Explanted Lungs From Patients With a Diagnosis of Pulmonary Sarcoidosis
    (Elsevier, 2016-02) Zhang, Chen; Chan, Kevin M.; Schmidt, Lindsay A.; Myers, Jeffrey L.; Department of Pathology & Laboratory Medicine, IU School of Medicine
    Background Pathologic features of end-stage pulmonary sarcoidosis (ESPS) are not well defined; anecdotal reports have suggested that ESPS may mimic usual interstitial pneumonia (UIP). We hypothesized that ESPS has distinct histologic features. Methods Twelve patients who received a diagnosis of pulmonary sarcoidosis and underwent lung transplantation were included. Control subjects were 10 age- and sex-matched lung transplant patients with UIP. Hematoxylin and eosin-stained tissue sections were examined for the following features: extent/pattern of fibrosis; presence and quantity (per 10 high-power fields) of fibroblast foci and granulomas; distribution and morphology of granulomas; and presence and extent of honeycomb change. Extent of fibrosis and honeycomb change in lung parenchyma was scored as follows: 1 = 1% to 25%; 2 = 26% to 50%; 3 = 51% to 75%; 4 = 76% to 100% of lung parenchyma. Results Eight of 12 cases demonstrated histologic findings typical of ESPS. All showed well-formed granulomas with associated fibrosis distributed in a distinct lymphangitic fashion. Granulomas were present in hilar or mediastinal lymph nodes from six of six patients with ESPS and none of eight control subjects. The extent of fibrosis, honeycomb change, and fibroblast foci was significantly lower in ESPS cases compared with control cases. Two patients with remote histories of sarcoidosis showed histologic features of diseases other than ESPS (UIP and emphysema) without granulomas. Two patients with atypical clinical findings demonstrated nonnecrotizing granulomas combined with either severe chronic venous hypertension or UIP. Conclusions ESPS and UIP have distinct histopathologic features in the lungs. Patients with a pretransplant diagnosis of sarcoidosis may develop other lung diseases that account for their end-stage fibrosis.
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    Identification of Topological Features in Renal Tumor Microenvironment Associated with Patient Survival
    (Oxford, 2018-03) Cheng, Jun; Mo, Xiaokui; Wang, Xusheng; Parwani, Anil; Feng, Qianjin; Huang, Kun; Medicine, School of Medicine
    Motivation As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers.
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    Mitotic cell detection in H&E stained meningioma histopathology slides
    (2019-12) Cheng, Huiwen; Tsechpenakis, Gavriil; Tuceryan, Mihran; Jiang, Yu zheng
    Meningioma represent more than one-third of all primary central nervous system (CNS) tumors, and it can be classified into three grades according to WHO (World Health Organization) in terms of clinical aggressiveness and risk of recurrence. A key component of meningioma grades is the mitotic count, which is defined as quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at 10 consecutive high-power fields (HPF) on a glass slide under a microscope, which is an extremely laborious and time-consuming process. The goal of this thesis is to investigate the use of computerized methods to automate the detection of mitotic nuclei with limited labeled data. We built computational methods to detect and quantify the histological features of mitotic cells on a whole slides image which mimic the exact process of pathologist workflow. Since we do not have enough training data from meningioma slide, we learned the mitotic cell features through public available breast cancer datasets, and predicted on meingioma slide for accuracy. We use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Hand crafted features are inspired by the domain knowledge, while the data-driven VGG16 models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. Our work on detection of mitotic cells shows 100% recall , 9% precision and 0.17 F1 score. The detection using VGG16 performs with 71% recall, 73% precision, and 0.77 F1 score. Finally, this research of automated image analysis could drastically increase diagnostic efficiency and reduce inter-observer variability and errors in pathology diagnosis, which would allow fewer pathologists to serve more patients while maintaining diagnostic accuracy and precision. And all these methodologies will increasingly transform practice of pathology, allowing it to mature toward a quantitative science.