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1.
Cancer Inform ; 16: 1176935117694349, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28469389

RESUMO

Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Innovative solutions were implemented to detect and extract unstructured clinical information that was embedded in paper/text documents, including synoptic pathology reports. Supporting important precision medicine use cases, the growing Warehouse enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information of patient tumors individually or as part of large cohorts to identify changes and patterns that may influence treatment decisions and potential outcomes.

2.
IEEE Trans Biomed Eng ; 63(7): 1347-53, 2016 07.
Artigo em Inglês | MEDLINE | ID: mdl-26930673

RESUMO

GOAL: The objective of this study is to design and develop a portable tool consisting of a disposable biochip for measuring electrothermomechanical (ETM) properties of breast tissue. METHODS: A biochip integrated with a microheater, force sensors, and electrical sensors is fabricated using microtechnology. The sensor covers the area of 2 mm and the biochip is 10 mm in diameter. A portable tool capable of holding tissue and biochip is fabricated using 3-D printing. Invasive ductal carcinoma and normal tissue blocks are selected from cancer tissue bank in Biospecimen Repository Service at Rutgers Cancer Institute of New Jersey. The ETM properties of the normal and cancerous breast tissues (3-mm thickness and 2-mm diameter) are measured by indenting the tissue placed on the biochip integrated inside the 3-D printed tool. RESULTS: Integrating microengineered biochip and 3-D printing, we have developed a portable cancer diagnosis device. Using this device, we have shown a statistically significant difference between cancerous and normal breast tissues in mechanical stiffness, electrical resistivity, and thermal conductivity. CONCLUSION: The developed cancer diagnosis device is capable of simultaneous ETM measurements of breast tissue specimens and can be a potential candidate for delineating normal and cancerous breast tissue cores. SIGNIFICANCE: The portable cancer diagnosis tool could potentially provide a deterministic and quantitative information about the breast tissue characteristics, as well as the onset and disease progression of the tissues. The tool can be potentially used for other tissue-related cancers.


Assuntos
Neoplasias da Mama/diagnóstico , Neoplasias da Mama/fisiopatologia , Mama/química , Sistemas Microeletromecânicos/instrumentação , Microtecnologia/instrumentação , Desenho de Equipamento , Feminino , Humanos , Engenharia Tecidual
3.
Lab Chip ; 14(23): 4523-32, 2014 Dec 07.
Artigo em Inglês | MEDLINE | ID: mdl-25267099

RESUMO

The mechanical properties of tissue change significantly during the progression from healthy to malignant. Quantifying the mechanical properties of breast tissue within the tumor microenvironment can help to delineate benign from cancerous stages. In this work, we study high-grade invasive ductal carcinoma in comparison with their matched tumor adjacent areas, which exhibit benign morphology. Such paired tissue cores obtained from eight patients were indented using a MEMS-based piezoresistive microcantilever, which was positioned within pre-designated epithelial and stromal areas of the specimen. Field emission scanning electron microscopy studies on breast tissue cores were performed to understand the microstructural changes from benign to malignant. The normal epithelial tissues appeared compact and organized. The appearance of cancer regions, in comparison, not only revealed increased cellularity but also showed disorganization and increased fenestration. Using this technique, reliable discrimination between epithelial and stromal regions throughout both benign and cancerous breast tissue cores was obtained. The mechanical profiling generated using this method has the potential to be an objective, reproducible, and quantitative indicator for detecting and characterizing breast cancer.


Assuntos
Neoplasias da Mama/química , Mama/química , Sistemas Microeletromecânicos/métodos , Análise Serial de Tecidos/métodos , Neoplasias da Mama/classificação , Carcinoma Ductal de Mama/química , Feminino , Humanos , Microscopia Eletrônica de Varredura , Fenótipo
4.
IEEE Trans Biomed Eng ; 61(2): 547-56, 2014 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-24081838

RESUMO

Nanoindentation using contact-mode atomic force microscopy (AFM) has emerged as a powerful tool for effective material characterization of a wide variety of biomaterials across multiple length scales. However, the interpretation of force-indentation experimental data from AFM is subject to some debate. Uncertainties in AFM data analysis stems from two primary sources: The exact point of contact between the AFM probe and the biological specimen and the variability in the spring constant of the AFM probe. While a lot of attention has been directed toward addressing the contact-point uncertainty, the effect of variability in the probe spring constant has not received sufficient attention. In this paper, we report on an error-in-variables-based Bayesian change-point approach to quantify the elastic modulus of human breast tissue samples after accounting for variability in both contact point and the probe spring constant. We also discuss the efficacy of our approach to a wide range of hyperparameter values using a sensitivity analysis.


Assuntos
Microscopia de Força Atômica/métodos , Modelos Estatísticos , Nanotecnologia/métodos , Análise Serial de Tecidos/métodos , Mama/química , Neoplasias da Mama/química , Feminino , Humanos
5.
Artigo em Inglês | MEDLINE | ID: mdl-24294144

RESUMO

Contact mode Atomic Force Microscopy (CM-AFM) is popularly used by the biophysics community to study mechanical properties of cells cultured in petri dishes, or tissue sections fixed on microscope slides. While cells are fairly easy to locate, sampling in spatially heterogeneous tissue specimens is laborious and time-consuming at higher magnifications. Furthermore, tissue registration across multiple magnifications for AFM-based experiments is a challenging problem, suggesting the need to automate the process of AFM indentation on tissue. In this work, we have developed an image-guided micropositioning system to align the AFM probe and human breast-tissue cores in an automated manner across multiple magnifications. Our setup improves efficiency of the AFM indentation experiments considerably. Note to Practitioners: Human breast tissue is by nature heterogeneous, and in the samples we studied, epithelial tissue is formed by groups of functional breast epithelial cells that are surrounded by stromal tissue in a complex intertwined way. Therefore sampling a specific cell type on an unstained specimen is very difficult. To aid us, we use digital stained images of the same tissue annotated by a certified pathologist to identify the region of interest (ROI) at a coarse magnification and an image-guided positioning system to place the unstained tissue near the AFM probe tip. Using our setup, we could considerably reduce AFM operating time and we believe that our setup is a viable supplement to commercial AFM stages with limited X-Y range.

6.
J Am Med Inform Assoc ; 18(4): 403-15, 2011.
Artigo em Inglês | MEDLINE | ID: mdl-21606133

RESUMO

OBJECTIVE AND DESIGN: The design and implementation of ImageMiner, a software platform for performing comparative analysis of expression patterns in imaged microscopy specimens such as tissue microarrays (TMAs), is described. ImageMiner is a federated system of services that provides a reliable set of analytical and data management capabilities for investigative research applications in pathology. It provides a library of image processing methods, including automated registration, segmentation, feature extraction, and classification, all of which have been tailored, in these studies, to support TMA analysis. The system is designed to leverage high-performance computing machines so that investigators can rapidly analyze large ensembles of imaged TMA specimens. To support deployment in collaborative, multi-institutional projects, ImageMiner features grid-enabled, service-based components so that multiple instances of ImageMiner can be accessed remotely and federated. RESULTS: The experimental evaluation shows that: (1) ImageMiner is able to support reliable detection and feature extraction of tumor regions within imaged tissues; (2) images and analysis results managed in ImageMiner can be searched for and retrieved on the basis of image-based features, classification information, and any correlated clinical data, including any metadata that have been generated to describe the specified tissue and TMA; and (3) the system is able to reduce computation time of analyses by exploiting computing clusters, which facilitates analysis of larger sets of tissue samples.


Assuntos
Mineração de Dados/métodos , Sistemas de Gerenciamento de Base de Dados , Processamento de Imagem Assistida por Computador , Análise Serial de Tecidos/instrumentação , Humanos , Disseminação de Informação , Design de Software , Estados Unidos
7.
IEEE Trans Inf Technol Biomed ; 13(4): 636-44, 2009 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-19369162

RESUMO

Breast cancer accounts for about 30% of all cancers and 15% of cancer deaths in women. Advances in computer-assisted analysis hold promise for classifying subtypes of disease and improving prognostic accuracy. We introduce a grid-enabled decision support system for performing automatic analysis of imaged breast tissue microarrays. To date, we have processed more than 1,00,000 digitized specimens (1200 x 1200 pixels each) on IBM's World Community Grid (WCG). As a part of the Help Defeat Cancer (HDC) project, we have analyzed that the data returned from WCG along with retrospective patient clinical profiles for a subset of 3744 breast tissue samples, and have reported the results in this paper. Texture-based features were extracted from the digitized specimens, and isometric feature mapping was applied to achieve nonlinear dimension reduction. Iterative prototyping and testing were performed to classify several major subtypes of breast cancer. Overall, the most reliable approach was gentle AdaBoost using an eight-node classification and regression tree as the weak learner. Using the proposed algorithm, a binary classification accuracy of 89% and the multiclass accuracy of 80% were achieved. Throughout the course of the experiments, only 30% of the dataset was used for training.


Assuntos
Neoplasias da Mama/patologia , Diagnóstico por Computador/métodos , Interpretação de Imagem Assistida por Computador/métodos , Análise Serial de Tecidos/métodos , Algoritmos , Neoplasias da Mama/metabolismo , Análise por Conglomerados , Feminino , Histocitoquímica , Humanos , Estudos Retrospectivos
8.
IEEE Trans Inf Technol Biomed ; 13(3): 291-9, 2009 May.
Artigo em Inglês | MEDLINE | ID: mdl-19171530

RESUMO

Large-scale, multisite collaboration has become indispensable for a wide range of research and clinical activities that rely on the capacity of individuals to dynamically acquire, share, and assess images and correlated data. In this paper, we report the development of a Web-based system, PathMiner , for interactive telemedicine, intelligent archiving, and automated decision support in pathology. The PathMiner system supports network-based submission of queries and can automatically locate and retrieve digitized pathology specimens along with correlated molecular studies of cases from "ground-truth" databases that exhibit spectral and spatial profiles consistent with a given query image. The statistically most probable diagnosis is provided to the individual who is seeking decision support. To test the system under real-case scenarios, a pipeline infrastructure was developed and a network-based test laboratory was established at strategic sites at the University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, Robert Wood Johnson University Hospital, the University of Pennsylvania School of Medicine, Hospital of the University of Pennsylvania, The Cancer Institute of New Jersey, and Rutgers University. The average five-class classification accuracy of the system was 93.18% based on a tenfold cross validation on a close dataset containing 3691 imaged specimens. We also conducted prospective performance studies with the PathMiner system in real applications in which the specimens exhibited large variations in staining characters compared with the training data. The average five-class classification accuracy in this open-set experiment was 87.22%. We also provide the comparative results with the previous literature and the PathMiner system shows superior performance.


Assuntos
Redes de Comunicação de Computadores , Diagnóstico por Computador/métodos , Interpretação de Imagem Assistida por Computador/métodos , Armazenamento e Recuperação da Informação/métodos , Algoritmos , Inteligência Artificial , Células Sanguíneas/classificação , Células Sanguíneas/citologia , Células Sanguíneas/patologia , Humanos , Internet , Modelos Estatísticos , Reprodutibilidade dos Testes , Interface Usuário-Computador
9.
Comput Methods Programs Biomed ; 79(1): 59-72, 2005 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-15908036

RESUMO

In many subspecialties of pathology, the intrinsic complexity of rendering accurate diagnostic decisions is compounded by a lack of definitive criteria for detecting and characterizing diseases and their corresponding histological features. In some cases, there exists a striking disparity between the diagnoses rendered by recognized authorities and those provided by non-experts. We previously reported the development of an Image Guided Decision Support (IGDS) system, which was shown to reliably discriminate among malignant lymphomas and leukemia that are sometimes confused with one another during routine microscopic evaluation. As an extension of those efforts, we report here a web-based intelligent archiving subsystem that can automatically detect, image, and index new cells into distributed ground-truth databases. Systematic experiments showed that through the use of robust texture descriptors and density estimation based fusion the reliability and performance of the governing classifications of the system were improved significantly while simultaneously reducing the dimensionality of the feature space.


Assuntos
Armazenamento e Recuperação da Informação , Patologia , Diagnóstico Diferencial , Humanos , Leucemia/classificação , Leucemia/diagnóstico , Linfoma/classificação , Linfoma/diagnóstico
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