Lauri Goodell

Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy

The process of discriminating among pathologies involving peripheral blood, bone marrow, and lymph node has traditionally begun with subjective morphological assessment of cellular materials viewed using light microscopy. The subtle visible differences exhibited by some malignant lymphomas and leukemia, however, give rise to a significant number of false negatives during microscopic evaluation by medical technologists. We have developed a distributed, clinical decision support prototype for distinguishing among hematologic malignancies. The system consists of two major components, a distributed telemicroscopy system and an intelligent image repository. The hybrid system enables individuals located at disparate clinical and research sites to engage in interactive consultation and to obtain computer-assisted decision support. Software, written in Java, allows primary users to control the specimen stage, objective lens, light levels, and focus of a robotic microscope remotely while a digital representation of the specimen is continuously broadcast to all session participants. Primary user status can be passed as a token. The system features shared graphical pointers, text messaging capability, and automated database management. Search engines for the database allow one to automatically identify and retrieve images, diagnoses, and correlated clinical data of cases from a gold standard database which exhibit spectral and spatial profiles which are most similar to a given query image.

Mesenchymal stromal cells protect mantle cell lymphoma cells from spontaneous and drug-induced apoptosis through secretion of B-cell activating factor and activation of the canonical and non-canonical nuclear factor κB pathways

Background There is increasing evidence that stromal cell interactions are required for the survival and drug resistance of several types of B-cell malignancies. There is relatively little information regarding the role of the bone marrow/lymphoid microenvironment in the pathogenesis of mantle cell lymphoma. In this study we investigated the interaction of primary mantle cell lymphoma cells with stromal cells in an ex vivo co-culture system. Design and Methods The murine stromal cell line MS-5 and human bone marrow mesenchymal stromal cells were each co-cultured with primary mantle cell lymphoma cells for up to 7 months. Mantle cell lymphoma cultures alone or combined with human stromal cells were analyzed for cell number, cell migration, nuclear factor-κB activation and drug resistance. Results Co-culture of mantle cell lymphoma cells and human stromal cells results in the survival and proliferation of primary mantle cell lymphoma cells for at least 7 months compared to mantle cell lymphoma cells cultured alone. Mantle cell lymphoma-human stromal cell interactions resulted in activation of the B-cell activating factor/nuclear factor-κB signaling axis resulting in reduced apoptosis, increased mantle cell lymphoma migration and increased drug resistance. Conclusions Direct mantle cell lymphoma-human stromal cell interactions support long-term expansion and increase the drug-resistance of primary mantle cell lymphoma cells. This is due in part to activation of the canonical and non-canonical nuclear factor κB pathways. We also demonstrated the ability of B-cell activating factor to augment CXCL12- and CXCL13-induced cell migration. Collectively, these findings demonstrate that human stromal cell-mantle cell lymphoma interactions play a pivotal role in the pathogenesis of mantle cell lymphoma and that analysis of mantle cell lymphoma-human stromal cell interactions may help in the identification of novel targets for therapeutic use.

ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology

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.

Virtual Microscopy and Grid-Enabled Decision Support for Large-Scale Analysis of Imaged Pathology Specimens

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 times 1200 pixels each) on IBMs 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.

PathMiner: A Web-Based Tool for Computer-Assisted Diagnostics in Pathology

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 ldquoground-truthrdquo 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.

Image mining for investigative pathology using optimized feature extraction and data fusion

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.

Clinical Actionability of Comprehensive Genomic Profiling for Management of Rare or Refractory Cancers

To study the frequency with which targeted tumor sequencing results will lead to implemented change in care, this study assessed tumors from 100 patients for utility, feasibility, and limitations of genomic sequencing for genomically guided therapy or other clinical purpose in the setting of a multidisciplinary molecular tumor board. Comprehensive profiling led to implementable clinical action in 35% of tumors with genomic alterations.

Content-based histopathology image retrieval using CometCloud

BackgroundThe development of digital imaging technology is creating extraordinary levels of accuracy that provide support for improved reliability in different aspects of the image analysis, such as content-based image retrieval, image segmentation, and classification. This has dramatically increased the volume and rate at which data are generated. Together these facts make querying and sharing non-trivial and render centralized solutions unfeasible. Moreover, in many cases this data is often distributed and must be shared across multiple institutions requiring decentralized solutions. In this context, a new generation of data/information driven applications must be developed to take advantage of the national advanced cyber-infrastructure (ACI) which enable investigators to seamlessly and securely interact with information/data which is distributed across geographically disparate resources. This paper presents the development and evaluation of a novel content-based image retrieval (CBIR) framework. The methods were tested extensively using both peripheral blood smears and renal glomeruli specimens. The datasets and performance were evaluated by two pathologists to determine the concordance.ResultsThe CBIR algorithms that were developed can reliably retrieve the candidate image patches exhibiting intensity and morphological characteristics that are most similar to a given query image. The methods described in this paper are able to reliably discriminate among subtle staining differences and spatial pattern distributions. By integrating a newly developed dual-similarity relevance feedback module into the CBIR framework, the CBIR results were improved substantially. By aggregating the computational power of high performance computing (HPC) and cloud resources, we demonstrated that the method can be successfully executed in minutes on the Cloud compared to weeks using standard computers.ConclusionsIn this paper, we present a set of newly developed CBIR algorithms and validate them using two different pathology applications, which are regularly evaluated in the practice of pathology. Comparative experimental results demonstrate excellent performance throughout the course of a set of systematic studies. Additionally, we present and evaluate a framework to enable the execution of these algorithms across distributed resources. We show how parallel searching of content-wise similar images in the dataset significantly reduces the overall computational time to ensure the practical utility of the proposed CBIR algorithms.

Nuclear Factor-κB Modulation in Patients Undergoing Induction Chemotherapy for Acute Myelogenous Leukemia

Purpose: Nuclear factor-κB (NF-κB) is constitutively expressed in many acute myelogenous leukemia (AML) cells and AML stem cells. Ex vivo treatment of AML cells with inhibitors of NF-κB results in diminished AML cell survival and enhances the cytotoxic effects of chemotherapeutic agents. The purpose of this study was to determine if standard anti-inflammatory agents modulate AML cell nuclear NF-κB when administered in conjunction with induction chemotherapy. Experimental Design: Patients with newly diagnosed AML were treated with dexamethasone, choline magnesium trisalicylate, or both for 24 hours prior to and 24 hours following initiation of standard induction chemotherapy. AML cell nuclear NF-κB was measured at baseline, 24, and 48 hours. Results: Choline magnesium trisalicylate ± dexamethasone decreased nuclear NF-κB, whereas dexamethasone alone was associated with an increase in nuclear NF-κB in AML cells. Conclusions: These results show the feasibility of NF-κB modulation in conjunction with induction chemotherapy for patients with AML using inexpensive readily available medications. A follow-up study to determine the effects of NF-κB modulation on clinical end points is warranted.

A Semi-Automated Positioning System for Contact-Mode Atomic Force Microscopy (AFM)

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.