Automated Cancer Diagnostic Tool of Detecting, Quantifying and Mapping Mitotically-Active Proliferative Cells in Tumor Tissue Histopathology Whole-Slide Images

Cancer diagnosis is based on the assessment of patient biopsies to determine the tumor type, grade, and stage of malignancy. The proliferative potential of tumors correlates to their growth and metastasis. Visually identifying and quantifying mitotic figures (MF) in cancer biopsy tissue can be used as a surrogate for proliferative activity in tumors.

Mitotic Figures Electronic Counting Application for Surgical Pathology

Cancer diagnosis depends on the assessment of patient biopsies to determine tumor type, grading, and stage of malignancy. Pathologists visually review specimens and count mitotic figures (MF) in a variety of cancer types to help gauge aggressiveness, guide treatment, and inform patient prognosis. Current technology for recording MF counts in surgical pathology is lacking in objectivity, and enumeration of MF by microscopy can be error prone. In particular, a lack of systematic means for recording contributes to recognized variability.

A Preclinical Model for Mutant Human EGFR-driven Lung Adenocarcinoma

Previously described epidermal growth factor receptor- (EGFR) driven tumor mouse models develop diffuse tumors, which are dissimilar to human lung tumor morphology and difficult to measure by CT and MRI scans. Scientists at the National Cancer Institute (NCI) have developed and characterized a genetically engineered mouse (GEM) model of human EGFR-driven tumor model (hEGFR-TL) that recapitulates the discrete lung tumor nodules similar to those found in human lung tumor morphology.

New Insect Sf9-ET Cell Line for Determining Baculovirus Titers

The baculovirus-based protein expression system has gained increased prominence as a method for expressing recombinant proteins that are used in a wide range of biomedical applications. An important step in the use of this system is the ability to determine the virus infectious titer, i.e., the number of active baculovirus particles produced during an infection of the insect host cell.

A Specialized Tissue Collection Device for the Preservation and Transportation of Needle Biopsies

The ability to hold and transport tissue, especially needle biopsies in a pre-defined and controlled environment is critical for the preservation of biopsy samples in downstream analytic applications. Currently, tissue specimens are placed in open containers with variable, poorly controlled solutions applied to them, often in less than sterile conditions.  Evaluation of the tissue by examination through a stereoscope or similar approaches to determine adequacy is limited and requires manipulation of the tissue that can further damage the tissue.

Device for Simulating Explosive Blast and Imaging Biological Specimens

Traumatic brain injury (TBI) is a major health problem.  Between 3.2 and 5.3 million people live with long-term disabilities resulting from TBI, and thus, contribute to the need to develop therapies that treat TBI-induced cellular damage. Researchers at the National Institute of Child Health and Human Development (NICHD) have developed a device that simulates the pressure waves resulting from explosions.

AngleNav: Micro-Electro-Mechanical Systems (MEMs) Trackers to Facilitate Computed Topography (CT)-Guided Needle Puncture

Conventional free-hand needle puncture procedures for biopsy and other procedures, often rely on unguided manual movements to guide a needle to its destination. Freehand procedures risk missing the tumor, or accidental injury, such as puncturing a vital organ. Needle guidance systems may improve accuracy and reduce risks but available guidance technologies are cumbersome and expensive and may carry other risks.

Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation

Medical image datasets are an important clinical resource. Effectively referencing patient images against similar related images and case histories can inform and produce better treatment outcomes. Labeling and identifying disease features and relations between images within a large image database has not been a task capable of automation. Rather, it is a task that must be performed by highly trained clinicians who can identify and label the medically meaningful image features.