Tailoring the best treatments to cancer patients remains a highly important endeavor in the oncology field. However, personalized treatment courses are challenging to determine, and technologies or methods that can successfully be employed for precision oncology are lacking.
Nuclear medicine is the second largest source of medical radiation exposure to the general population after computed tomography imaging. Imaging modalities utilizing nuclear medicine produce a more detailed view of internal structure and function and are most commonly used to diagnose diseases such as heart disease, Alzheimer’s and brain disorders. They are used to visualize tumors, abscesses due to infection or abnormalities in abdominal organs.
About half of the per capita dose of radiation due to medical exposures is provided by computed tomography (CT) examinations. Approximately 80 million CTs are performed annually in the United States. CT scans most commonly look for internal bleeding or clots, abscesses due to infection, tumors and internal structures. Although CT provides great patient benefit, concerns exist about potential associated risks from radiation doses – especially in pediatric patients more sensitive to radiation.
Extracellular vesicles (EVs) are lipid bilayer-enclosed particles that are released from cells. EVs may contain proteins derived from their cells of origin with the potential as diagnostic biomarkers indicating the state of the cells when released. However, due to their small size (50-1000nm), the methods currently used to phenotype EVs have limited sensitivity and scale. A need exists for development of novel technologies improving EV detection and phenotyping.
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.
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.
Scientists at the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD) have developed a method implemented as pulse sequences and software to be used with magnetic resonance imaging (MRI) scanners and systems. This technology is available for licensing and commercial development. The method allows for measuring and mapping features of the bulk or average apparent diffusion coefficient (ADC) of water in tissue – aiding in stroke diagnosis and cancer therapy assessment.
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.
Medical imaging is an important resource for early diagnostic, detection, and effective treatment of cancers. However, the screening and review processes for radiologists have been shown to overlook a certain percentage of potentially cancerous image features. Such review errors may result in misdiagnosis and failure to identify tumors. These errors result from human fallibility, fatigue, and from the complexity of visual search required.
Many biological and clinical procedures require functional validation of a desired cell type. Current techniques to validate rely on various assays and methods, such as staining with dyes, antibodies, and nucleic acid probes, to assess stem cell health, death, proliferation, and functionality. These techniques potentially destroy stem cells and risk contaminating cells and cultures by exposing them to the environment; they are low-throughput and difficult to scale-up.