PROJECT

Detection of Metastases in Lymph Nodes

Detection of Metastases in Lymph Nodes

This project aims to use quantitative-ultrasound (QUS) methods for detecting metastases in lymph nodes in a broad range of cancer types. This project is a NIH-funded Academic-Industrial Partnership consisting of Riverside Research of New York, NY as the primary partner in collaboration with GE Research of Niskayuna, NY, as the industrial partner. Our clinical/academic partners include Stony Brook Medicine affiliated with the State University of New York at Stony Brook, NY, and Kuakini Medical Center affiliated with the University of Hawaii in Honolulu, HI. 

The proposed project will implement these QUS methods in a modified clinical scanner, such as the GE LOGIQ series, for efficient detection and typing of lymph nodes in intact surgically excised specimens and in patients. This project will hopefully lead to clinical instrumentation for lymph-node detection and characterization in surgically excised specimens in pathology laboratories and in patients in examination and operating rooms. 

The key health-care benefits will be more-efficient and accurate staging of cancer and, as a result, improved disease treatment and patient management.  This work has been funded under NIH contract NIH 1R01CA192407.

Project Impact

Reliable detection of lymph nodes in excised specimens and in patients and accurate determination of the presence or absence of metastases to the nodes is essential for proper staging and treatment of cancer. Current methods can fail to detect and properly characterize lymph nodes. We will develop ultrasonic-imaging instrumentation for node detection and metastasis assessment in excised specimens and patients.

  • Our Expertise

    Capabilities utilized for Detection of Metastases in Lymph Nodes project

  • Ultrasound Imaging

    The physics of ultrasound signal generation,  propagation and detection

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  • Biomedical Engineering

    The intersection of human physiology, electronics, chemistry and optics

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  • Computer Vision

    Performing fundamental and applied research to mimic human visualization and interpretation

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