In vivo Evaluation of Lymph Nodes Using Quantitative Ultrasound

Weill Medical Coll Of Cornell Univ, New York NY

Investigators

Abstract

This application for an Administrative Supplement proposes a collaborative study to be performed entirely at Weill Cornell Medicine (WCM) in New York, NY. The parent project (R01CA277038) is a collaboration between WCM, GE Research in Niskayuna, NY, and Stony Brook Medicine (SBM) affiliated with the State University of New York in Stony Brook, NY. The parent project addresses the need for reliable, highly sensitive means of detecting metastases to lymph nodes (LNs) and distinguishing them from primary lymphomas and LNs affected by benign conditions. This capability will allow improved staging and treatment of disease. Accordingly, the project builds on encouraging results obtained in prior studies by WCM and SBM using quantitative-ultrasound (QUS) imaging methods to detect metastases in LNs by applying and evaluating these promising methods using a standard clinical scanner to acquire ultrasonic echo-signal data from patients undergoing medically required ultrasonically guided biopsies. The Administrative Supplement will expand the parent study to three additional WCM clinical sites. This strategic expansion will substantially enhance the generalizability of the parent study, which will enable us to characterize how QUS imaging performs among breast-cancer patients from various socio-economic groups, which often experience delays in cancer care. Using patients’ zip code data, we will link individual-level data to the American Community Survey to capture area-level factors such as poverty that are known to impact cancer care delivery. To investigate the potential of this novel technology to reduce delays in breast cancer care, we have assembled a multi-disciplinary team of scientists and clinicians across radiology, engineering, breast surgery, and health services research. The QUS data acquired at WCM will be used to determine whether QUS parameters are different among sub-groups of breast-cancer patients and whether QUS-based classification can be improved by including patient level data. The project will also investigate how QUS technology can reduce barriers to breast cancer care delivery (e.g., by reducing time to treatment initiation).