Translational Medical Research Commons^™

Contrast-Enhanced Endoscopic Ultrasound For Identification Of Sentinel Lymph Nodes In Esophageal Cancer, Bethanne Venkatesan, Ji-Bin Liu, Md, John R. Eisenbrey, Phd, Sriharsha Gummadi, Md, Thomas Kowalski, Md, Robert Coben, Md, Flemming Forsberg, Phd, Corinne Wessner, Priscilla Machado, Md, David Loren, Md

Phase 1

Introduction:

In esophageal carcinoma, lymph node involvement is a crucial aspect of nodal staging and determining treatment strategies. Although grayscale endoscopic ultrasound (EUS) is the standard of care for staging, it is unable to identify lymph node drainage from primary tumors or sentinel lymph nodes (SLN). The goal of this study was to determine if Contrast Enhanced Endoscopic Ultrasound (CE- EUS) is superior to EUS in the identification of SLNs and nodal staging in esophageal carcinoma.

Methods:

In the unblinded pilot study, patients with newly diagnosed esophageal carcinoma were recruited to undergo CE-EUS and standard EUS. EUS was performed …

Machine Learning By Ultrasonography For Risk Stratification Of Axillary Breast Lymph Nodes, Joshua Yu, John Eisenbrey, Phd, Aylin Tahmasebi, Md

Phase 1

Introduction: Breast cancer is the most common cancer among women worldwide, and ultrasonography has been an essential tool in the management of breast cancer. In order to improve upon ultrasonography efficacy, establishing a machine learning image analysis model would provide additional prognostic and diagnostic factors in the evaluation, monitoring, and treatment of breast cancer.

Methods: This retrospective study utilizes axillary lymph node ultrasound images of patients at Thomas Jefferson University Hospital who have had axillary lymph node biopsies. Automated machine learning of the images was performed on AutoML Vision; Google LLC which generated custom models for classification. About 80% of …