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Principal Investigator
Name
Michael Toth
Degrees
Ph.D.
Institution
University of Vermont
Position Title
Professor of Medicine
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-1152
Initial CDAS Request Approval
Oct 31, 2023
Title
Early Body Composition Adaptations in Lung Cancer Patients
Summary
Weight loss is a common symptom and side effect of cancer. A significant proportion of patients with lung cancer lose weight prior to diagnosis and a majority experience tissue loss during treatment, particularly in late-stage disease. Tissue wasting results from alterations in systemic metabolism instigated by the tumor to facilitate its growth and metastasis. The composition of the weight loss may be of therapeutic and prognostic significance, as the utilization of different energy substrates coming from adipose (fat) or muscle (amino acids) tissue may identify druggable metabolic pathways, stages of tumor growth, treatment resistance, and potential for metastasis. Our current understanding of cancer-related changes in body composition is based almost completely by studies conducted after diagnosis. In contrast, the tissue composition of weight loss prior to diagnosis of lung cancer, which could serve as an early biomarker of cancer and inform treatment strategies has not been rigorously examined. The goal of this project is to analyze low-dose, helical computed tomography (LDCT) scan data from the National Lung Screening Trial (NLST) using recently developed automated software to derive body composition data to characterize early, pre-diagnosis body composition changes in patients subsequently diagnosed with lung cancer.
Aims

Aim 1: To determine changes in body composition prior to lung cancer diagnosis in patients participating in the NLST.
Aim 2: To determine if changes in body composition prior to lung cancer diagnosis predict mortality n the NLST.

Collaborators

Bennett Landman, PhD – Vanderbilt University
Kim Sandler, MD – Vanderbilt University
Kaiwen Xu, PhD – Vanderbilt University