Evaluation of Lipid Poor Renal Masses with Magnetic Resonance Spectroscopy in Tuberous Sclerosis Complex

Abstract

The Research Project supported by this DOD Award investigates the potential of in vivo and ex vivo MRS in characterizing the metabolomic spectra of TSC-associated renal masses. In an effort to optimize our in vivo image acquisition protocol, we began by scanning healthy volunteers and have formalized our novel in vivo MRS protocol, which will be submitted for publication. Subsequently, our overall enrollment of TSC patients with lipid poor renal masses had been lower than expected. For this reason and in order to meet our goals of gaining more metabolomic data on renal masses, we expanded our subjects to include TSC patients with lipid rich masses, non-TSC patients with lipid poor and lipid rich renal masses. We continue to enroll patients and analyze our current and ongoing data. Our analysis cohort was also expanded with the utilization of our MGH Genitourinary Oncology Tumor Bank, including AML and other renal tumor tissue. Our ex vivo work from the tumor bank was presented virtually at the 2020 American Urological Association national meeting and was awarded Best Poster. The manuscript is currently under preparation. The findings from this study will help the TSC community gain an understanding of the metabolomics of AML and other renal tumors.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2020
Accession Number
AD1126835

Entities

People

  • Adam S. Feldman

Organizations

  • Massachusetts General Hospital

Tags

DTIC Thesaurus Topics

  • Biomedical Research
  • Bladder Cancer
  • Cancer
  • Carcinoma
  • Cell Physiological Processes
  • Department Of Defense
  • Health Services
  • Kidney Diseases
  • Magnetic Resonance
  • Mass Spectrometry
  • Medical Personnel
  • Neoplasms
  • New England
  • New York
  • Oncology
  • Penis
  • Personnel Management
  • Physicians
  • Prostate Cancer
  • Public Health
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  • United States

Fields of Study

  • Medicine
  • Physics

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  • Oncology
  • Oncology and Biomarker-Based Cancer Detection.