Imaging and Exosomal Genomics as an Early Identifier of Lung Cancer
Abstract
Lung cancer is the predominant form of cancer in the United States due to its high incidence, often escaping diagnoses at early stages. Around 234,000 new cases of lung cancer and over 154,000 Americans are estimated to die from this disease in 2018. Lung cancer screening by low dose computed tomography (LDCT) results in the detection of a significant number of small lung nodules. The majority of these nodules do not represent cancer; however, they cannot be discriminated based on the CT imaging alone. As a consequence, many patients will undergo unnecessary invasive procedures that could be avoided if we would have better tests to distinguish between malignant and benign nodules. Nanometer scale (30-150 nm) extracellular vesicles (EVs) that carry tumor specific proteins and nucleic acids in lung cancer patients blood have recently received substantial attention, as they may contain biomarkers that indicate disease state and progression. Our goal is to develop a model correlating imaging data with exosomal genomic biomarker profiles to discriminate benign from malignant nodules.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2021
- Accession Number
- AD1160526
Entities
People
- Daniel Kim
- Harmeet Bedi
- Jarett Rosenberg
- Mehmet Ozgün Ozen
- Sandy Napel
- Utkan Demirci
Organizations
- Stanford University