Accession Number:

AD1160526

Title:

Imaging and Exosomal Genomics as an Early Identifier of Lung Cancer

Descriptive Note:

[Technical Report, Annual Report]

Corporate Author:

LELAND STANFORD JUNIOR UNIV CALELAND STANFORD JUNIOR UNIV CA

Personal Author(s):

• Napel, Sandy
• Demirci, Utkan
• Bedi, Harmeet
• Ozen, Mehmet O
• Rosenberg, Jarett
• Kim, Daniel

Report Date:

2021-10-01

Pagination or Media Count:

26

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.

Descriptors:

• health services
• medical personnel
• machine learning
• information science
• neural networks
• artificial intelligence
• united states
• data science
• deep learning
• diagnostic imaging
• lung cancer
• neoplasms
• north america
• computational science
• diseases and disorders
• personalized medicine
• x-ray computed tomography

Subject Categories:

• Medicine and Medical Research
• Genetic Engineering and Molecular Biology
• Biochemistry

Distribution Statement:

[A, Approved For Public Release]