Zhe Li

Ph.D.

Singlera Genomics Inc.

Project Scientist

NLST (Learn more about this study)

NLST-621

Dec 16, 2019

Integrative analysis to predict lung nodule classification

Lung cancer is one of the most common forms of cancer and is responsible for approximately 1.8 million deaths per year worldwide. The current 5-year survival rate for lung cancer is only 18%; however, this improves to 56% if the cancer is detected early. While low-dose computed tomography (CT) scans have shown promise as an early detection method, only 16% of lung cancer is currently detected at an early stage. We therefore sought to develop a non-invasive blood-based screening assay to identify lung cancer at an early stage using methylated circulating tumor DNA (ctmDNA), and then integrate the results with AI-assisted medical imaging analysis technology on low dose CT scans, with the goal to provide diagnostic evaluation and classification of pulmonary nodules to detect cancer early.

Aim 1: using non-invasive blood-based screening assay to extract methylated circulating tumor DNA (ctmDNA) and predict lung nodule classification

Aim 2: AI-assisted medical imaging analysis technology on low dose CT scans to measure changes in nodule metrics and predict lung nodule classification

Aim 3: integrate results from both approaches to adjust the computational model to provide diagnostic evaluation and classification of pulmonary nodules