A Computer Tool for Aiding in Accurate Assessment of Indeterminate Lung Nodules
Lung cancer is the leading cause of cancer-related mortality. There is improved detection of early lung cancer with the wide utilization of low-dose computed tomography (LDCT) in screening for early lung cancer. However, the primary problem associated with CT is the high false-positive detections (~96%). This issue often leads to unpleasant and costly unintended consequences (e.g., follow-up scans and/or invasive biopsies). In this project, we propose to develop a novel computer tool to aid in the accurate assessment of indeterminate lung nodules. The goal is to accurately and efficiently quantify the potential risk of developing lung cancer and its future prognosis, thereby facilitating precise/personalized lung cancer screening and optimal patient management.
(1) Develop and optimize deep learning methods for lung nodule detection
(2) Develop and optimize deep learning methods for benign/malignance lung nodule classification
(3) Validate and optimize the algorithms with NLST data
Currently no collaborators