Thursday, May 2, 2024
5/2/2024
Lung cancer has a 5-year survival rate of 21% and more than 80% of all new patients are diagnosed at an advanced stage. Finding small lung nodules representing the early stages of lung cancer are critical, but diagnostic error can be as high as 50% in harder-to-detect lung nodules. Nodule detection is the outcome of a difficult search task which employs both conscious and non-conscious brain processes that radiologists spend years of training enhancing. Currently, detection is constrained to those processes that become conscious. A critical need is characterizing and utilizing these non-conscious processes to improve nodule detection beyond conscious detection limits. This proposed RO1 will use an innovative new paradigm to isolate non-conscious processes during lung nodule searches in CT images. Using eye-tracking, the project will show clear and reliable biomarkers of non-conscious detection for “missed” nodules in the absence of any conscious detection or consideration of the nodule. These biomarkers will be used to train Machine Learning (ML) to detect “missed” nodules. The innovation of this application is capitalizing on the full expertise of the radiologist by utilizing these biomarkers of non-conscious detection to develop ML to read the radiologist and not the image; disrupting the status quo of ML in radiology, and creating ML that can detect “missed” nodules. The central hypothesis will be tested in three Specific Aims: 1: To evaluate the extent to which identified biomarkers of non-conscious detection of missed nodules can be used to train and refine ML models to increase nodule detection; 2: To quantify the extent that feedback of the locations that ML models indicate are missed nodules can increase nodule detection; and 3: To specify the extent that trained ML models can generalize to a novel set of radiologists on a novel set of chest CTs to detect missed lung nodules and increase nodule detection. These Aims will be carried out by testing radiologist on lung nodule searches in CT images using high-speed eye- tracking and our innovative paradigm that allow us to isolate non-conscious processes during misses and demonstrate that non-conscious processes are successfully detecting the “missed” nodules. ML models will be trained on these non-conscious biomarkers to detect “missed” lung nodules. The ML models will provide significant feedback to the radiologists to reduce the number of nodules missed by the limits of conscious detection. The proposed research is significant because it is expected to provide strong scientific justification for the use of non-conscious processes in diagnostic visual search, and to create ML models capable of detecting otherwise “missed” lung nodules; hence, changing clinical practice, reducing nodule misses, improving early detection, and increasing lung cancer's 5-year survival rate.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
