Lung Cancer: AI Can Reduce False Positives on Low-Dose CT Screening Interview with:

Panayiotis (Takis) Benos, Ph.D.Professor and Vice Chair for Academic AffairsDepartment of Computational and Systems BiologyAssociate Director, Integrative Systems Biology ProgramDepartment of Computational and Systems Biology, SOM andDepartments of Biomedical Informatics and Computer ScienceUniversity of Pittsburgh

Dr. Benos

Panayiotis (Takis) Benos, Ph.D.
Professor and Vice Chair for Academic Affairs
Department of Computational and Systems Biology
Associate Director, Integrative Systems Biology Program
Department of Computational and Systems Biology, SOM and
Departments of Biomedical Informatics and Computer Science
University of Pittsburgh What is the background for this study? What are the main findings?

Response: Low-dose computed tomography (LDCT) scans is the main method used for early lung cancer diagnosis.  Early lung cancer diagnosis significantly reduces mortality.  LDCT scans identify nodules in the lungs of 24% of the people in the high-risk population, but 96% of these nodules are benign.  Currently there is no accurate way to discriminate benign from malignant nodules and hence all people with identified nodules are subjected to follow up screens or biopsies.  This increases healthcare costs and creates more anxiety for these individuals.  By analyzing a compendium of low-dose computed tomography scan data together with demographics and other clinical variables we were able to develop a predictor that offers a promising solution to this problem. 

Continue reading