To develop an AI model for predicting future lung cancer in low-dose screening CT

Screening with low-dose CT has been shown to significantly reduce lung cancer related mortality in high-risk ever-smokers. Interval cancer (IC) is a rising challenge in lung cancer screening because it usually presents in an advanced stage (stage III/IV non-small cell cancer) or is more biologically aggressive (i.e., small cell histology) and have a poorer prognosis than prevalent cancers. The dilemma is how to catch IC early because the regularly scheduled follow-up CT is often too late. We propose that artificial intelligence (AI) tools can identify sub-visual changes in the “normal” lung before a clinically detectable IC develops. We have access to three population-based screening datasets with ICs. Using the prior CT(s) before a diagnosed IC or benign nodule develops, we propose to build an AI algorithm that can distinguish a pre-IC “negative” CT from CTs of subjects that will remain negative. We will use this information to guide the follow-up interval for individual subjects. This AI tool has the potential to standardize the triage process, enable personalized follow-up intervals for high-risk individuals, detect IC earlier, and improve the outcome and cost-efficiency of lung cancer screening.

Faculty Supervisor:

Calum MacAulay

Student:

Partner:

BC Cancer

Discipline:

Physics

Sector:

Health and Related Sciences & Technology; Professional, scientific and technical services

University:

The University of British Columbia

Program: