CT Image analysis to improve equity in lung cancer screening in Aotearoa

Lung cancer is the leading cause of cancer death in Aotearoa New Zealand, and Māori experience the greatest burden. Māori are more likely to develop lung cancer at a younger age, are over three times more likely to die from it, and often present at later stages of disease when treatment options are limited. Early detection through low-dose CT (LDCT) screening can reduce deaths by up to 24%, but we need to ensure screening tools are accurate and equitable for all New Zealanders.

Quantitative CT (QCT) provides detailed, reproducible measures of lung structure, such as density, airway thickness, vascular pruning, and tissue heterogeneity. Differences in these features between Māori and non-Māori may influence both lung cancer risk and the performance of AI models that are increasingly being used to detect lung nodules. Without accounting for these differences, screening programmes risk being less effective for Māori, reinforcing existing inequities.

This project will create the most comprehensive dataset of QCT biomarkers in Aotearoa by analysing hundreds of LDCT scans from Māori and non-Māori participants. Using automated image analysis tools, we will segment lung structures and extract a wide range of biomarkers, including novel measures like Quadtree Decomposition and pulmonary vessel volume.

By comparing results across populations, this research will identify structural signatures of inequity, improve understanding of lung biology, and ensure that future AI tools for lung cancer screening are fair and effective. Ultimately, this work will help shape a national lung cancer screening programme that delivers better, more equitable outcomes for Māori and all New Zealanders.

Undergraduate degree in engineering or health-related sciences. The ideal candidate will have bioengineering expertise, image analysis experience, and computational skills.

Page expires: 12 September 2026