The World Health Organization’s cancer control framework emphasizes early detection, risk-based screening of defined high-risk cancer populations, and equitable access to diagnostic pathways and timely treatment, particularly in low- and middle-income countries (LMICs) where cancer mortality remains disproportionately high. For lung cancer, this includes populations at elevated risk due to age, environmental exposures, occupational hazards, and comorbidities many of whom fall outside traditional smoking-based screening criteria.
Back
In 2022, countries including India, Turkey, Mexico, Indonesia, and Egypt collectively reported more than 173,000 new lung cancer cases and approximately 153,700 lung cancer-related deaths, reflecting a striking mortality rate of nearly 89%. This imbalance underscores the urgent need for earlier and more targeted approaches to lung cancer detection, particularly those that can be deployed at scale in resource-constrained settings.
While low-dose CT (LDCT) is globally recognized as the gold standard for lung cancer screening, its large-scale adoption remains limited by cost, infrastructure requirements, radiation exposure concerns, and constrained clinical workforce capacity. As a result, chest radiographs (CXRs) continue to serve as the most widely used first-line imaging modality in routine clinical practice across LMICs. National screening and early detection programs therefore require scalable, cost-effective solutions that can optimize existing imaging pathways without placing additional strain on already burdened health systems.
Within this context, the CREATE study (NCT05817110) provides important real-world evidence on the role of AI-enabled analysis of chest radiographs for lung cancer risk stratification. Conducted across both resource-limited and resource-adequate settings, CREATE evaluated Qure.ai’s qXR-LNMS (Lung Nodule Malignancy Score) an AI-derived imaging biomarker designed to predict the likelihood of malignancy in pulmonary nodules detected on CXRs against radiologist assessment on LDCT.
The study met its primary endpoint, achieving a positive predictive value of 54.2% and a negative predictive value of 93.4% against predefined thresholds of 20% and 70%, respectively. These results demonstrate the utility of qXR-LNMS in differentiating benign and malignant incidental pulmonary nodules (IPNs) on chest radiographs across diverse healthcare settings.
Notably, among incidentally diagnosed lung cancer cases, nearly 72% were never-smokers and 39% were under the age of 55 populations that are typically excluded from conventional lung cancer screening programs. By applying AI to routine chest X-rays and longitudinally following patients, CREATE demonstrates a pragmatic approach to identifying higher-risk individuals earlier and directing them to LDCT only when clinically warranted. This enables more focused use of specialist resources, supports risk-based screening aligned with WHO guidance, and strengthens national lung cancer control programs by facilitating earlier diagnosis and stage shift. Phase II of the CREATE study is currently ongoing, with interim results presented at ESMO 2025.
