Global pharmaceutical company, AstraZeneca, has presented a recent study at the European Lung Cancer Congress 2025 highlighting the potential of Artificial Intelligence in improving lung cancer risk detection. It found that AI-integrated CXR could be the preliminary step before low-dose computed tomography (LDCT) in resource-limited settings to identify individuals who are at high risk. This could optimize the cost-effectiveness of the screening programs as well.
Lung Cancer remains the leading cause of cancer-related deaths globally. While LDCT is the standard treatment for lung cancer screening but its limited accessibility makes it challenging to implement in low and middle income countries. AstraZeneca’s commitment to address healthcare disparities is reflected in its collaboration with Qure.ai to enhance early detection of lung cancer.
Iskra Reic, Executive Vice President (EVP), International, AstraZeneca, said, “To transform patient care and work towards eliminating cancer as a cause of death, we must address the disproportionate burden in underserved regions through tailored and scalable solutions.” As a part of this study, AstraZeneca used qXR-LNMS tool by Qure.AI. This is an AI-driven system that automatically detects lung nodules and lymph node metastasis on chest X-rays. It provides real-world data to predict the risk of malignancy of incidental Pulmonary nodules on chest X-ray in comparison to radiologist’s assessment. qXR-LNMS utilizes AI to automatically detect lung nodules and lymph node metastasis. On the other hand, the current diagnostic approach manually analyzes and interprets chest X-rays.
When compared to the traditional methods for lung cancer detection, AI provides automated, faster and more accurate risk assessments. This new AI-driven imaging technology offers real-time, high-precision navigation for biopsy. The advanced imaging system is capable of improving diagnostic accuracy and enhance the precision of lung cancer treatment.
The information shared in this blog is for educational purposes only.
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