Each year approximately 2 billion chest X-rays are performed globally. They are fast, noninvasive, and relatively inexpensive radiological examinations for front-line diagnostics in outpatient, emergency, or community settings.
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- But beyond the simplicity of CXR lies a secret weapon in the fight against lung cancer: artificial intelligence.
Be it serendipitous screening, opportunistic detection, or incidental identification, there is potential for AI incorporated into CXR to screen patients for disease when they are getting an unrelated medical examination.
- This could include the patient in the ER undergoing a CXR for suspected broken ribs after a fall, or an individual referred by their doctor for a CXR with suspected pneumonia. These people, without symptoms, may unknowingly have small yet growing pulmonary nodules.
AI can find these abnormalities and flag them to clinicians as a suspicious finding for further investigation.
- This has the potential to find nodules earlier, in the very early stages of lung cancer when it is easier to biopsy or treat.
Indeed, only 5.8% of eligible ex-smoking Americans undergo CT-based lung cancer screening.
- So the ability to cast the detection net wider through incidental pulmonary nodule detection has significant merits.
Early global studies into the power of AI for incidental pulmonary nodules (IPNs) shows exciting promise.
- The latest evidence shows one lung cancer detected for every 1,120 CXRs has major implications to diagnose and treat people earlier – and potentially save lives.
The qXR-LN chest X-ray AI algorithm from Qure.ai is raising the bar for incidental pulmonary nodule detection. In a retrospective study performed on missed or mislabelled US CXR data, qXR-LN achieved an impressive negative predictive value of 96% and an AUC score of 0.99 for detection of pulmonary nodules.
- By acting as a second pair of eyes for radiologists, qXR-LN can help detect subtle anatomical anomalies that may otherwise go unnoticed, particularly in asymptomatic patients.
The FDA-cleared solution serves as a crucial second reader, assisting in the review of chest radiographs on the frontal projection.
- In another multicenter study involving 40 sites from across the U.S., the qXR-LN algorithm demonstrated an impressive AUC of 94% for scan-level nodule detection, highlighting its potential to significantly impact patient outcomes by identifying early signs of lung cancer that can be easily missed.
The Takeaway
By harnessing the power of AI for opportunistic lung cancer surveillance, healthcare providers can adopt a proactive approach to early detection, without significant new investment, and ultimately improving patient survival rates.
