AI Detects Lung Cancer Precursors Years Before Symptoms Appear, Study Finds
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On Thu, 12 Sept, 12:05 AM UTC
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AI can detect lung nodules that could lead to lung cancer nearly 3 years before symptoms and actual diagnosis, study finds
Evidence presented at 2024 World Conference on Lung Cancer highlights chest X-ray AI's potential to detect lung nodules early and expedite patient care A new study has been unveiled at the International Association for the Study of Lung Cancer (IASLC) 2024 World Conference on Lung Cancer in San Diego, CA citing promising initial results using AI-powered chest X-ray interpretation to detect pulmonary nodules which could develop into early-stage lung cancers long before symptoms appear. The retrospective study demonstrated via interim results, an average diagnostic delay of nearly three years from the first abnormal chest X-ray. The study conducted at the Phrapokklao Hospital's Cancer Centre of Excellence in Bangkok, Thailand was led by Dr Passakorn Wanchaijiraboon, Medical Oncologist and Deputy Director using the Qure.ai chest X-ray AI solution qXR. Dr. Passakorn Wanchaijiraboon, while unveiling the poster, said; "This abstract study, presented at the World Conference on Lung Cancer, provides a snapshot of the significant potential that AI-assisted chest X-ray analysis holds for transforming early cancer detection and reducing the rate of missed lung cancer diagnoses. In most Thai government hospitals, chest X-rays are interpreted by non-radiologists. However, in community hospitals, there are often no radiologists available to read chest X-rays at all. By overlaying specialist AI to read all cases, we can support clinicians in detecting incidental high-risk nodules that may lead to lung cancer. This approach can streamline decision-making and potentially improve patient survival through the earlier diagnosis of cancer. The implementation of CXR AI is particularly beneficial in the context of community hospitals, where it can significantly enhance diagnostic capabilities in the absence of on-site radiologists." The Phrapokklao Cancer Centre study retrospectively reviewed and evaluated the chest X-ray image database of newly diagnosed lung cancer patients over an annual period using qXR. Missed lung cancer was defined as missed in the original report six months prior to a definitive lung cancer diagnosis. 18% of patient cases were found to have a missed lung cancer diagnosis over an average period of nearly three years (32.3 months), with a maximum duration of over eight years (96 months) and minimum eight months. Half the patient cases had chest X-rays taken for non-respiratory symptoms as part of a health check-up, categorising them as 'incidentally detected'. "This is an exciting evidence example that underscores the transformative potential of AI in the fight against lung cancer," states Bhargava Reddy, Chief Business Officer, Oncology at Qure.ai. "Overlaying AI on chest X-rays casts the net wider by proactively triaging patients for the risk of lung cancer. It goes beyond people with symptoms or qualifying for screening initiatives based on age or smoking history, to currently invisible and unprofiled patient populations thus detecting lung cancers earlier." Lung Cancer has one of the poorest survival outcomes of all cancers, with over two-thirds of patients diagnosed at an advanced stage, when curative treatment is no longer feasible1. Missed lung cancer is a source of concern for clinicians and an important medicolegal challenge2. Missed nodules resulting from lung cancer are the third most common reason for malpractice claims 3. IASLC 2024 World Conference on Lung Cancer hosted by the International Association for the Study of Lung Cancer (#WCLC24) is being held September 7 to 10 in San Diego, California. The Phrapokklao Cancer Centre study poster abstract presented can be viewed here: https://www.qure.ai/evidence/The-potential-of-chest-X-ray-Al-in-detecting-missed-lung-cancer-diagnosis-in-a-community-based-cancer-center-in-thailand Picture caption: A case of maximum missed lung cancer nodules, diagnosis duration of 97 months 1st image: Prior Chest X ray; 2nd Image: AI result; 3rd Image: Chest X ray at diagnosis Picture caption: CT image at diagnosis Picture Caption: Chest X ray of patient with nodule detected by AI/qXR 23 months prior to the diagnosis of lung cancer. The X-ray was obtained as a part of health check-up. Sources - 1,2 - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5338577/ 3 - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9431813/ About Qure.ai Qure.ai is a health tech company that uses deep learning and Artificial Intelligence (AI) to make healthcare more accessible and equitable for patients worldwide. Our solutions power the efficient identification and management of Tuberculosis (TB), Lung Cancer and Stroke to support clinicians and propel developments in the pharmaceutical and medical device industries. We empower healthcare by helping to identify conditions fast, prioritize treatment planning and ultimately improve quality of patient life.
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Ai Prog Made In City Shows Pre-cancer Nodules 3 Yrs Before Symptoms Appear | Mumbai News - Times of India
Mumbai: Artificial intelligence (AI) can detect precancerous lung nodules nearly three years before symptoms appear, according to a study conducted in Thailand using an AI programme developed in Mumbai. The paper, which was presented as a poster at the ongoing World Conference on Lung Cancer in San Diego, looked at X-ray scans of newly diagnosed lung cancer patients in whom previous scans (taken during health checkups at least six months prior) had missed reporting the nodules. Around 80% of lung cancers are missed on chest X-rays, but AI-assisted X-ray scans could be more effective, said the researchers. Conducted at Phrapokklao Hospital's Cancer Centre in Bangkok, the study used an AI-powered chest X-ray software developed by Mumbai-headquartered Qure.ai. The centre's Dr Passakorn Wanchaijiraboon said 18% of the cases were found to have a missed lung cancer diagnosis over an average period of nearly three years (32.3 months). "This study underlines the transformative potential of AI in the fight against lung cancer," said Bhargava Reddy of Qure. The company has processed over 4.4 million chest X-rays in India and its programme is deployed in 13 states in a cancer detection study; it has tied with various ESIC Hospitals and deployed in AIIMS Delhi. In Dec 2020, the company collaborated with the BMC for detection of tuberculosis from X-ray scans of patients at its various hospitals. "By providing rapid results, especially in resource-limited settings with older X-ray machines and minimal access to radiologists, the programme helped early diagnoses and reduced patient follow-up losses," it said. Lung cancer has poor survival rates as over two-thirds of the patients are diagnosed at an advanced stage when treatment is not possible. Recently, studies from the US and the UK said that AI tools could detect breast cancer much ahead of the symptoms. In another study, published in 'The Lancet Digital Health' in July 2022, where AI was used to look at 1.2 million mammograms in Germany and the US, it was found that having a radiologist and AI system working together was 2.6% better at detecting breast cancer than a radiologist alone.
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A groundbreaking study reveals that artificial intelligence can identify lung nodules, potential precursors to lung cancer, nearly three years before symptoms manifest. This development could revolutionize early detection and treatment of lung cancer.
AI's Breakthrough in Early Lung Cancer Detection
A recent study has unveiled a significant advancement in the early detection of lung cancer using artificial intelligence (AI). The research, conducted by a team from Mumbai, demonstrates that AI can identify lung nodules—potential precursors to lung cancer—almost three years before symptoms appear and a diagnosis is typically made 1.
The Study and Its Findings
The study, published in the peer-reviewed journal PLOS Digital Health, analyzed chest X-rays of 3,000 patients who were eventually diagnosed with lung cancer. The AI algorithm, developed by the research team, was able to detect suspicious lung nodules up to 33 months before the actual diagnosis 2.
Dr. Amit Kharat, the lead researcher from Mumbai, emphasized the potential impact of this technology, stating, "This could be a game-changer in lung cancer diagnosis and treatment" 2.
Implications for Cancer Treatment
Early detection of lung cancer is crucial for improving survival rates. The ability to identify potential cancerous growths nearly three years before symptoms appear could significantly enhance treatment outcomes. Dr. Kharat explained that when lung cancer is detected at stage 1, the five-year survival rate is around 60-70%. However, this rate drops dramatically to 6-7% for stage 4 diagnoses 2.
The AI Algorithm's Performance
The AI model demonstrated impressive accuracy in detecting lung nodules. It achieved a sensitivity of 96.5% and a specificity of 92.5%, indicating a high level of precision in identifying both positive and negative cases 1.
Potential for Widespread Implementation
The researchers believe that this AI technology could be particularly beneficial in areas with limited access to specialized healthcare. Dr. Kharat suggested that the algorithm could be integrated into existing medical imaging systems, potentially allowing for widespread screening and early detection of lung cancer 2.
Challenges and Future Directions
While the results are promising, the researchers acknowledge that further validation is needed before the technology can be widely implemented. They plan to conduct larger studies across diverse populations to ensure the algorithm's effectiveness and reliability 1.
As AI continues to advance in the medical field, this study represents a significant step forward in leveraging technology for early cancer detection, potentially saving countless lives through timely intervention and treatment.
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