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A new app has been developed that is able to help doctors and radiographers identify cancerous nodules on lung X-ray images.
Scientists from the charity Cancer Research UK and the British Thoracic Society have launched the free iOS app - which can be accessed via iPhones and iPads and purchased via the App Store - to enable medical professionals to be able to see whether or not nodules that appear as white shadows on chest X-rays are cancerous.
The app features official guidelines on identifying and diagnosing the severity of nodules, as well as a calculator tool that is able to estimate how fast and how big a cancerous nodule will grow. As a result, doctors can quickly determine a patient's prognosis, allowing them to begin accessing the best treatment for their needs as soon as possible.
Additionally, the app will provide guidance on which symptoms mean patients are most likely to require further investigations before their condition can be correctly diagnosed, as not all of these white shadows turn out to be cancerous - some can be harmless.
British Thoracic Society member Professor David Baldwin, who is also a consultant respiratory physician at Nottingham University Hospitals, explained that the newly-developed app builds on the previous BTS Pulmonary Nodule Risk Prediction Calculator, combining it with expertise gathered from Cancer Research UK scientists to create one highly detailed resource.
As the new diagnostic tool can be carried around in a healthcare worker's pocket, it is significantly more accessible and portable than other pieces of machinery, meaning it has the potential to be very useful to radiographers in the future.
Dr Jodie Moffat, head of early diagnosis at Cancer Research UK, commented: "Moving beyond selfies, social media and playlists, this app is designed to support clinicians in their decision-making so we can help ensure the right people are getting the right tests at the right time.
"Using smartphones to put this knowledge into clinicians' hands will enable them to make the best possible choices more quickly and efficiently, helping to speed up the time it takes to diagnose lung cancer."
Written by Megan Smith
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