The earlier healthcare providers can detect lung cancer, the greater the opportunity to treat patients with curative intent. Today we announced a new strategic collaboration with Aidence to provide artificial intelligence (AI) software solutions to hospitals across Europe, helping to enable and increase diagnosis of lung cancer at earlier stages. The AI software will allow better detection and follow-up of incidental pulmonary nodules (IPNs) that sometimes can be cancerous.
IPNs are abnormal growths in the lungs found while investigating or diagnosing another health condition using imaging scans. The majority of IPNs are benign, but some are cancerous and can often be overlooked, misinterpreted, or not appropriately followed up for further testing.
Better identification of IPNs and appropriate follow-up once they are detected are necessary steps towards increasing the diagnosis of lung cancer at early stages. However, rates of follow-up are persistently low for these patients, with one recent study finding that only 36% of patients with an identified IPN received any subsequent follow-up.1
Our strategic collaboration with Aidence aims to enable and increase early-stage lung cancer detection through the implementation of two types of software at hospitals across Europe:
- Veye Lung Nodules – an AI solution that automatically detects IPNs from lung CT scans and provides information regarding the nodule’s type, size and growth. In addition to providing benefits for patients, Veye Lung Nodules AI supports the improvement of patient outcomes by detecting possible early-stage lung cancer and lowering the risk of misdiagnosis. It can also improve efficiency and quality of care by enabling faster detection, reporting information, reducing unnecessary follow-ups and acting as a ‘second pair of eyes’ for radiologists—all of which are associated with potential cost savings.
- Veye Clinic – an application that facilitates follow-up for patients with identified IPNs based on established clinical guidelines to ensure timely diagnosis and treatment.
We will fund a pilot phase of implementing these solutions in 2021, before expanding to a planned 25-30 European hospitals next year. An external Steering Committee has been established to support the collaboration which will include developing a protocol to optimise the early lung cancer detection early detection pathway through collaboration with scientific societies, establishing lung nodule clinics, supporting improved patient communication for follow-up and educating HCPs and patients.
Incidental pulmonary nodules are a major contributor to medical procedures that require follow up, and although the majority will not be cancerous, sufficient numbers of patients will have cancer detected in this way. A uniform approach and better characterisation of these nodules is vital for patients as well as for the economic use of healthcare.
The potential of AI to detect nodules and support clinical teams in determining the probability of cancer could help to save lives and to work more effectively. Alongside funding the implementation of this technology, a dedicated Steering Committee will develop a protocol to change behaviour and optimise the lung cancer care pathway, working closely with the radiology community. I am pleased to chair this project together with Prof Giorgio Scagliotti from the University of Turino
This partnership is a testament to our ongoing commitment to supporting healthcare systems globally in achieving better outcomes for people with lung cancer, including as a founding partner of the Lung Ambition Alliance, a collaboration between AstraZeneca, International Association for the Study of Lung Cancer, Guardant Health, and Global Lung Cancer Coalition. It also supports our wider strategy of safeguarding health systems worldwide from the significant impact of the COVID-19 pandemic and the potential impact of future health crises.
Through this collaboration with Aidence, we are also pleased to support the use of AI software that will empower healthcare and pharmaceutical professionals to deliver faster, more precise diagnostics and treatments.
To determine the success of the project, we will collect data on the number of actionable nodules found, lung cancers diagnosed and the stage of disease at diagnosis. The Steering Committee plans to gather, review and publish aggregated data periodically, to assess the impact of the software and act on initial learnings.
1. Pyenson BS, Bazell CM, Bellanich MJ, Caplen MA, Zulueta JJ. No Apparent Workup for most new Indeterminate Pulmonary Nodules in US Commercially-Insured Patients. JHEOR. 2019;6(3):118-129. doi:10.36469/9674