AI Software to Help Early Detection of Lung Cancer

A team of experts in lung cancer, machine learning and medical technology product development has come together to address a huge and growing problem in lung cancer diagnosis, the management of patients presenting with indeterminate pulmonary nodules. The researchers have developed the world’s first image-based decision support software for improving patient management and reducing unnecessary follow-up procedures.

EIT Health LUCINDA (Early Lung Cancer Diagnosis with Artificial Intelligence and Big Data) is a consortium of leading clinicians & hospitals in the UK, the Netherlands and Germany (Oxford University Hospital, the University Medical Center Groningen, Heidelberg University Hospital & ThoraxKlinik Heidelberg, and the University of Oxford) and Optellum (Oxford, UK), a high-tech start up. Optellum is developing the world’s first automated patient management and image-based risk stratification software for incidental and screen-detected nodules in Computed Tomography (CT). By using deep learning, the company aims to make significant improvements in lung cancer diagnosis and patient management from the current standard of care.

Early detection of lung cancer by a chest CT scan can dramatically improve survival rates by identifying pulmonary nodules, small opacities in the lung, typically less than 1 cm in size. Up to 30% of all patients scanned have such small nodules, although the vast majority is harmless and will not cause any problems to the patient. Unfortunately, radiologists find it difficult to determine if a nodule is cancerous, resulting in an indeterminate diagnosis, which requires up to two year follow-up imaging for monitoring growth. In some cases, additional biopsies and surgeries need to be performed in order to investigate nodules, which ultimately prove to be benign. Such additional procedures increase patient stress, create a risk of complications and burden healthcare system resources.

EIT Health’s expert-level decision support software can improve a doctor’s ability to correctly diagnose lung nodules. The software utilizes state-of-the-art deep learning to provide an objective risk score of nodule malignancy learned from a database of thousands of examples with known ground-truth diagnoses. The output enables clinicians to confidently stratify lung nodule patients earlier, potentially on the basis of only one or two scans.

“Our project consortium, comprising Europe’s leading institutes in lung cancer screening in Groningen and Heidelberg, along with experts in healthcare machine learning at Optellum and Oxford, is uniquely positioned to tackle this critically important problem,” said Prof. Gleeson (Oxford), co-author of the 2015 British Thoracic Society guidelines for the management of pulmonary nodules. “We believe that this system will improve patient care and reduce the burden of managing indeterminate lung nodules in both incidental and screening settings.”

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