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The Dutch Belgian NELSON trial has recently demonstrated that low-dose CT screening leads to a 24% reduction in lung cancer mortality amongst long-term smokers. However, if a cohort were invited for screening based on age and long-term smoking (like in NELSON), many individuals would not benefit. Furthermore, many screen-detected lung nodules lead to extra investigations while most nodules are benign. Better methods are needed for stricter selection of screenees who will benefit from screening, and for improved nodule stratification.
The Dutch Cancer Society has awarded the consortium project ‘Multi-source data approach for Personalized Outcome Prediction in lung cancer screening (NELSON-POP)’ with a grant of 1,425,000 Euro. In this consortium, the unique expertise and data from the various NELSON sites and associated research groups are combined to leverage various unexplored data sources, in order to identify the factors most predictive of lung cancer. Fundamental knowledge will be gained about the predictive power of data sources and biomarkers that can be derived for NELSON participants, namely genetic and environmental data, lung nodule risk data and chest CT biomarkers.
The consortium project is a public-private partnership and will be led by Rozemarijn Vliegenthart (University Medical Center Groningen) and brings the expertise of University Medical Center Groningen, Erasmus MC, UMC Utrecht, KU Leuven, Radboud UMC and MUMC+ together. This project is co-funded by Siemens Healthineers.
From Radboudumc, the department of Medical Imaging is involved in the consortium project. Colin Jacobs (Medical Imaging) will lead the work package on using artificial intelligence to accurately determine the cancer probability of lung nodules, and subsequently develop optimized nodule management protocols.
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