Multi-source data approach for personalized outcome prediction in lung cancer screening: update from the NELSON trial

Abstract

Trials show that low-dose computed tomography (CT) lung cancer screening in long-term (ex-)smokers reduces lung cancer mortality. However, many individuals were exposed to unnecessary diagnostic procedures. This project aims to improve the efficiency of lung cancer screening by identifying high-risk participants, and improving risk discrimination for nodules. This study is an extension of the Dutch-Belgian Randomized Lung Cancer Screening Trial, with a focus on personalized outcome prediction (NELSON-POP). New data will be added on genetics, air pollution, malignancy risk for lung nodules, and CT biomarkers beyond lung nodules (emphysema, coronary calcification, bone density, vertebral height and body composition). The roles of polygenic risk scores and air pollution in screen-detected lung cancer diagnosis and survival will be established. The association between the AI-based nodule malignancy score and lung cancer will be evaluated at baseline and incident screening rounds. The association of chest CT imaging biomarkers with outcomes will be established. Based on these results, multisource prediction models for pre-screening and post-baseline-screening participant selection and nodule management will be developed. The new models will be externally validated. We hypothesize that we can identify 15–20% participants with low-risk of lung cancer or short life expectancy and thus prevent ~140,000 Dutch individuals from being screened unnecessarily. We hypothesize that our models will improve the specificity of nodule management by 10% without loss of sensitivity as compared to assessment of nodule size/growth alone, and reduce unnecessary work-up by 40–50%.

The relationship of fat and muscle measurements with emphysema and bronchial wall thickening in smokers

“…The Medical Ethics Committee approved the ancillary study protocol, and written informed consent for side studies was obtained from all participants included in the present study. This study was conducted as part of the NELSON-POP project, which is an extension of NELSON [ 25 ].…”

Editorial: Using physical & genomics markers for smart therapy via expert systems with computer learning

“…The integration of physical and genomics markers is essential for smart therapy. This is achieved through expert systems with computer learning ( Chen et al, 2022 ; Deng et al, 2022 ; Ma et al, 2022 ; Chen et al, 2023 ; Sidorenkov et al, 2023 ).…”

“…Machine learning and whole-evidence analysis have facilitated comprehensive evaluation in cancer research. Studies focusing on lung cancer screening and hepatocellular carcinoma prognosis have enhanced screening and prognostic accuracy ( Deng et al, 2022 ; Sidorenkov et al, 2023 ). Additionally, deep learning-based systems have demonstrated expert-level accuracy in delineating head and neck lymph node levels, contributing to advancements in radiotherapy research ( Weissmann et al, 2023 ).…”