Deep learning to stratify lung nodules on annual follow-up CT
Authors: Marjolein A Heuvelmans, Matthijs Oudkerk
Journal: The Lancet Digital Health (2019)

The main goal of lung cancer screening is to identify early-stage lung cancer while preventing unnecessary workup for benign nodules. The major source of error in early lung cancer detection and lung cancer screening is through radiological analysis. Findings of two large randomised controlled trials in high-risk populations (the National Lung Screening Trial [NLST]1Aberle DR Adams AM et al.National Lung Screening Trial Research TeamReduced lung-cancer mortality with low-dose computed tomographic screening.N Engl J Med. 2011; 365: 395-409Crossref PubMed Scopus (6858) Google Scholar and the NELSON study2De Koning H Van Der Aalst C Ten Haaf K Oudkerk M PL02.05 Effects of volume CT lung cancer screening: mortality results of the NELSON randomised-controlled population based trial.J Thorac Oncol. 2018; 13: S185Summary Full Text Full Text PDF Google Scholar) have shown the positive effect of lung cancer screening by low-dose chest CT. With expected worldwide implementation of this screening method, the number of CT-detected lung nodules will increase greatly, because around half of people undergoing screening have at least one nodule.3Horeweg N van Rosmalen J Heuvelmans MA et al.Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening.Lancet Oncol. 2014; 15: 1332-1341Summary Full Text Full Text PDF PubMed Scopus (344) Google Scholar In the USA, nodules detected at screening are managed according to the Lung CT Screening Reporting & Data System (Lung-RADS), which is based on diameter at first detection and the increase in diameter at follow-up. A proposed European protocol4Oudkerk M Devaraj A Vliegenthart R et al.European position statement on lung cancer screening.Lancet Oncol. 2017; 18: e754-e766Summary Full Text Full Text PDF PubMed Scopus (346) Google Scholar is based on nodule volume at first detection and a combination of volume and volume doubling time (VDT) at follow-up. To estimate baseline lung cancer risk, risk calculators are available.5McWilliams A Tammemagi MC Mayo JR et al.Probability of cancer in pulmonary nodules detected on first screening CT.N Engl J Med. 2013; 369: 910-919Crossref PubMed Scopus (787) Google Scholar However, the probability of nodules detected at baseline being malignant differs from that of nodules detected at incidence screening. Baseline nodules might have been present for years whereas new nodules identified at incidence screening are relatively young and fast growing and possess a substantially higher cancer probability.6Walter JE Heuvelmans MA Ten Haaf K et al.Persisting new nodules in incidence rounds of the NELSON CT lung cancer screening study.Thorax. 2019; 74: 247-253Crossref PubMed Scopus (16) Google Scholar For new nodules, temporal characteristics (ie, growth or change in density) will provide information about lung cancer risk rather than spatial (size) characteristics. Therefore, new nodules are managed differently from baseline nodules in established guidelines.1Aberle DR Adams AM et al.National Lung Screening Trial Research TeamReduced lung-cancer mortality with low-dose computed tomographic screening.N Engl J Med. 2011; 365: 395-409Crossref PubMed Scopus (6858) Google Scholar, 4Oudkerk M Devaraj A Vliegenthart R et al.European position statement on lung cancer screening.Lancet Oncol. 2017; 18: e754-e766Summary Full Text Full Text PDF PubMed Scopus (346) Google Scholar In a lung cancer screening programme, a participant will receive one baseline screening, after which they will have up to 24 annual follow-up CTs. Therefore, accurate management of incident nodules will be crucial for the performance of screening, with more stringent volume threshold criteria.6Walter JE Heuvelmans MA Ten Haaf K et al.Persisting new nodules in incidence rounds of the NELSON CT lung cancer screening study.Thorax. 2019; 74: 247-253Crossref PubMed Scopus (16) Google Scholar Machine learning techniques for clinicians' support are augmenting 21st century health care with surprising force.7Topol EJ High-performance medicine: the convergence of human and artificial intelligence.Nat Med. 2019; 25: 44-56Crossref PubMed Scopus (1714) Google Scholar In The Lancet Digital Health, Peng Huang and colleagues8Huang P Lin CT Li Y et al.Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.Lancet Digital Health. 2019; (published online Oct 17)https://doi.org/10.1016/S2589-7500(19)30159-1Summary Full Text Full Text PDF Scopus (50) Google Scholar report a deep learning algorithm (termed DeepLR) for classification of screen-detected lung nodules in follow-up imaging, and they validate DeepLR in a large external dataset. With the DeepLR algorithm, which is available online, Huang and colleagues claim to outperform Lung-RADS and stand-alone diameter-based VDT for lung cancer prediction at follow-up CT, both in a training set (using data from NLST) and in a large external validation set. In a large-scale contribution to estimate lung cancer risk by deep learning based on images from subsequent CTs,9Ardila D Kiraly AP Bharadwaj S et al.End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.Nat Med. 2019; 25: 954-961Crossref PubMed Scopus (721) Google Scholar lung cancer risk estimation was restricted to 1-year post CT. Thus, those findings cannot be used to identify individuals who might benefit from a longer screening interval. Huang and colleagues' study adds value8Huang P Lin CT Li Y et al.Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.Lancet Digital Health. 2019; (published online Oct 17)https://doi.org/10.1016/S2589-7500(19)30159-1Summary Full Text Full Text PDF Scopus (50) Google Scholar because DeepLR can identify a low-risk group among their high-risk screening population who had only a 0·2% chance to develop lung cancer in the next 3 years. These individuals might, therefore, benefit from repeat screening after 2 years, or even 3 years, rather than the current recommendation for 1-year screening. The study findings confirm that their deep learning method, which was trained on time-dependent characteristics, outperforms a diameter-based nodule protocol in terms of lung cancer detection sensitivity. Before we begin using deep learning techniques as guidance for lung cancer risk estimation in clinical practice, it is important to realise the limitations. First, Huang and colleagues emphasise that DeepLR was trained using mainly baseline and annual screens from the NLST, but the performance of DeepLR on shorter or longer follow-up intervals is unknown. Second, VDT in Huang and colleagues' study was calculated based on manual diameter measurements, which has been shown to be unreliable in a previous study,10Heuvelmans MA Walter JE Vliegenthart R et al.Disagreement of diameter and volume measurements for pulmonary nodule size estimation in CT lung cancer screening.Thorax. 2018; 73: 779-781Crossref PubMed Scopus (44) Google Scholar suggesting that VDT should be based on nodule volume alone. Furthermore, VDT should never be used as a stand-alone procedure, as was done by Huang and colleagues, but always in combination with a nodule volume cutoff.6Walter JE Heuvelmans MA Ten Haaf K et al.Persisting new nodules in incidence rounds of the NELSON CT lung cancer screening study.Thorax. 2019; 74: 247-253Crossref PubMed Scopus (16) Google Scholar Moreover, training of DeepLR was time-dependent and, therefore, included annual nodule growth rate, which is comparable with a nodule's VDT. Thus, results for VDT in Huang and colleagues' study may not reflect the true VDT value and findings cannot be translated into clinical practice. What is the main message of Huang and colleagues' study of deep learning in lung cancer nodule stratification? In follow-up imaging of a lung nodule, temporal changes provide valuable additional information for lung cancer risk prediction to spatial characteristics, surpassing Lung-RADS. The time-dependent training of DeepLR resulted in a very high true-negative nodule rate, potentially identifying individuals who might benefit from repeat screening in 2 or 3 years, compared with the current 1-year recommendation. MAH and MO declare no competing interests. Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning methodDeepLR recognises patterns in both temporal and spatial changes and synergy among changes in nodule and non-nodule features. DeepLR scores could be used to accurately guide clinical management after the next scheduled repeat screening CT scan. Full-Text PDF Open Access