ERS/ESTS/ESTRO/ESR/ESTI/EFOMP statement on management of incidental findings from low dose CT screening for lung cancer

Pros and cons of reporting incidental findings in lung cancer screening

Incidental findings (IFs) are common in lung cancer screening (LCS). While the detection of some of these findings can lead to early diagnosis and treatment of clinically significant conditions, it also carries the risks of overdiagnosis and overtreatment, causing anxiety for patients and increased economic costs for health systems. Effective management of IFs requires a balanced approach guided by clear guidelines, standardized reporting, and participants-centered communication. As the field of LCS evolves, continued research and innovation will be essential in refining the strategies for managing IFs, ensuring that the benefits of screening are maximized while minimizing potential harm. Evidence-based guidelines on reporting and management of IFs, however, are still lacking. This narrative review explores the pros and cons of reporting IFs in LCS, focusing on key controversies. KEY POINTS: Reporting and managing incidental findings in lung cancer screening is largely debated. The detection of incidental findings can lead to early diagnosis of clinically significant conditions but carries the risks of overdiagnosis and overtreatment. A balance must be found to have a positive impact on the population while not placing a burden on healthcare systems.

Paradigm shift in early detection: Lung cancer screening to comprehensive CT screening

Large-scale lung cancer screening implementation combined with improvements in early detection techniques for three major tobacco-related diseases presents a rare opportunity to markedly improve population health outcomes for millions of people. Chest CT enables routine detection of early lung cancer as well as characterizing coronary calcium and detecting early emphysema in the course of lung cancer screening. Integrated preventive care centered on comprehensive chest CT screening has the potential to bring large benefits across co-morbid diseases with a common etiology. The current one-disease/ silo paradigm of medical practice is an obstacle to maximizing chest CT screening's benefits. The large potential for improved health outcomes across the world demands careful public health, quality assurance, and health policy considerations. A systematic analysis of imaging and health data from ongoing chest CT screening could accelerate this paradigm shift through sustained optimization of screening detection, quantitation and management for the three most lethal tobacco-related co-morbidities. To coordinate this effort to advance progress with implementing the full benefit of comprehensive chest CT screening, a new multi- disciplinary professional and advocacy consortium has been developed to foster collaboration to realize the future of multi-disease chest CT screening.

Repeatability of AI-based, automatic measurement of vertebral and cardiovascular imaging biomarkers in low-dose chest CT: the ImaLife cohort

OBJECTIVE

To evaluate the repeatability of AI-based automatic measurement of vertebral and cardiovascular markers on low-dose chest CT.

METHODS

We included participants of the population-based Imaging in Lifelines (ImaLife) study with low-dose chest CT at baseline and 3-4 month follow-up. An AI system (AI-Rad Companion chest CT prototype) performed automatic segmentation and quantification of vertebral height and density, aortic diameters, heart volume (cardiac chambers plus pericardial fat), and coronary artery calcium volume (CACV). A trained researcher visually checked segmentation accuracy. We evaluated the repeatability of adequate AI-based measurements at baseline and repeat scan using Intraclass Correlation Coefficient (ICC), relative differences, and change in CACV risk categorization, assuming no physiological change.

RESULTS

Overall, 632 participants (63 ± 11 years; 56.6% men) underwent short-term repeat CT (mean interval, 3.9 ± 1.8 months). Visual assessment showed adequate segmentation in both baseline and repeat scan for 98.7% of vertebral measurements, 80.1-99.4% of aortic measurements (except for the sinotubular junction (65.2%)), and 86.0% of CACV. For heart volume, 53.5% of segmentations were adequate at baseline and repeat scans. ICC for adequately segmented cases showed excellent agreement for all biomarkers (ICC > 0.9). Relative difference between baseline and repeat measurements was < 4% for vertebral and aortic measurements, 7.5% for heart volume, and 28.5% for CACV. There was high concordance in CACV risk categorization (81.2%).

CONCLUSION

In low-dose chest CT, segmentation accuracy of AI-based software was high for vertebral, aortic, and CACV evaluation and relatively low for heart volume. There was excellent repeatability of vertebral and aortic measurements and high concordance in overall CACV risk categorization.

KEY POINTS

Question Can AI algorithms for opportunistic screening in chest CT obtain an accurate and repeatable result when applied to multiple CT scans of the same participant? Findings Vertebral and aortic analysis showed accurate segmentation and excellent repeatability; coronary calcium segmentation was generally accurate but showed modest repeatability due to a non-electrocardiogram-triggered protocol. Clinical relevance Opportunistic screening for diseases outside the primary purpose of the CT scan is time-consuming. AI allows automated vertebral, aortic, and coronary artery calcium (CAC) assessment, with highly repeatable outcomes of vertebral and aortic biomarkers and high concordance in overall CAC categorization.

Opportunity and Opportunism in Artificial Intelligence-Powered Data Extraction: A Value-Centered Approach

Radiologists' traditional role in the diagnostic process is to respond to specific clinical questions and reduce uncertainty enough to permit treatment decisions to be made. This charge is rapidly evolving due to forces such as artificial intelligence (AI), big data (opportunistic imaging, imaging prognostication), and advanced diagnostic technologies. A new modernistic paradigm is emerging whereby radiologists, in conjunction with computer algorithms, will be tasked with extracting as much information from imaging data as possible, often without a specific clinical question being posed and independent of any stated clinical need. In addition, AI algorithms are increasingly able to predict long-term outcomes using data from seemingly normal examinations, enabling AI-assisted prognostication. As these algorithms become a standard component of radiology practice, the sheer amount of information they demand will increase the need for streamlined workflows, communication, and data management techniques. In addition, the provision of such information raises reimbursement, liability, and access issues. Guidelines will be needed to ensure that all patients have access to the benefits of this new technology and guarantee that mined data do not inadvertently create harm. In this Review, we discuss the challenges and opportunities relevant to radiologists in this changing landscape, with an emphasis on ensuring that radiologists provide high-value care.

[CT imaging of chronic obstructive pulmonary disease: What aspects and what role?]

Chronic obstructive pulmonary disease (COPD), commonly defined as irreversible airflow limitation, is associated with specific morphological changes involving all three parts of the lung, namely the bronchi, parenchyma and pulmonary vessels. In vivo imaging, with its ability to describe the different types of lung alterations and their regional distribution, helps to elucidate the relationship between lung structure and respiratory function. High-resolution computed tomography (CT) of the lung is the imaging modality best suited to assessing the pathological changes associated with airflow obstruction occurring in COPD. Over the last few decades, numerous studies have demonstrated the role of CT as a morphological and functional method conducive to the phenotyping of COPD patients. This review proposes to examine the data on CT imaging of COPD with a critical approach to recent data, and to determine the extent to which CT could be integrated into care or clinical research on patients with this/these disease(s).

Lung Cancer and Interstitial Lung Diseases

Lung cancer continues to be one of the leading causes of cancer-related death worldwide. There is evidence of a complex interplay between lung cancer and interstitial lung disease (ILD), affecting disease progression, management strategies, and patient outcomes. Both conditions develop as the result of common risk factors such as smoking, environmental exposures, and genetic predispositions. The presence of ILD poses diagnostic and therapeutic challenges in lung cancer management, including difficulties in interpreting radiological findings and increased susceptibility to treatment-related toxicities, such as acute exacerbation of ILD after surgery and pneumonitis after radiation therapy and immunotherapy. Moreover, due to the lack of large, phase III randomized controlled trials, the evidence-based therapeutic options for patients with ILDs and lung cancer remain limited. Antifibrotic treatment may help prevent pulmonary toxicity due to lung cancer treatment, but its effect is still unclear. Emerging diagnostic modalities and biomarkers and optimizing personalized treatment strategies are essential to improve outcomes in this patient population.

Lung cancer screening: where do we stand?

Lung cancer screening (LCS) programmes have emerged over recent years around the world. LCS programmes present differences in delivery, inclusion criteria and resource allocation. On a national scale, only a few LCS programmes have been fully established, but more are anticipated to follow. Evidence has shown that, in combination with a low-dose chest computed tomography scan, smoking cessation should be offered as part of a LCS programme for improved patient outcomes. Promising tools in LCS include further refined risk prediction models, the use of biomarkers, artificial intelligence and radiomics. However, these tools require further study and clinical validation is required prior to routine implementation.

Lung cancer: an update on the multidisciplinary approach from screening to palliative care

This issue of Breathe aims to provide a succinct overview of the current state of play in various aspects in thoracic oncology https://bit.ly/3XQexmp.