Quality assurance and quantitative imaging biomarkers in low-dose CT lung cancer screening

AI integrations with lung cancer screening: Considerations in developing AI in a public health setting

Lung cancer screening implementation has led to expanded imaging of the chest in older, tobacco-exposed populations. Growing numbers of screening cases are also found to have CT-detectable emphysema or elevated levels of coronary calcium, indicating the presence of coronary artery disease. Early interventions based on these additional findings, especially with coronary calcium, are emerging and follow established protocols. Given the pace of diagnostic innovation and the potential public health impact, it is timely to review issues in developing useful chest CT screening infrastructure as chest CT screening will soon involve millions of participants worldwide. Lung cancer screening succeeds because it detects curable, early primary lung cancer by characterizing and measuring changes in non-calcified, lung nodules in the size-range from 3mm to 15 mm in diameter. Therefore, close attention to imaging methodology is essential to lung screening success and similar image quality issues are required for reliable quantitative characterization of early emphysema and coronary artery disease. Today's emergence of advanced image analysis using artificial intelligence (AI) is disrupting many aspects of medical imaging including chest CT screening. Given these emerging technological and volume trends, a major concern is how to balance the diverse needs of parties committed to building AI tools for precise, reproducible, and economical chest CT screening, while addressing the public health needs of screening participants receiving this service. A new consortium, the Alliance for Global Implementation of Lung and Cardiac Early Disease Detection and Treatment (AGILEDxRx) is committed to facilitate broad, equitable implementation of multi-disciplinary, high quality chest CT screening using advanced computational tools at accessible cost.

Action plan for an international imaging framework for implementation of global low-dose CT screening for lung cancer

Reduction in lung cancer mortality is achievable through low dose computed tomography (LDCT) screening in high-risk individuals. Many countries are progressing from local LDCT screening studies to national screening programs. Implementation of effective large-scale screening programs is complex and requires a multi-disciplinary approach. A recent overview of the technical aspects of implementing high quality LDCT for screening resulted from the inaugural international expert meeting of the Alliance for Global Implementation of Lung and Cardiac Early Disease Detection and Treatment (AGILE). This covers the most important aspects of the CT imaging process: standardisation in CT image acquisition and interpretation, CT protocol management, technology developments and minimal requirements, integration of lung cancer biomarkers, and the role of AI in CT lung nodule detection, segmentation, and classification, and related data security issues.

CT acquisition protocols for lung cancer screening-current landscape and the urgent need for consistency

KEY POINTS

Standardizing CT acquisition protocols reduces radiation exposure in lung cancer screening. Cross-continent collaboration will enhance understanding of diverse clinical practices. Survey results will inform future advancements in radiology sustainability efforts.

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.

Absolute ground truth-based validation of computer-aided nodule detection and volumetry in low-dose CT imaging

PURPOSE

To validate the performance of computer-aided detection (CAD) and volumetry software using an anthropomorphic phantom with a ground truth (GT) set of 3D-printed nodules.

METHODS

The Kyoto Kaguku Lungman phantom, containing 3D-printed solid nodules including six diameters (4 to 9 mm) and three morphologies (smooth, lobulated, spiculated), was scanned at varying CTDIvol levels (6.04, 1.54 and 0.20 mGy). Combinations of reconstruction algorithms (iterative and deep learning image reconstruction) and kernels (soft and hard) were applied. Detection, volumetry and density results recorded by a commercially available AI-based algorithm (AVIEW LCS + ) were compared to the absolute GT, which was determined through µCT scanning at 50 µm resolution. The associations between image acquisition parameters or nodule characteristics and accuracy of nodule detection and characterization were analyzed with chi square tests and multiple linear regression.

RESULTS

High levels of detection sensitivity and precision (minimal 83 % and 91 % respectively) were observed across all acquisitions. Neither reconstruction algorithm nor radiation dose showed significant associations with detection. Nodule diameter however showed a highly significant association with detection (p < 0.0001). Volumetric measurements for nodules > 6 mm were accurate within 10 % absolute range from volumeGT, regardless of dose and reconstruction. Nodule diameter and morphology are major determinants of volumetric accuracy (p < 0.001). Density assignment was not significantly influenced by any parameters.

CONCLUSIONS

Our study confirms the software's accurate performance in nodule volumetry, detection and density characterization with robustness for variations in CT imaging protocols. This study suggests the incorporation of similar phantom setups in quality assurance of CAD tools.

Contemporary issues in the implementation of lung cancer screening

Lung cancer screening with low-dose computed tomography can reduce death from lung cancer by 20-24% in high-risk smokers. National lung cancer screening programmes have been implemented in the USA and Korea and are being implemented in Europe, Canada and other countries. Lung cancer screening is a process, not a test. It requires an organised programmatic approach to replicate the lung cancer mortality reduction and safety of pivotal clinical trials. Cost-effectiveness of a screening programme is strongly influenced by screening sensitivity and specificity, age to stop screening, integration of smoking cessation intervention for current smokers, screening uptake, nodule management and treatment costs. Appropriate management of screen-detected lung nodules has significant implications for healthcare resource utilisation and minimising harm from radiation exposure related to imaging studies, invasive procedures and clinically significant distress. This review focuses on selected contemporary issues in the path to implement a cost-effective lung cancer screening at the population level. The future impact of emerging technologies such as deep learning and biomarkers are also discussed.

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation

PURPOSE

To improve outcomes for lung cancer through low-dose computed tomography (LDCT) early lung cancer detection. The International Association for the Study of Lung Cancer is developing the Early Lung Imaging Confederation (ELIC) to serve as an open-source, international, universally accessible environment to analyze large collections of quality-controlled LDCT images and associated biomedical data for research and routine screening care.

METHODS

ELIC is an international confederation that allows access to efficiently analyze large numbers of high-quality computed tomography (CT) images with associated de-identified clinical information without moving primary imaging/clinical or imaging data from its local or regional site of origin. Rather, ELIC uses a cloud-based infrastructure to distribute analysis tools to the local site of the stored imaging and clinical data, thereby allowing for research and quality studies to proceed in a vendor-neutral, collaborative environment. ELIC’s hub-and-spoke architecture will be deployed to permit analysis of CT images and associated data in a secure environment, without any requirement to reveal the data itself (ie, privacy protecting). Identifiable data remain under local control, so the resulting environment complies with national regulations and mitigates against privacy or data disclosure risk.

RESULTS

The goal of pilot experiments is to connect image collections of LDCT scans that can be accurately analyzed in a fashion to support a global network using methodologies that can be readily scaled to accrued databases of sufficient size to develop and validate robust quantitative imaging tools.

CONCLUSION

This initiative can rapidly accelerate improvements to the multidisciplinary management of early, curable lung cancer and other major thoracic diseases (eg, coronary artery disease and chronic obstructive pulmonary disease) visualized on a screening LDCT scan. The addition of a facile, quantitative CT scanner image quality conformance process is a unique step toward improving the reliability of clinical decision support with CT screening worldwide.

QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications

As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.

Implementation of lung cancer screening in Europe: challenges and potential solutions: summary of a multidisciplinary roundtable discussion

Recent randomised trials on screening with low-dose CT have shown important reductions in lung cancer (LC) mortality and have triggered international efforts to implement LC screening. Detection rates of stage I LC with volume CT approaching 70% have been demonstrated. In April 2019 ‘ESMO Open – Cancer Horizons’ convened a roundtable discussion on the challenges and potential solutions regarding the implementation of LC screening in Europe. The expert panel reviewed the current evidence for LC screening with low-dose CT and discussed the next steps, which are covered in this article. The panel concluded that national health policy groups in Europe should start to implement CT screening as adequate evidence is available. It was recognised that there are opportunities to improve the screening process through ‘Implementation Research Programmes’.