The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Open-Source Deep Learning and Quantitative Measurement Initiative.

INTRODUCTION: With global adoption of computed tomography (CT) lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open-source, cloud-based, globally distributed, screening CT imaging data set and computational environment that are compliant with the most stringent international privacy regulations that also protect the intellectual properties of researchers, the International Association for the Study of Lung Cancer sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be used for clinically relevant AI research. METHODS: In this second phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans. RESULTS: A total of 1394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness more than or equal to 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high-quality CT scans. CONCLUSIONS: These initial experiments revealed that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based data sets.

Quantitative imaging workshop XIX: Utilizing quantitative thoracic imaging to optimize population health final summary.

Lung cancer screening involves the use of thoracic CT for both detection and measurements of suspicious lung nodules to guide the screening management. Since lung cancer screening eligibility typically requires age over 50 years along with >20 pack-year tobacco exposure, thoracic CT scans also frequently reveal evidence for pulmonary emphysema as well as coronary artery calcification. These three thoracic diseases are collectively three of the leading causes of premature death across the world. Screening for the major thoracic diseases in this heavily tobacco-exposed cohort is broadening the focus of lung cancer screening to a more comprehensive health evaluation including discussing the relevance of screen-detected findings of the heart and lung parenchyma. The status and implications of these emerging issues were reviewed in a multidisciplinary workshop focused on the process of quantitative imaging in the lung cancer screening setting to guide the evolution of this important new area of public health.

Thoracic CT screening: using routinely detectable COPD information.

Efforts to collect thoracic CT images with standardized quality from individuals undergoing longitudinal lung cancer screening have been highlighted as an important opportunity to increase the yield of crucial clinical information obtainable to advance the public health benefits of lung cancer screening.

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.

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.

From clinical specimens to human cancer preclinical models-a journey the NCI-cell line database-25 years later.

The intramural the National Cancer Institute (NCI) and more recently the University of Texas Southwestern Medical Center with many different collaborators comprised a complex, multi-disciplinary team that collaborated to generated large, comprehensively annotated, cell-line related research resources which includes associated clinical, and molecular characterization data. This material has been shared in an anonymized fashion to accelerate progress in overcoming lung cancer, the leading cause of cancer death across the world. However, this cell line collection also includes a range of other cancers derived from patient-donated specimens that have been remarkably valuable for other types of cancer and disease research. A comprehensive analysis conducted by the NCI Center for Research Strategy of the 278 cell lines reported in the original Journal of Cellular Biochemistry Supplement, documents that these cell lines and related products have since been used in more than 14 000 grants, and 33 207 published scientific reports. This has resulted in over 1.2 million citations using at least one cell line. Many publications involve the use of more than one cell line, to understand the value of the resource collectively rather than individually; this method has resulted in 2.9 million citations. In addition, these cell lines have been linked to 422 clinical trials and cited by 4700 patents through publications. For lung cancer alone, the cell lines have been used in the research cited in the development of over 70 National Comprehensive Cancer Network clinical guidelines. Finally, it must be underscored again, that patient altruism enabled the availability of this invaluable research resource.

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

After years of assessment through controlled clinical trials, low-dose CT screening for lung cancer is becoming part of clinical practice. As with any cancer screening test, those undergoing lung cancer screening are not being evaluated for concerning signs or symptoms, but are generally in good health and proactively trying to prevent premature death. Given the resultant obligation to achieve the screening aim of early diagnosis while also minimizing the potential for morbidity from workup of indeterminate but ultimately benign screening abnormalities, careful implementation of screening with conformance to currently recognized best practices and a focus on quality assurance is essential. In this review, we address the importance of each component of the screening process to optimize the effectiveness of CT screening, discussing options for quality assurance at each step. We also discuss the potential added advantages, quality assurance requirements and current status of quantitative imaging biomarkers related to lung cancer screening. Finally, we highlight suggestions for improvements and needs for further evidence in evaluating the performance of CT screening as it transitions from the research trial setting into daily clinical practice.

Moonshot Acceleration Factor: Medical Imaging.

Medical imaging is essential to screening, early diagnosis, and monitoring responses to cancer treatments and, when used with other diagnostics, provides guidance for clinicians in choosing the most effective patient management plan that maximizes survivorship and quality of life. At a gathering of agency officials, patient advocacy organizations, industry/professional stakeholder groups, and clinical/basic science academicians, recommendations were made on why and how one should build a "cancer knowledge network" that includes imaging. Steps to accelerate the translation and clinical adoption of cancer discoveries to meet the goals of the Cancer Moonshot include harnessing computational power and architectures, developing data sharing policies, and standardizing medical imaging and in vitro diagnostics. Cancer Res; 77(21); 5717-20. ©2017 AACR.

Scientific Advances in Thoracic Oncology 2016.

Lung cancer care is rapidly changing with advances in genomic testing, the development of next-generation targeted kinase inhibitors, and the continued broad study of immunotherapy in new settings and potential combinations. The International Association for the Study of Lung Cancer and the Journal of Thoracic Oncology publish this annual update to help readers keep pace with these important developments. Experts in thoracic cancer and care provide focused updates across multiple areas, including prevention and early detection, molecular diagnostics, pathology and staging, surgery, adjuvant therapy, radiotherapy, molecular targeted therapy, and immunotherapy for NSCLC, SCLC, and mesothelioma. Quality and value of care and perspectives on the future of lung cancer research and treatment have also been included in this concise review.

Lung cancer: current therapies and new targeted treatments.

Lung cancer is the most frequent cause of cancer-related deaths worldwide. Every year, 1·8 million people are diagnosed with lung cancer, and 1·6 million people die as a result of the disease. 5-year survival rates vary from 4-17% depending on stage and regional differences. In this Seminar, we discuss existing treatment for patients with lung cancer and the promise of precision medicine, with special emphasis on new targeted therapies. Some subgroups, eg-patients with poor performance status and elderly patients-are not specifically addressed, because these groups require special treatment considerations and no frameworks have been established in terms of new targeted therapies. We discuss prevention and early detection of lung cancer with an emphasis on lung cancer screening. Although we acknowledge the importance of smoking prevention and cessation, this is a large topic beyond the scope of this Seminar.

Volumes Learned: It Takes More Than Size to "Size Up" Pulmonary Lesions.

RATIONALE AND OBJECTIVES: This study aimed to review the current understanding and capabilities regarding use of imaging for noninvasive lesion characterization and its relationship to lung cancer screening and treatment. MATERIALS AND METHODS: Our review of the state of the art was broken down into questions about the different lung cancer image phenotypes being characterized, the role of imaging and requirements for increasing its value with respect to increasing diagnostic confidence and quantitative assessment, and a review of the current capabilities with respect to those needs. RESULTS: The preponderance of the literature has so far been focused on the measurement of lesion size, with increasing contributions being made to determine the formal performance of scanners, measurement tools, and human operators in terms of bias and variability. Concurrently, an increasing number of investigators are reporting utility and predictive value of measures other than size, and sensitivity and specificity is being reported. Relatively little has been documented on quantitative measurement of non-size features with corresponding estimation of measurement performance and reproducibility. CONCLUSIONS: The weight of the evidence suggests characterization of pulmonary lesions built on quantitative measures adds value to the screening for, and treatment of, lung cancer. Advanced image analysis techniques may identify patterns or biomarkers not readily assessed by eye and may also facilitate management of multidimensional imaging data in such a way as to efficiently integrate it into the clinical workflow.

Lung Cancer Workshop XI: Tobacco-Induced Disease: Advances in Policy, Early Detection and Management.

The Prevent Cancer Foundation Lung Cancer Workshop XI: Tobacco-Induced Disease: Advances in Policy, Early Detection and Management was held in New York, NY on May 16 and 17, 2014. The two goals of the Workshop were to define strategies to drive innovation in precompetitive quantitative research on the use of imaging to assess new therapies for management of early lung cancer and to discuss a process to implement a national program to provide high quality computed tomography imaging for lung cancer and other tobacco-induced disease. With the central importance of computed tomography imaging for both early detection and volumetric lung cancer assessment, strategic issues around the development of imaging and ensuring its quality are critical to ensure continued progress against this most lethal cancer.

Role of the Quantitative Imaging Biomarker Alliance in optimizing CT for the evaluation of lung cancer screen-detected nodules.

The Quantitative Imaging Biomarker Alliance (QIBA) is a multidisciplinary consortium sponsored by the RSNA to define processes that enable the implementation and advancement of quantitative imaging methods described in a QIBA profile document that outlines the process to reliably and accurately measure imaging features. A QIBA profile includes factors such as technical (product-specific) standards, user activities, and relationship to a clinically meaningful metric, such as with nodule measurement in the course of CT screening for lung cancer. In this report, the authors describe how the QIBA approach is being applied to the measurement of small pulmonary nodules such as those found during low-dose CT-based lung cancer screening. All sources of variance with imaging measurement were defined for this process. Through a process of experimentation, literature review, and assembly of expert opinion, the strongest evidence was used to define how to best implement each step in the imaging acquisition and evaluation process. This systematic approach to implementing a quantitative imaging biomarker with standardized specifications for image acquisition and postprocessing for a specific quantitative measurement of a pulmonary nodule results in consistent performance characteristics of the measurement (eg, bias and variance). Implementation of the QIBA small nodule profile may allow more efficient and effective clinical management of the diagnostic workup of individuals found to have suspicious pulmonary nodules in the course of lung cancer screening evaluation.