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.

Issues with implementing a high-quality lung cancer screening program.

After a comprehensive review of the evidence, the United States Preventive Services Task Force recently endorsed screening with low-dose computed tomography as an early detection approach that has the potential to significantly reduce deaths due to lung cancer. Prudent implementation of lung cancer screening as a high-quality preventive health service is a complex challenge. The clinical evaluation and management of high-risk cohorts in the absence of symptoms mandates an approach that differs significantly from that of symptom-detected lung cancer. As with other cancer screenings, it is essential to provide to informed at-risk individuals a safe, high-quality, cost-effective, and accessible service. In this review, the components of a successful screening program are discussed as we begin to disseminate lung cancer screening as a national resource to improve outcomes with this lethal cancer. This information about lung cancer screening will assist clinicians with communications about the potential benefits and harms of this service for high-risk individuals considering participation in the screening process.

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.

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.

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.

Screening for lung cancer: in pursuit of pre-metastatic disease.

One reason for the high death rate of lung cancer is that tumours are not usually detected until the disease is at a late stage, at which point the cancer is non-curable. Spiral computerized tomography is a highly sensitive imaging method that could be used to screen high-risk populations, such as current or former smokers, for early-stage tumours. Trials to validate this tool are just underway, but beyond the imaging tools, population-based care of pre-metastatic lung cancer requires considerable evolution in clinical management approaches. More sensitive imaging tools might also provide a window into earlier biology, enabling the molecular dynamics of lung cancer progression to be elucidated.

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.

Cytokines and growth factors stimulate hyaluronan production: role of hyaluronan in epithelial to mesenchymal-like transition in non-small cell lung cancer.

In this study, we investigated the role of hyaluronan (HA) in non-small cell lung cancer (NSCLC) since close association between HA level and malignancy has been reported. HA is an abundant extracellular matrix component and its synthesis is regulated by growth factors and cytokines that include epidermal growth factor (EGF) and interleukin-1beta (IL-1beta). We showed that treatment with recombinant EGF and IL-1beta, alone or in combination with TGF-beta, was able to stimulate HA production in lung adenocarcinoma cell line A549. TGF-beta/IL-1beta treatment induced epithelial to mesenchymal-like phenotype transition (EMT), changing cell morphology and expression of vimentin and E-cadherin. We also overexpressed hyaluronan synthase-3 (HAS3) in epithelial lung adenocarcinoma cell line H358, resulting in induced HA expression, EMT phenotype, enhanced MMP9 and MMP2 activities and increased invasion. Furthermore, adding exogenous HA to A549 cells and inducing HA H358 cells resulted in increased resistance to epidermal growth factor receptor (EGFR) inhibitor, Iressa. Together, these results suggest that elevated HA production is able to induce EMT and increase resistance to Iressa in NSCLC. Therefore, regulation of HA level in NSCLC may be a new target for therapeutic intervention.

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.

Successful anti-cancer drug targets able to pass FDA review demonstrate the identifiable signature distinct from the signatures of random genes and initially proposed targets.

MOTIVATION: New efforts to guide and prioritize the selection of cancer drug targets are urgently needed, as is evident by the slow development of novel anti-cancer agents and the narrow therapeutic index of existing drugs. Given these limitations, the current study was conducted to explore the classification features defining the therapeutic success that can result from targeting a particular gene. RESULTS: Classification was based on extracting features specific to known successful anti-cancer targets and combining them in a linear classifier, resulting in calculation of an enrichment score for each gene. Extended description, the search tool used in this study, enriched existing drug target candidates by up to 10-fold at an approximately 50% recall rate, covering approximately 24 000 genes or approximately 80% of genome. More importantly, the target category with high attrition rate was classified from target category with low attrition rate, allowing to refine the drug development portfolios. Biological relevance of the parameters comprising the enrichment score was explored. Enrichment in cancer-specific effects was independently demonstrated by literature analysis. Imposing these enrichment scores on existing structural, pathway and phenotype-based procedures for prospective target selection may enhance the efficiency and accuracy of target identification and accelerate drug design. AVAILABILITY: The software used in this work is available upon request.

Lung cancer screening with low-dose computed tomography: a non-invasive diagnostic protocol for baseline lung nodules.

BACKGROUND: Indeterminate non-calcified lung nodules are frequent when low-dose spiral computed tomography (LD-CT) is used for lung cancer screening. We assessed the diagnostic utility of a non-invasive work-up protocol for nodules detected at baseline in volunteers enrolled in our single-centre screening trial, and followed for at least 1 year. METHODS: 5201 high-risk volunteers, recruited over 1 year from October 2004, underwent baseline LD-CT; 4821 (93%) returned for the first repeat LD-CT. Nodules 8mm received combined CT-positron emission tomography (CT-PET). A subset of nodules >8mm was studied by CT with contrast. Protocol failures were delayed diagnosis with disease progression beyond stage I, and negative surgical biopsy. RESULTS: 2754 (53%) volunteers presented one or more non-calcified nodules. Ninety-two lung cancers were diagnosed: 55 at baseline and 37 at annual screening (66% stage I). Among the 37 incident cancers, 17 had a baseline nodule that remained stage I, 7 had a baseline nodule that progressed beyond stage I, and 13 presented a new malignant nodule. Baseline and annual cancers were 79 (1.5%) and 13 (0.2%), respectively. In 15 of 104 (14%) invasive diagnostic procedures, the lesion was benign. Sensitivity, and specificity were 91 and 99.7%, respectively, for the entire protocol; 88 and 93% for CT-PET; and 100 and 59% for CT with contrast. CONCLUSIONS: The protocol limits invasive diagnostic procedures while few patients have diagnosis delay, supporting the feasibility of lung cancer screening in high-risk subjects by LD-CT. Nevertheless further optimization of the clinical management of screening-detected nodules is necessary.

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.