The IASLC Early Lung Imaging Confederation (ELIC) Open-Source Deep Learning and Quantitative Measurement Initiative.

BackgroundWith global adoption of 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 dataset and computational environment that are compliant with the most stringent international privacy regulations that also protects the intellectual properties of researchers, the International Association of the Study of Lung Cancer (IASLC) 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 utilized for clinically relevant AI research.MethodsIn 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.ResultsA total of 1,394 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 ≥ 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.ConclusionThese initial experiments demonstrated that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based datasets

Cystic Primary Lung Cancer: Evolution of Computed Tomography Imaging Morphology over Time

Purpose: Primary lung cancers associated with cystic airspaces are increasingly being recognized; however, there is a paucity of data on their natural history. We aimed to evaluate the prevalence, pathologic, and imaging characteristics of cystic lung cancer in a regional thoracic surgery center with a focus on the evolution of computed tomography morphology over time. Materials and Methods: Consecutive patients referred for potential surgical management of primary lung cancer between January 2016 and December 2018 were included. Clinical, imaging, and pathologic data were collected at the time of diagnosis and at the time of the oldest computed tomography showing the target lesion. Descriptive analysis was carried out. Results: A total of 441 cancers in 431 patients (185 males, 246 females), median age 69.6 years (interquartile range: 62.6 to 75.3 y), were assessed. Overall, 41/441 (9.3%) primary lung cancers were cystic at the time of diagnosis. The remaining showed solid (67%), part-solid (22%), and ground-glass (2%) morphologies. Histopathology of the cystic lung cancers at diagnosis included 31/41 (76%) adenocarcinomas, 8/41 (20%) squamous cell carcinomas, 1/41 (2%) adenosquamous carcinoma, and 1/41 (2%) unspecified non-small cell lung carcinoma. Overall, 8/34 (24%) cystic cancers at the time of diagnosis developed from different morphologic subtype precursor lesions, while 8/34 (24%) cystic precursor lesions also transitioned into part-solid or solid cancers at the time of diagnosis. Conclusions: This study demonstrates that cystic airspaces within lung cancers are not uncommon, and may be seen transiently as cancers evolve. Increased awareness of the spectrum of cystic lung cancer morphology is important to improve diagnostic accuracy and lung cancer management

Predicting Malignancy Risk of Screen-Detected Lung Nodules-Mean Diameter or Volume

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Chronic obstructive pulmonary disease prevalence and prediction in a high-risk lung cancer screening population

Background Chronic obstructive pulmonary disease (COPD) is an underdiagnosed condition sharing risk factors with lung cancer. Lung cancer screening may provide an opportunity to improve COPD diagnosis. Using Pan-Canadian Early Detection of Lung Cancer (PanCan) study data, the present study sought to determine the following: 1) What is the prevalence of COPD in a lung cancer screening population? 2) Can a model based on clinical and screening low-dose CT scan data predict the likelihood of COPD? Methods The single arm PanCan study recruited current or former smokers age 50–75 who had a calculated risk of lung cancer of at least 2% over 6 years. A baseline health questionnaire, spirometry, and low-dose CT scan were performed. CT scans were assessed by a radiologist for extent and distribution of emphysema. With spirometry as the gold standard, logistic regression was used to assess factors associated with COPD. Results Among 2514 recruited subjects, 1136 (45.2%) met spirometry criteria for COPD, including 833 of 1987 (41.9%) of those with no prior diagnosis, 53.8% of whom had moderate or worse disease. In a multivariate model, age, current smoking status, number of pack-years, presence of dyspnea, wheeze, participation in a high-risk occupation, and emphysema extent on LDCT were all statistically associated with COPD, while the overall model had poor discrimination (c-statistic = 0.627 (95% CI of 0.607 to 0.650). The lowest and the highest risk decile in the model predicted COPD risk of 27.4 and 65.3%. Conclusions COPD had a high prevalence in a lung cancer screening population. While a risk model had poor discrimination, all deciles of risk had a high prevalence of COPD, and spirometry could be considered as an additional test in lung cancer screening programs. Trial registration (Clinical Trial Registration: ClinicalTrials.gov, number NCT00751660 , registered September 12, 2008)Other UBCNon UBCReviewedFacult

Circulating Proteome for Pulmonary Nodule Malignancy.

BackgroundWhile lung cancer screening with low-dose computed tomography (LDCT) is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a major challenge. We conducted one of the first systematic investigations of circulating protein markers to differentiate malignant versus benign screen-detected pulmonary nodules.MethodsBased on four international LDCT screening studies, we assayed 1078 protein markers using pre-diagnostic blood samples from 1253 participants based on a nested case-control design. Protein markers were measured using proximity extension assays and data were analyzed using multivariable logistic regression, random forest, and penalized regressions. Protein burden scores for overall nodule malignancy (PBS-overall) and imminent tumors (PBS-imminent) were estimated.ResultsWe identified 36 potentially informative circulating protein markers differentiating malignant from benign nodules, representing a tightly connected biological network. Ten markers were found to be particularly relevant for imminent lung cancer diagnoses within one year. Increases in PBS-overall and PBS-imminent by 1 standard deviation were associated with odds ratios of 2.29(95%CI = 1.95-2.72), and 2.81(95%CI = 2.27-3.54) for nodule malignancy overall, and within 1 year of diagnosis, respectively. Both PBS-overall and PBS-imminent were substantially higher for those with malignant nodules compared to those with benign nodules even when limited to LungRADS category 4 (p ConclusionsCirculating protein markers can help to differentiate malignant from benign pulmonary nodules. Validation with an independent CT-screening study will be required prior to clinical implementation

Protocol and rationale for the international lung screening trial

: The NLST (National Lung Screening Trial) reported a 20% reduction in lung cancer mortality with low-dose computed tomography screening; however, important questions on how to optimize screening remain, including which selection criteria are most accurate at detecting lung cancers and what nodule management protocol is most efficient. The PLCO (Prostate, Lung, Colorectal and Ovarian) Cancer Screening Trial 6-year and PanCan (Pan-Canadian Early Detection of Lung Cancer) nodule malignancy risk models are two of the better validated risk prediction models for screenee selection and nodule management, respectively. Combined use of these models for participant selection and nodule management could significantly improve screening efficiency.: The ILST (International Lung Screening Trial) is a prospective cohort study with two primary aims: ) Compare the accuracy of the PLCO model against U.S. Preventive Services Task Force (USPSTF) criteria for detecting lung cancers and ) evaluate nodule management efficiency using the PanCan nodule probability calculator-based protocol versus Lung-RADS.: ILST will recruit 4,500 participants who meet USPSTF and/or PLCO risk ≥1.51%/6-year selection criteria. Participants will undergo baseline and 2-year low-dose computed tomography screening. Baseline nodules are managed according to PanCan probability score. Participants will be followed up for a minimum of 5 years. Primary outcomes for aim 1 are the proportion of individuals selected for screening, proportion of lung cancers detected, and positive predictive values of either selection criteria, and outcomes for aim 2 include comparing distributions of individuals and the proportion of lung cancers in each of three management groups: next surveillance scan, early recall scan, or diagnostic evaluation recommended. Statistical powers to detect differences in the four components of primary study aims were ≥82%.: ILST will prospectively evaluate the comparative accuracy and effectiveness of two promising multivariable risk models for screenee selection and nodule management in lung cancer screening.Clinical trial registered with www.clinicaltrials.gov (NCT02871856)

Economic impact of using risk models for eligibility selection to the International lung screening Trial

OBJECTIVES: Using risk models as eligibility criteria for lung screening can reduce race and sex-based disparities. We used data from the International Lung Screening Trial(ILST; NCT02871856) to compare the economic impact of using the PLCOm2012 risk model or the US Preventative Services' categorical age-smoking history-based criteria (USPSTF-2013). MATERIALS AND METHODS: The cost-effectiveness of using PLCOm2012 versus USPSTF-2013 was evaluated with a decision analytic model based on the ILST and other screening trials. The primary outcomes were costs in 2020 International Dollars ($), quality-adjusted life-years (QALY) and incremental net benefit (INB, in$ per QALY). Secondary outcomes were selection characteristics and cancer detection rates (CDR). RESULTS: Compared with the USPSTF-2013 criteria, the PLCOm2012 risk model resulted in $355 of cost savings per 0.2 QALYs gained (INB=$4294 at a willingness-to-pay threshold of $20 000/QALY (95$4205-$4383). Using the risk model was more cost-effective in females at both a 1.5$6616 and $6112, respectively), compared with males ($5221 and $695). The PLCOm2012 model selected more females, more individuals with fewer years of formal education, and more people with other respiratory illnesses in the ILST. The CDR with the risk model was higher in females compared with the USPSTF-2013 criteria (Risk Ratio = 7.67, 95 % CI: 1.87-31.38). CONCLUSION: The PLCOm2012 model saved costs, increased QALYs and mitigated socioeconomic and sex-based disparities in access to screening