Visual scoring of osteoporosis on low-dose CT in lung cancer screening population.

OBJECTIVES: The risk factors for lung cancer screening eligibility, age as well as smoking history, are also present for osteoporosis. This study aims to develop a visual scoring system to identify osteoporosis that can be applied to low-dose CT scans obtained for lung cancer screening. MATERIALS AND METHODS: We retrospectively reviewed 1000 prospectively enrolled participants in the lung cancer screening program at the Mount Sinai Hospital. Optimal window width and level settings for the visual assessment were chosen based on a previously described approach. Visual scoring of osteoporosis and automated measurement using dedicated software were compared. Inter-reader agreement was conducted using six readers with different levels of experience who independently visually assessed 30 CT scans. RESULTS: Based on previously validated formulas for choosing window and level settings, we chose osteoporosis settings of Width = 230 and Level = 80. Of the 1000 participants, automated measurement was successfully performed on 774 (77.4 %). Among these, 138 (17.8 %) had osteoporosis. There was a significant correlation between the automated measurement and the visual score categories for osteoporosis (Kendall's Tau = -0.64, p < 0.0001; Spearman's rho = -0.77, p < 0.0001). We also found substantial to excellent inter-reader agreement on the osteoporosis classification among the 6 radiologists (Fleiss κ = 0.91). CONCLUSIONS: Our study shows that a simple approach of applying specific window width and level settings to already reconstructed sagittal images obtained in the context of low-dose CT screening for lung cancer is highly feasible and useful in identifying osteoporosis.

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.

Calibration phantom-based prediction of CT lung nodule volume measurement performance.

Background: A calibration phantom-based method has been developed for predicting small lung nodule volume measurement bias and precision that is specific to a particular computed tomography (CT) scanner and acquisition protocol. Methods: The approach involves CT scanning a simple reference object with a specific acquisition protocol, analyzing the scan to estimate the fundamental imaging properties of the CT acquisition system, generating numerous simulated images of a target geometry using the fundamental imaging properties, measuring the simulated images with a standard nodule volume segmentation algorithm, and calculating bias and precision performance statistics from the resulting volume measurements. We evaluated the ability of this approach to predict volume measurement bias and precision of Teflon spheres (diameters =4.76, 6.36, and 7.94 mm) placed within an anthropomorphic chest phantom when using 3M Scotch Magic™ tape as the reference object. CT scanning of the spheres was performed with 0.625, 1.25, and 2.5 mm slice thickness and spacing. Results: The study demonstrated good agreement between predicted volumetric performance and observed volume measurement performance for both volumetric measurement bias and precision. The predicted and observed volume mean for all slice thicknesses was found to be 28% and 13% lower on average than the manufactured sphere volume, respectively. When restricted to 0.625 and 1.25 mm slice thickness scans, which are recommended for small lung nodule volume measurement, we found that the difference between predicted and observed volume coefficient of variation was less than 1.0 %. The approach also showed a resilience to varying CT image acquisition protocols, a critical capability when deploying in a real-world clinical setting. Conclusions: This is the first report of a calibration phantom-based method's ability to predict both small lung nodule volume measurement bias and precision. Volume measurement bias and precision for small lung nodules can be predicted using simple low-cost reference objects to estimate fundamental CT image characteristics and modeling and simulation techniques. The approach demonstrates an improved method for predicting task specific, clinically relevant measurement performance using advanced and fully automated image analysis techniques and low-cost reference objects.

Environmental exposures are important risk factors for advanced liver fibrosis in African American adults.

Background & Aims: The prevalence and aetiology of liver fibrosis vary over time and impact racial/ethnic groups unevenly. This study measured time trends and identified factors associated with advanced liver fibrosis in the United States. Methods: Standardised methods were used to analyse data on 47,422 participants (≥20 years old) in the National Health and Nutrition Examination Survey (1999-2018). Advanced liver fibrosis was defined as Fibrosis-4 ≥2.67 and/or Forns index ≥6.9 and elevated alanine aminotransferase. Results: The estimated number of people with advanced liver fibrosis increased from 1.3 million (95% CI 0.8-1.9) to 3.5 million (95% CI 2.8-4.2), a nearly threefold increase. Prevalence was higher in non-Hispanic Black and Mexican American persons than in non-Hispanic White persons. In multivariable logistic regression analysis, cadmium was an independent risk factor in all racial/ethnic groups. Smoking and current excessive alcohol use were risk factors in most. Importantly, compared with non-Hispanic White persons, non-Hispanic Black persons had a distinctive set of risk factors that included poverty (odds ratio [OR] 2.09; 95% CI 1.44-3.03) and susceptibility to lead exposure (OR 3.25; 95% CI 1.95-5.43) but did not include diabetes (OR 0.88; 95% CI 0.61-1.27; p =0.52). Non-Hispanic Black persons were more likely to have high exposure to lead, cadmium, polychlorinated biphenyls, and poverty than non-Hispanic White persons. Conclusions: The number of people with advanced liver fibrosis has increased, creating a need to expand the liver care workforce. The risk factors for advanced fibrosis vary by race/ethnicity. These differences provide useful information for designing screening programmes. Poverty and toxic exposures were associated with the high prevalence of advanced liver fibrosis in non-Hispanic Black persons and need to be addressed. Impact and Implications: Because liver disease often produces few warning signs, simple and inexpensive screening tests that can be performed by non-specialists are needed to allow timely diagnosis and linkage to care. This study shows that non-Hispanic Black persons have a distinctive set of risk factors that need to be taken into account when designing liver disease screening programs. Exposure to exogenous toxins may be especially important risk factors for advanced liver fibrosis in non-Hispanic Black persons.

Evaluation of emphysema on thoracic low-dose CTs through attention-based multiple instance deep learning.

In addition to lung cancer, other thoracic abnormalities, such as emphysema, can be visualized within low-dose CT scans that were initially obtained in cancer screening programs, and thus, opportunistic evaluation of these diseases may be highly valuable. However, manual assessment for each scan is tedious and often subjective, thus we have developed an automatic, rapid computer-aided diagnosis system for emphysema using attention-based multiple instance deep learning and 865 LDCTs. In the task of determining if a CT scan presented with emphysema or not, our novel Transfer AMIL approach yielded an area under the ROC curve of 0.94 ± 0.04, which was a statistically significant improvement compared to other methods evaluated in our study following the Delong Test with correction for multiple comparisons. Further, from our novel attention weight curves, we found that the upper lung demonstrated a stronger influence in all scan classes, indicating that the model prioritized upper lobe information. Overall, our novel Transfer AMIL method yielded high performance and provided interpretable information by identifying slices that were most influential to the classification decision, thus demonstrating strong potential for clinical implementation.

Dose-response relationship between World Trade Center dust exposure and hepatic steatosis.

BACKGROUND: The World Trade Center (WTC) attack exposed thousands of workers to toxic chemicals that have been linked to liver diseases and cancers. This study examined the relationship between the intensity of WTC dust exposure and the risk of hepatic steatosis in the WTC General Responders Cohort (GRC). METHODS: All low-dose computed tomography (CT) scans of the chest performed on the WTC GRC between September 11, 2001 and December 31, 2018, collected as part of the World Trade Center Health Program, were reviewed. WTC dust exposure was categorized into five groups based on WTC arrival time. CT liver density was estimated using an automated algorithm, statistics-based liver density estimation from imaging. The relationship between the intensity of WTC dust exposure and the risk of hepatic steatosis was examined using univariate and multivariable regression analyses. RESULTS: Of the 1788 WTC responders, 258 (14.4%) had liver attenuation less than 40 Hounsfield units (HU < 40) on their earliest CT. Median time after September 11, 2001 and the earliest available CT was 11.3 years (interquartile range: 8.0-14.9 years). Prevalence of liver attenuation less than 40 HU was 17.0% for arrivals on September 11, 2001, 16.0% for arrivals on (September 12, 2001 or September 13, 2001), 10.9% for arrivals on September 14-30, 2001, and 9.0% for arrivals on January 10, 2001 or later (p = 0.0015). A statistically significant trend of increasing liver steatosis was observed with earlier arrival times (p < 0.0001). WTC arrival time remained a significant independent factor for decreased liver attenuation after controlling for other covariates. CONCLUSIONS: Early arrival at the WTC site was significantly associated with increasing hepatic steatosis.

Implementation of low-dose CT screening in two different health care systems: Mount Sinai Healthcare System and Phoenix VA Health Care System.

Implementation of lung screening (LS) programs is challenging even among health care organizations that have the motivation, the resources, and more importantly, the goal of providing for life-saving early detection, diagnosis, and treatment of lung cancer. We provide a case study of LS implementation in different healthcare systems, at the Mount Sinai Healthcare System (MSHS) in New York City, and at the Phoenix Veterans Affairs Health Care System (PVAHCS) in Phoenix, Arizona. This will illustrate the commonalities and differences of the LS implementation process in two very different health care systems in very different parts of the United States. Underlying the successful implementation of these LS programs was the use of a comprehensive management system, the Early Lung Cancer Action Program (ELCAP) Management SystemTM. The collaboration between MSHS and PVAHCS over the past decade led to the ELCAP Management SystemTM being gifted by the Early Diagnosis and Treatment Research Foundation to the PVAHCS, to develop a "VA-ELCAP" version. While there remain challenges and opportunities to continue improving LS and its implementation, there is an increasing realization that most patients who are diagnosed with lung cancer as a result of annual LS can be cured, and that of all the possible risks associated with LS, the greater risk of all is for heavy cigarette smokers not to be screened. We identified 10 critical components in implementing a LS program. We provided the details of each of these components for the two healthcare systems. Most importantly, is that continual re-evaluation of the screening program is needed based on the ongoing quality assurance program and database of the actual screenings. At minimum, there should be an annual review and updating. As early diagnosis of lung cancer must be followed by optimal treatment to be effective, treatment advances for small, early lung cancers diagnosed as a result of screening also need to be assessed and incorporated into the entire screening and treatment program.

Added benefits of early detection of other diseases on low-dose CT screening.

Low-dose CT screening for lung cancer provides images of the entire chest and upper abdomen. While the focus of screening is on finding early lung cancer, radiology leadership has embraced the fact that the information contained in the images presents a new challenge to the radiology profession. Other findings in the chest and upper abdomen were not the reason for obtaining the screening CT scan, nor symptom-prompted, but still need to be reported. Reporting these findings and making recommendations for further workup requires careful consideration to avoid unnecessary workup or interventions while still maximizing the benefit that early identification of these other diseases provided. Other potential findings, such as cardiovascular disease and chronic pulmonary obstructive diseases actually cause more deaths than lung cancer. Existing recommendations for workup of abnormal CT findings are based on symptom-prompted indications for imaging. These recommendations may be different when the abnormalities are identified in asymptomatic people undergoing CT screening for lung cancer. I-ELCAP, a large prospectively collected multi-institutional and multi-national database of screenings, was used to analyze CT findings identified in screening for lung cancer. These analyses and recommendations were made by radiologists in collaboration with clinicians in different medical specialties.

The Regimen of Computed Tomography Screening for Lung Cancer: Lessons Learned Over 25 Years From the International Early Lung Cancer Action Program.

We learned many unanticipated and valuable lessons since we started planning our study of low-dose computed tomography (CT) screening for lung cancer in 1991. The publication of the baseline results of the Early Lung Cancer Action Project (ELCAP) in Lancet 1999 showed that CT screening could identify a high proportion of early, curable lung cancers. This stimulated large national screening studies to be quickly started. The ELCAP design, which provided evidence about screening in the context of a clinical program, was able to rapidly expand to a 12-institution study in New York State (NY-ELCAP) and to many international institutions (International-ELCAP), ultimately working with 82 institutions, all using the common I-ELCAP protocol. This expansion was possible because the investigators had developed the ELCAP Management System for screening, capturing data and CT images, and providing for quality assurance. This advanced registry and its rapid accumulation of data and images allowed continual assessment and updating of the regimen of screening as advances in knowledge and new technology emerged. For example, in the initial ELCAP study, introduction of helical CT scanners had allowed imaging of the entire lungs in a single breath, but the images were obtained in 10 mm increments resulting in about 30 images per person. Today, images are obtained in submillimeter slice thickness, resulting in around 700 images per person, which are viewed on high-resolution monitors. The regimen provides the imaging acquisition parameters, imaging interpretation, definition of positive result, and the recommendations for further workup, which now include identification of emphysema and coronary artery calcifications. Continual updating is critical to maximize the benefit of screening and to minimize potential harms. Insights were gained about the natural history of lung cancers, identification and management of nodule subtypes, increased understanding of nodule imaging and pathologic features, and measurement variability inherent in CT scanners. The registry also provides the foundation for assessment of new statistical techniques, including artificial intelligence, and integration of effective genomic and blood-based biomarkers, as they are developed.

Lung cancer screening intervals based on cancer risk.

OBJECTIVES: As low-dose CT screening is gaining acceptance, focus is on increasing the efficiency of screening. One major consideration is to reduce the total number of annual rounds by increasing the interval between screening rounds. It has been suggested that longer intervals could be used for individuals who are at lower risk of lung cancer. In this study, we explored whether eligible participants in a program of LDCT screening who are at lower risk of lung cancer have less aggressive cancers than those at higher risk. METHODS: We retrospectively identified 118 participants in I-ELCAP database between 1992-2019 who had been screened using HIPAA-compliant protocols and had solid lung cancers diagnosed on an annual round of screening, 7-18 months after the prior round. Volume doubling time (VDT) for each cancer was calculated. Estimated risk of developing lung cancer was calculated using PLCOM2012 model. The strength of the relationship between VDT and individual PLCOM2012 scores was assessed by Pearson(r) and Spearman (ρ) correlation coefficients. RESULTS: VDTs were significantly different by cell-type (p < 0.0001); median VDT for small cell was 34.0 days, followed by other cell-types (61.8 days), squamous-cell (73.3 days), and adenocarcinoma (135.7 days). The median VDT for the 78 (66.1 %) Stage I lung cancers was significantly longer than the 40 Stage II + lung cancers (101.4 days vs. 45.5 days, p < 0.0001). None of the established lung cancer risk indicators (age, pack-years of smoking, or PLCOM2012 scores) were significant predictors of VDT or lung cancer stage. CONCLUSION: No significant relationship was demonstrated between risk of developing lung cancer (measured by risk models, age or smoking history) and lung cancer aggressiveness (measured by VDT, cell-type and Stage). This suggests that there is no evidence for determining intervals between repeat screenings using risk-based characteristics. It does not, however, exclude the possibility that future models may establish such a relationship.

Combined aortic valve and coronary artery calcifications in lung cancer screening as predictors of death from cardiovascular disease.

OBJECTIVES: Smoking is a major risk factor for both cardiovascular disease (CVD) and lung cancer. Aortic valve calcification (AVC) and coronary artery calcification (CAC) are both due to atherosclerotic disease. We aim to investigate whether AVC on low-dose CT (LDCT) predicts death from CVD in smokers beyond that provided by CAC. METHODS: We reviewed a prospective cohort of 8618 smokers enrolled in LDCT screening for lung cancer in New York State between June 2000 and December 2005. As of December 2009, 169 of the 643 deaths were due to CVD; median follow-up time was 96.4 months. Visual AVC was assessed as being absent (AVC = 0) or present (AVC > 0). CAC ordinal scores of 0-12 were categorized into three validated prognostic categories (0, 1-3, and 4-12). Cox proportional hazards regression analysis was used to assess whether AVC > 0 increased the risk of CVD death, after adjustment for CAC categories and other risk factors. RESULTS: The prevalence of AVC significantly increased (p < 0.0001) with the increasing severity of the CAC categories; Pearson, Spearman, and Kendall's correlation coefficients showed a significant correlation between AVC and CAC with r = 0.29, ρ = 0.32, and τB = 0.28 (all p values < 0.0001), respectively. CAC and AVC were significant predictors of CVD death when considered alone using multivariable Cox regression analysis (adjusted HR of CAC = 1.57, p = 0.04; adjusted HR of AVC = 1.39, p = 0.045). When AVC > 0 and CAC ≥ 4, the hazard ratio was 2.35 (95%CI 1.57-3.50) compared with the reference group of AVC = 0 and CAC < 4, when adjusted for other risk factors. CONCLUSIONS: The presence of AVC identified on LDCT is a significant predictor of future CVD death, particularly for those with ordinal CAC score ≥ 4. KEY POINTS: • Aortic valve calcification (AVC) and coronary artery calcification (CAC) are both due to atherosclerotic disease. The prevalence of AVC in lung cancer screening cohort significantly increased with the increasing severity of CAC. • CAC and AVC were significant predictors of cardiovascular disease (CVD) death when considered alone. Participants who underwent lung cancer screening with AVC > 0 and CAC ≥ 4 had more than a 2-fold increased risk of CVD death than the group with AVC = 0 and CAC < 4, when adjusted for other risk factors.

Visual scoring of aortic valve calcifications on low-dose CT in lung cancer screening.

OBJECTIVES: To evaluate risk factors for prevalence and progression of aortic valve calcification (AVC) in lung cancer screening participants and also to assess the sensitivity and reliability of visual AVCs on low-dose CT (LDCT) for predicting aortic stenosis (AS) in high-risk smokers. METHODS: We reviewed 1225 consecutive participants in annual LDCT screening for lung cancer at the Mount Sinai Hospital between 2010 and 2017. Sensitivity and specificity of moderate/severe AVC score on LDCT to identify AS on echocardiogram were calculated for 126 participants who had both within 12 months. Using regression analyses, risk factors for AVC at baseline, for progression, and for new AVC on annual rounds of screening were identified. Reliability of AVC assessment on LDCT was assessed by comparing visual AVC scores (1) with standard-dose, electrocardiography (ECG)-gated CT for 31 participants who had both within 12 months and (2) with Agatston scores of 1225 participants and by determining (3) the intra-reader agreement of 1225 participants. RESULTS: Visual AVC scores on LDCT had substantial agreement with the severity of AS on echocardiography and substantial inter-observer and excellent intra-observer agreement. Sensitivity and specificity of moderate/severe visual AVC scores for moderate/severe AS on echocardiogram were 100% and 94%, respectively. Significant predictors for baseline AVC were male sex (OR = 2.52), age (OR10 years = 2.87), and coronary artery calcification score (OR = 1.18), the significant predictor for AVC progression after baseline was pack-years of smoking (HR10 packyears = 1.14), and significant predictors for new AVC on annual LDCT were male sex (HR = 1.51), age (HR10 years = 2.17), CAC (HR = 1.09)  and BMI (HR = 1.06). CONCLUSIONS: AVC scores on LDCT should be documented, especially in lung cancer screening program. KEY POINTS: • LDCT screening for lung cancer provides an opportunity to identify lung cancer and cardiovascular disease in asymptomatic smokers. • Visual aortic valve calcification scores could be reliably evaluated on LDCT and had substantial agreement with the severity of aortic valve stenosis on echocardiography. • Sensitivity and specificity of moderate/severe visual AVC scores on LDCT for moderate/severe AS on echocardiogram were 100% and 94%, respectively.

Automated measurement of liver attenuation to identify moderate-to-severe hepatic steatosis from chest CT scans.

PURPOSE: Develop and validate an automated method for measuring liver attenuation in non-contrast low-dose chest CT (LDCT) scans and compare it to the standard manual method for identifying moderate-to-severe hepatic steatosis (HS). METHOD: The automated method identifies a region below the right lung within the liver and uses statistical sampling techniques to exclude non-liver parenchyma. The method was used to assess moderate-to-severe HS on two IRB-approved cohorts: 1) 24 patients with liver disease examined between 1/2013-1/2017 with non-contrast chest CT and abdominal MRI scans obtained within three months of liver biopsy, and 2) 319 lung screening participants with baseline LDCT performed between 8/2011-1/2017. Agreement between the manual and automated CT methods, the manual MRI method, and pathology for determining moderate-to-severe HS was assessed using Cohen's Kappa by applying a 40 HU threshold to the CT method and 17.4% fat fraction to MRI. Agreement between the manual and automated CT methods was assessed using the intraclass correlation coefficient (ICC). Variability was assessed using Bland-Altman limits of agreement (LoA). RESULTS: In the first cohort, the manual and automated CT methods had almost perfect agreement (ICC = 0.97, κ = 1.00) with LoA of -7.6 to 4.7 HU. Both manual and automated CT methods had almost perfect agreement with MRI (κ = 0.90) and substantial agreement with pathology (κ = 0.77). In the second cohort, the manual and automated CT methods had almost perfect agreement (ICC = 0.94, κ = 0.87). LoA were -10.6 to 5.2 HU. CONCLUSION: Automated measurements of liver attenuation from LDCT scans can be used to identify moderate-to-severe HS on LDCT.

Initiative for Early Lung Cancer Research on Treatment: Development of Study Design and Pilot Implementation.

INTRODUCTION: To maximize the benefits of computed tomographic screening for lung cancer, optimal treatment for small, early lung cancers is needed. Limiting the extent of surgery spares lung tissue, preserves pulmonary function, and decreases operative time, complications, and morbidities. It also increases the likelihood of resecting future new primary lung cancers. The goal is to assess alternative treatments in a timely manner. METHODS: The focus sessions with patients and physicians separately highlighted the need to consider their perceptions. Literature reviews and analyses of treatment results using large databases were performed to formulate critical questions about long-term treatment outcomes, recurrence, and quality of life of alternative treatments. Based on these analyses, the investigators developed a prospective multi-institutional cohort study, the Initiative for Early Lung Cancer Research for Treatment, to compare treatments for stage I NSCLC. HIPAA compliant institutional review board approval was obtained and we performed a feasibility study of the first 206 surgical patients. RESULTS: Lobectomy was performed in 89 (43.2%) patients, and sublobar resection was performed in 117 (56.7%) patients. Mediastinal lymph node resection was performed in 173 (84.0%) patients, 8 had N1 and 3 N2 lymph node metastases. Patients stated that both the surgeon's opinion (93%) and the patient's own opinion (93%) were extremely important, followed by the patients' view that the chosen procedure would provide the best quality of life (90%). CONCLUSIONS: It was feasible to obtain pre- and postsurgical information from patients and surgeons. We anticipate statistically meaningful results about treatment alternatives in 3 to 5 years.

Pulmonary nodule classification in lung cancer screening with three-dimensional convolutional neural networks.

A three-dimensional (3-D) convolutional neural network (CNN) trained from scratch is presented for the classification of pulmonary nodule malignancy from low-dose chest CT scans. Recent approval of lung cancer screening in the United States provides motivation for determining the likelihood of malignancy of pulmonary nodules from the initial CT scan finding to minimize the number of follow-up actions. Classifier ensembles of different combinations of the 3-D CNN and traditional machine learning models based on handcrafted 3-D image features are also explored. The dataset consisting of 326 nodules is constructed with balanced size and class distribution with the malignancy status pathologically confirmed. The results show that both the 3-D CNN single model and the ensemble models with 3-D CNN outperform the respective counterparts constructed using only traditional models. Moreover, complementary information can be learned by the 3-D CNN and the conventional models, which together are combined to construct an ensemble model with statistically superior performance compared with the single traditional model. The performance of the 3-D CNN model demonstrates the potential for improving the lung cancer screening follow-up protocol, which currently mainly depends on the nodule size.

Lung Cancers Manifesting as Part-Solid Nodules in the National Lung Screening Trial.

OBJECTIVE: The objective of our study was to determine how often death occurred from lung cancers that manifested as part-solid nodules in the National Lung Screening Trial (NLST). MATERIALS AND METHODS: NLST radiologists classified nodules as solid, ground-glass, or mixed. All lung cancers classified as mixed nodules by NLST radiologists were reviewed by four experienced radiologists and reclassified as solid, nonsolid, or part-solid nodules. When possible, volume doubling times (VDTs) were calculated separately for the entire nodule and for the solid component of the nodule. RESULTS: Of 88 screening-diagnosed lung cancer cases identified by the NLST radiologists as mixed nodules, study radiologists confirmed that 19 were part-solid nodules. All the part-solid nodules were present at baseline (time 0), and none of the patients with a part-solid nodule had lymph node enlargement at CT before diagnosis or metastases at resection. Multilobar stage IV (T4N0M1) bronchioloalveolar carcinoma was diagnosed in one patient 25.0 months after study randomization, and the patient died 67.9 months after randomization. All 18 patients with a solitary or dominant part-solid nodule underwent surgery, and none died of lung cancer. From randomization, the average time to diagnosis was 18.6 months and the average time of follow-up was 79.2 months. On the last CT examination performed before diagnosis, the average size of the solid component of the part-solid nodules was 9.2 mm (SD, 4.9); the solid component was larger than 10 mm in five patients. The median VDT based on the entire nodule was 476 days, and the median VDT based on the solid component alone was 240 days. CONCLUSION: None of the patients with lung cancer manifesting as a solitary or dominant part-solid nodule had lymph node enlargement or metastases at pathology, and none died of lung cancer within the follow-up time of the NLST.

Lung Cancer Deaths in the National Lung Screening Trial Attributed to Nonsolid Nodules.

Purpose To validate the recommendation of performing annual follow-up of nonsolid nodules (NSNs) identified by computed tomographic (CT) screening for lung cancer, all cases of lung cancer manifesting as NSN in the National Lung Screening Trial (NLST) were reviewed. Materials and Methods Institutional review board and informed consent were waived for this study. The NLST database was searched to identify all participants with at least one NSN on CT scan with lung cancer as the cause of death (COD) documented by the NLST endpoint verification process. Among the 26 722 participants, 2534 (9.4%) had one or more NSNs, and lung cancer as the COD occurred for 48 participants. On review, 21 of the 48 patients had no NSN in the cancerous lobe, which left 27 patients whose CT scans were reviewed by four radiologists: Group A (n = 12) were cases of lung cancer as the COD because of adenocarcinoma, and group B (n = 15) were cases of lung cancer as the COD because of other cell types. Frequency of lung cancer as the COD because of NSN and the time from randomization to diagnosis within these groups was determined. Results Six of the 12 patients in group A had no NSN in the cancerous lobe whereas the remaining six patients had a dominant solid or part-solid nodule in the lobe that rapidly grew in four patients, was multifocal in one patient, and had a growing NSN in one patient in whom diagnosis was delayed for over 3 years. Five of the 15 patients in group B had no NSN, and for the remaining 10 patients, lung cancer as the COD was not because of NSN. Conclusion It seems unlikely that patients with lung cancer as the COD occurred with solitary or dominant NSN as long as annual follow-up was performed. This lends further support that lung cancers that manifest as NSNs have an indolent course and can be managed with annual follow-up. © RSNA, 2016.

Low-Dose CT Screening for Lung Cancer: Computer-aided Detection of Missed Lung Cancers.

Purpose To update information regarding the usefulness of computer-aided detection (CAD) systems with a focus on the most critical category, that of missed cancers at earlier imaging, for cancers that manifest as a solid nodule. Materials and Methods By using a HIPAA-compliant institutional review board-approved protocol where informed consent was obtained, 50 lung cancers that manifested as a solid nodule on computed tomographic (CT) scans in annual rounds of screening (time 1) were retrospectively identified that could, in retrospect, be identified on the previous CT scans (time 0). Four CAD systems were compared, which were referred to as CAD 1, CAD 2, CAD 3, and CAD 4. The total number of accepted CAD-system-detected nodules at time 0 was determined by consensus of two radiologists and the number of CAD-system-detected nodules that were rejected by the radiologists was also documented. Results At time 0 when all the cancers had been missed, CAD system detection rates for the cancers were 56%, 70%, 68%, and 60% (κ = 0.45) for CAD systems 1, 2, 3, and 4, respectively. At time 1, the rates were 74%, 82%, 82%, and 78% (κ = 0.32), respectively. The average diameter of the 50 cancers at time 0 and time 1 was 4.8 mm and 11.4 mm, respectively. The number of CAD-system-detected nodules that were rejected per CT scan for CAD systems 1-4 at time 0 was 7.4, 1.7, 0.6, and 4.5 respectively. Conclusion CAD systems detected up to 70% of lung cancers that were not detected by the radiologist but failed to detect about 20% of the lung cancers when they were identified by the radiologist, which suggests that CAD may be useful in the role of second reader. (©) RSNA, 2016.

Automated pulmonary nodule CT image characterization in lung cancer screening.

PURPOSE: In lung cancer screening, pulmonary nodules are first identified in low-dose chest CT images. Costly follow-up procedures could be avoided if it were possible to establish the malignancy status of these nodules from these initial images. Preliminary computer methods have been proposed to characterize the malignancy status of pulmonary nodules based on features extracted from a CT image. The parameters and performance of such a computer system in a lung cancer screening context are addressed. METHODS: A computer system that incorporates novel 3D image features to determine the malignancy status of pulmonary nodules is evaluated with a large dataset constructed from images from the NLST and ELCAP lung cancer studies. The system is evaluated with different data subsets to determine the impact of class size distribution imbalance in datasets and to evaluate different training and testing strategies. RESULTS: Results show a modest improvement in malignancy prediction compared to prediction by size alone for a traditional size-unbalanced dataset. Further, the advantage of size binning for classifier design and the advantages of a size-balanced dataset for both training and testing are demonstrated. CONCLUSION: Nodule classification in the context of low-resolution low-dose whole-chest CT images for the clinically relevant size range in the context of lung cancer screening is highly challenging, and results are moderate compared to what has been reported in the literature for other clinical contexts. Nodule class size distribution imbalance needs to be considered in the training and evaluation of computer-aided diagnostic systems for producing patient-relevant outcomes.

Coronary artery calcification on low-dose computed tomography: comparison of Agatston and Ordinal Scores.

OBJECTIVE: To compare Agatston with Ordinal Scores for the extent of coronary artery calcification (coronary artery calcium) using nongated low-dose computed tomography (CT) scans. MATERIALS AND METHODS: A total of 631 asymptomatic participants had CT scans from 2010 to 2013. Their Ordinal and Agatston Score were classified into categories. RESULTS: The Ordinal Score Categories showed excellent agreement (weighted kappa of 0.83; 95% confidence interval: 0.79-0.88) with the Agatston Score Categories. CONCLUSIONS: The use of the Ordinal Score is readily obtained on low-dose CT scans that are used for CT screening for lung cancer and these scores are useful for risk stratification of coronary artery disease.