Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate

An algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed tomography scans. The pleural surface was estimated and used to segment the nodule from the chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural surface but not on the nodule. A 3D surface was estimated from the identified surface points. The segmentation performance of the algorithm was evaluated on a database of 150 solid juxtapleural pulmonary nodules. Segmented images were rated on a scale of 1 to 4 based on visual inspection, with 3 and 4 considered acceptable. This algorithm offers a large improvement in the success rate of juxtapleural nodule segmentation, successfully segmenting 98.0% of nodules compared to 81.3% for a previously published plane-fitting algorithm, which will provide for the development of more robust automated nodule measurement methods

Sublobar resection is equivalent to lobectomy for clinical stage 1A lung cancer in solid nodules

ObjectivesA single randomized trial established lobectomy as the standard of care for the surgical treatment of early-stage non–small cell lung cancer. Recent advances in imaging/staging modalities and detection of smaller tumors have once again rekindled interest in sublobar resection for early-stage disease. The objective of this study was to compare lung cancer survival in patients with non–small cell lung cancer with a diameter of 30 mm or less with clinical stage 1 disease who underwent lobectomy or sublobar resection.MethodsWe identified 347 patients diagnosed with lung cancer who underwent lobectomy (n = 294) or sublobar resection (n = 53) for non–small cell lung cancer manifesting as a solid nodule in the International Early Lung Cancer Action Program from 1993 to 2011. Differences in the distribution of the presurgical covariates between sublobar resection and lobectomy were assessed using unadjusted P values determined by logistic regression analysis. Propensity scoring was performed using the same covariates. Differences in the distribution of the same covariates between sublobar resection and lobectomy were assessed using adjusted P values determined by logistic regression analysis with adjustment for the propensity scores. Lung cancer–specific survival was determined by the Kaplan–Meier method. Cox survival regression analysis was used to compare sublobar resection with lobectomy, adjusted for the propensity scores, surgical, and pathology findings, when adjusted and stratified by propensity quintiles.ResultsAmong 347 patients, 10-year Kaplan–Meier for 53 patients treated by sublobar resection compared with 294 patients treated by lobectomy was 85% (95% confidence interval, 80-91) versus 86% (confidence interval, 75-96) (P = .86). Cox survival analysis showed no significant difference between sublobar resection and lobectomy when adjusted for propensity scores or when using propensity quintiles (P = .62 and P = .79, respectively). For those with cancers 20 mm or less in diameter, the 10-year rates were 88% (95% confidence interval, 82-93) versus 84% (95% confidence interval, 73-96) (P = .45), and Cox survival analysis showed no significant difference between sublobar resection and lobectomy using either approach (P = .42 and P = .52, respectively).ConclusionsSublobar resection and lobectomy have equivalent survival for patients with clinical stage IA non–small cell lung cancer in the context of computed tomography screening for lung cancer

Evaluation of lung MDCT nodule annotation across radiologists and methods

RATIONALE AND OBJECTIVES: Integral to the mission of the National Institutes of Health–sponsored Lung Imaging Database Consortium is the accurate definition of the spatial location of pulmonary nodules. Because the majority of small lung nodules are not resected, a reference standard from histopathology is generally unavailable. Thus assessing the source of variability in defining the spatial location of lung nodules by expert radiologists using different software tools as an alternative form of truth is necessary. MATERIALS AND METHODS: The relative differences in performance of six radiologists each applying three annotation methods to the task of defining the spatial extent of 23 different lung nodules were evaluated. The variability of radiologists’ spatial definitions for a nodule was measured using both volumes and probability maps (p-map). Results were analyzed using a linear mixed-effects model that included nested random effects. RESULTS: Across the combination of all nodules, volume and p-map model parameters were found to be significant at P < .05 for all methods, all radiologists, and all second-order interactions except one. The radiologist and methods variables accounted for 15% and 3.5% of the total p-map variance, respectively, and 40.4% and 31.1% of the total volume variance, respectively. CONCLUSION: Radiologists represent the major source of variance as compared with drawing tools independent of drawing metric used. Although the random noise component is larger for the p-map analysis than for volume estimation, the p-map analysis appears to have more power to detect differences in radiologist-method combinations. The standard deviation of the volume measurement task appears to be proportional to nodule volume

Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe.

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached

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

PurposeTo 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.MethodsELIC 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.ResultsThe 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.ConclusionThis 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

Emphysema Predicts Hospitalisation and Incident Airflow Obstruction among Older Smokers: A Prospective Cohort Study

Emphysema on CT is common in older smokers. We hypothesised that emphysema on CT predicts acute episodes of care for chronic lower respiratory disease among older smokers.Participants in a lung cancer screening study age ≥ 60 years were recruited into a prospective cohort study in 2001-02. Two radiologists independently visually assessed the severity of emphysema as absent, mild, moderate or severe. Percent emphysema was defined as the proportion of voxels ≤ -910 Hounsfield Units. Participants completed a median of 5 visits over a median of 6 years of follow-up. The primary outcome was hospitalization, emergency room or urgent office visit for chronic lower respiratory disease. Spirometry was performed following ATS/ERS guidelines. Airflow obstruction was defined as FEV1/FVC ratio <0.70 and FEV1<80% predicted.Of 521 participants, 4% had moderate or severe emphysema, which was associated with acute episodes of care (rate ratio 1.89; 95% CI: 1.01-3.52) adjusting for age, sex and race/ethnicity, as was percent emphysema, with similar associations for hospitalisation. Emphysema on visual assessment also predicted incident airflow obstruction (HR 5.14; 95% CI 2.19-21.1).Visually assessed emphysema and percent emphysema on CT predicted acute episodes of care for chronic lower respiratory disease, with the former predicting incident airflow obstruction among older smokers

The Lung Image Database Consortium (LIDC):A comparison of different size metrics for pulmonary nodule measurements

RATIONALE AND OBJECTIVES: To investigate the effects of choosing between different metrics in estimating the size of pulmonary nodules as a factor both of nodule characterization and of performance of computer aided detection systems, since the latters are always qualified with respect to a given size range of nodules. MATERIALS AND METHODS: This study used 265 whole-lung CT scans documented by the Lung Image Database Consortium using their protocol for nodule evaluation. Each inspected lesion was reviewed independently by four experienced radiologists who provided boundary markings for nodules larger than 3 mm. Four size metrics, based on the boundary markings, were considered: a uni-dimensional and two bi-dimensional measures on a single image slice and a volumetric measurement based on all the image slices. The radiologist boundaries were processed and those with four markings were analyzed to characterize the inter-radiologist variation, while those with at least one marking were used to examine the difference between the metrics. RESULTS: The processing of the annotations found 127 nodules marked by all of the four radiologists and an extended set of 518 nodules each having at least one observation with three-dimensional sizes ranging from 2.03 to 29.4 mm (average 7.05 mm, median 5.71 mm). A very high inter-observer variation was observed for all these metrics: 95% of estimated standard deviations were in the following ranges [0.49, 1.25], [0.67, 2.55], [0.78, 2.11], and [0.96, 2.69] for the three-dimensional, the uni-dimensional, and the two bi-dimensional size metrics respectively (in mm). Also a very large difference among the metrics was observed: 0.95 probability-coverage region widths for the volume estimation conditional on uni-dimensional, and the two bi-dimensional size measurements of 10mm were 7.32, 7.72, and 6.29 mm respectively. CONCLUSIONS: The selection of data subsets for performance evaluation is highly impacted by the size metric choice. The LIDC plans to include a single size measure for each nodule in its database. This metric is not intended as a gold standard for nodule size; rather, it is intended to facilitate the selection of unique repeatable size limited nodule subsets

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

The Lung Image Database Consortium (LIDC): An Evaluation of Radiologist Variability in the Identification of Lung Nodules on CT Scans

RATIONALE AND OBJECTIVES: The purpose of this study was to analyze the variability of experienced thoracic radiologists in the identification of lung nodules on CT scans and thereby to investigate variability in the establishment of the “truth” against which nodule-based studies are measured. MATERIALS AND METHODS: Thirty CT scans were reviewed twice by four thoracic radiologists through a two-phase image annotation process. During the initial “blinded read” phase, radiologists independently marked lesions they identified as “nodule ≥ 3mm (diameter),” “nodule < 3mm,” or “non-nodule ≥ 3mm.” During the subsequent “unblinded read” phase, the blinded read results of all radiologists were revealed to each of the four radiologists, who then independently reviewed their marks along with the anonymous marks of their colleagues; a radiologist’s own marks then could be deleted, added, or left unchanged. This approach was developed to identify, as completely as possible, all nodules in a scan without requiring forced consensus. RESULTS: After the initial blinded read phase, a total of 71 lesions received “nodule ≥ 3mm” marks from at least one radiologist; however, all four radiologists assigned such marks to only 24 (33.8%) of these lesions. Following the unblinded reads, a total of 59 lesions were marked as “nodule ≥ 3 mm” by at least one radiologist. 27 (45.8%) of these lesions received such marks from all four radiologists, 3 (5.1%) were identified as such by three radiologists, 12 (20.3%) were identified by two radiologists, and 17 (28.8%) were identified by only a single radiologist. CONCLUSION: The two-phase image annotation process yields improved agreement among radiologists in the interpretation of nodules ≥ 3mm. Nevertheless, substantial variabilty remains across radiologists in the task of lung nodule identification