Pulmonary Nodules Detected at Lung Cancer Screening: Interobserver Variability of Semiautomated Volume Measurements

Gietema, Hester A.; Wang, Ying; Xu, Dongming; Klaveren, Rob J. van; Koning, Harry J. de; Scholten, Ernst Th.; Verschakelen, Johny; Kohl, Gerhard; Oudkerk, Matthijs; Prokop, Mathias

doi:10.1148/radiol.2411050860

Cited by 101 publications

(77 citation statements)

References 20 publications

(28 reference statements)

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“…15,16 Annual CT screening studies were interpreted first at the local institution and then again at a central site. CT scans from the NELSON study have been used by investigators associated with the project to investigate, for example, interobserver variability of semiautomated lung nodule volume measurements, 17 the discrimination between benign and malignant nodules, 18,19 automated lung nodule detection, 20,21 and automated lung segmentation. 22 The research value of image databases acquired during clinical studies has been realized in other anatomic sites as well, such as CT colonography.…”

Section: Introductionmentioning

confidence: 99%

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

et al. 2011

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Purpose:The development of computer-aided diagnostic ͑CAD͒ methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography ͑CT͒ scans. The Lung Image Database Consortium ͑LIDC͒ and Image Database Resource Initiative ͑IDRI͒ completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute ͑NCI͒, further advanced by the Foundation for the National Institutes of Health ͑FNIH͒, and accompanied by the Food and Drug Administration ͑FDA͒ through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ͑"noduleՆ 3 mm," "noduleϽ 3 mm," and "non-noduleՆ 3 mm"͒. In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results:The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "noduleՆ 3 mm" by at least one radiologist, of which 928 ͑34.7%͒ received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions:The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

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Section: Introductionmentioning

confidence: 99%

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

et al. 2011

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“…The second readers were unaware of the conclusions of the first readers. In case of discrepancy, the final decision was made by a third reader (10).…”

Section: Nelson Screen Resultsmentioning

confidence: 99%

“…Gietema et al found that interobserver correlation was very high (r = 0.99) in small-to-intermediate size (15- 500 mm 3 ) lung nodules (10). It was also found that variability on volume measurements is related to nodule size, morphology and location (16).…”

Section: Additional Results Of the Nelson Studymentioning

confidence: 99%

Screening for lung cancer by imaging: the NELSON study

Oudkerk

Heuvelmans

2013

Journal of the Belgian Society of Radiology

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“…In a recent study by Gietema et al, (23) Spearman correlation coefficient analysis revealed excellent interobserver agreement between two readers for the measure of nodule volumes using a semi-automated software program in which a spherical template is used to separate nodules from surrounding structures (r = 0.99). The segmentation of small or irregularly shaped nodules was less reproducible.…”

Section: Discussionmentioning

confidence: 98%

The Effect of Lung Volume on Nodule Size on CT

et al. 2007

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Rationale and objectives-To determine how measures of nodule diameter and volume on computed tomography (CT) vary with changes in inspiratory level.Materials and Methods-CT scans were performed with inspiration suspended at total lung capacity (TLC) and then at residual volume (RV) in 41 subjects, in whom 75 indeterminate lung nodules were detected. A fully automated contouring program was used to segment the lungs; followed by segmentation of all nodules and the corresponding lobe using semi-automated contouring in both TLC and RV scans. The percent changes in lung and lobar volumes between TLC and RV were correlated with percent changes in nodule diameters and volumes.Results-Both nodule diameter and volume varied non-uniformly from TLC to RV some nodules decreased in size, while others increased. There was a 16.8% mean change in absolute volume across all nodules. Stratified by size, the mean value of the absolute percent volume changes for nodules ≥ 5 mm and < 5 mm were not significantly different (p = 0.26). Stratified by maximum attenuation, the mean value of the absolute percent volume changes between the TLC and RV series for noncalcified (17.7%, SD = 13.1) and completely calcified nodules (8.6% SD = 5.7) were significantly different (p < 0.05).Conclusion-Significant differences in nodule size were measured between TLC and RV scans. This has important implications for standardizing acquisition protocols in any setting where size, and more importantly, size change are being used for purposes of lung cancer staging, nodule characterization, or treatment response assessment.

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Pulmonary Nodules Detected at Lung Cancer Screening: Interobserver Variability of Semiautomated Volume Measurements

Cited by 101 publications

References 20 publications

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

Screening for lung cancer by imaging: the NELSON study

The Effect of Lung Volume on Nodule Size on CT

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