Inherent Variability of CT Lung Nodule Measurements In Vivo Using Semiautomated Volumetric Measurements

Goodman, Lawrence R.; Gülsün, Meltem; Washington, Lacey; Nagy, Paul; Piacsek, Kelly L.

doi:10.2214/ajr.04.1821

Cited by 134 publications

(102 citation statements)

References 18 publications

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“…The interscan standard deviation of the mean was 13.1 % (confidence limits, ± 25.6%), signaling the important and predominant influence of this factor on measurement reproducibility. In this study, the authors acknowledged that lung volumes were not controlled between scans, which may have contributed to the interscan variability they observed (22).…”

Section: Discussionmentioning

confidence: 96%

“…In another recent study evaluating interobserver agreement (repeatability) and interscan agreement (reproducibility) of lung nodule volume measurements, Goodman et al used a fully automatic segmentation software program to compute the volumes of nodules scanned multiple times during the same session. The study was done on 50 nodules of less than 20 mm diameter (22). The authors reported a mean interobserver variability of 0.02% (SD = 0.73%), and concluded that interobserver variability was minimal (22).…”

Section: Discussionmentioning

confidence: 99%

“…The study was done on 50 nodules of less than 20 mm diameter (22). The authors reported a mean interobserver variability of 0.02% (SD = 0.73%), and concluded that interobserver variability was minimal (22). The interscan standard deviation of the mean was 13.1 % (confidence limits, ± 25.6%), signaling the important and predominant influence of this factor on measurement reproducibility.…”

Section: Discussionmentioning

confidence: 99%

“…One major challenge in determining the accuracy of CT volumetry of lung nodules is the variable methodology and statistical reporting used by different investigators, making it difficult to summarize the reported experience (22). Moreover, most current sophisticated nodule segmentation tools still require a small degree of human editing, such that performance reflects the interplay between the user and the software.…”

Section: Discussionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The Effect of Lung Volume on Nodule Size on CT

et al. 2007

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“…Addressing the problem from a different perspective is the active interest in developing computer assisted methods that will aid the physician in measuring the size of lesions using volumetric methods [6][7][8][9][10][11]. The challenge here is how to calibrate and validate such methods.…”

Section: Introductionmentioning

confidence: 99%

The Lung Image Database Consortium (LIDC)

Reeves¹,

Biancardi²,

Apanasovich³

et al. 2007

Academic Radiology

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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. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public AccessAuthor Manuscript Acad Radiol. Author manuscript; available in PMC 2008 December 1. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript 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.

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Pulmonary nodule segmentation with CT sample synthesis using adversarial networks

et al. 2019

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Purpose Segmentation of pulmonary nodules is critical for the analysis of nodules and lung cancer diagnosis. We present a novel framework of segmentation for various types of nodules using convolutional neural networks (CNNs). Methods The proposed framework is composed of two major parts. The first part is to increase the variety of samples and build a more balanced dataset. A conditional generative adversarial network (cGAN) is employed to produce synthetic CT images. Semantic labels are generated to impart spatial contextual knowledge to the network. Nine attribute scoring labels are combined as well to preserve nodule features. To refine the realism of synthesized samples, reconstruction error loss is introduced into cGAN. The second part is to train a nodule segmentation network on the extended dataset. We build a three‐dimensional (3D) CNN model that exploits heterogeneous maps including edge maps and local binary pattern maps. The incorporation of these maps informs the model of texture patterns and boundary information of nodules, which assists high‐level feature learning for segmentation. Residual unit, which learns to reduce residual error, is adopted to accelerate training and improve accuracy. Results Validation on LIDC‐IDRI dataset demonstrates that the generated samples are realistic. The mean squared error and average cosine similarity between real and synthesized samples are 1.55×10−2 and 0.9534, respectively. The Dice coefficient, positive predicted value, sensitivity, and accuracy are, respectively, 0.8483, 0.8895, 0.8511, and 0.9904 for the segmentation results. Conclusions The proposed 3D CNN segmentation framework, based on the use of synthesized samples and multiple maps with residual learning, achieves more accurate nodule segmentation compared to existing state‐of‐the‐art methods. The proposed CT image synthesis method can not only output samples close to real images but also allow for stochastic variation in image diversity.

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Inherent Variability of CT Lung Nodule Measurements In Vivo Using Semiautomated Volumetric Measurements

Abstract: Volumetric measurements show minimal interobserver variability (0.018%) but an interscan SEM of 13.1% (confidence limits, +/- 25.6%). Repeatability and reproducibility of volumetric measurements are better than those of linear measurements reported in the literature.

Cited by 134 publications

References 18 publications

The Effect of Lung Volume on Nodule Size on CT

The Effect of Lung Volume on Nodule Size on CT

The Lung Image Database Consortium (LIDC)

Pulmonary nodule segmentation with CT sample synthesis using adversarial networks

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