Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study

Zhao, Binsheng; Tan, Yongqiang; Tsai, Wei Yann; Schwartz, Lawrence H.; Lin, Liwu

doi:10.1593/tlo.13865

Cited by 111 publications

(106 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While all 41 CT volumes have been used in prior studies [21][22][23][25][26][27], they have never been used as a set for the comparison of segmentation algorithms, as we present here, and there therefore is no scientific overlap between the work described here and prior publications.…”

Section: Datasetsmentioning

confidence: 99%

“…Raunig [20] Kessler [21] Barnhart [26] Within-subject standard deviation (wSD) σ Kessler [21] Within-subject coefficient of variation (wCV) σ/μ Obuchowski (19 ) Repeatability coefficient (RC) BThe least significant difference between two repeated measurements on a case taken under the same conditions^K essler [21] Obuchowski [19] Bland [27] Intra-class correlation coefficient (ICC) Consistency of repeated measures relative to the total variability in the population (assumes independent normally distributed samples)…”

Section: ] Precisionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

et al. 2016

Self Cite

View full text Add to dashboard Cite

Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 μl to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.

show abstract

Section: Datasetsmentioning

confidence: 99%

Section: ] Precisionmentioning

confidence: 99%

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…They have both spatial resolution (voxel size) and gray-level/density resolution (density bin size) which are determined by imaging acquisition techniques and parameters. To date, our knowledge of the reliability of QIFs is limited to studies of CT and PET test-retest reproducibility [16–19], intra- and inter-observer variability [20,21], segmentation method-induced variability [22,23], variation due to CT acquisition parameters (phantom study) [24], and effects of different CT scanners [25]. There was no in vivo study that assessed how CT imaging acquisition parameters such as slice thickness and reconstruction algorithm affect the computations of QIFs, until the recently published same-day repeat CT study [26].…”

Section: Introductionmentioning

confidence: 99%

Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings

et al. 2016

Self Cite

View full text Add to dashboard Cite

ObjectivesRadiomics utilizes quantitative image features (QIFs) to characterize tumor phenotype. In practice, radiological images are obtained from different vendors’ equipment using various imaging acquisition settings. Our objective was to assess the inter-setting agreement of QIFs computed from CT images by varying two parameters, slice thickness and reconstruction algorithm.Materials and MethodsCT images from an IRB-approved/HIPAA-compliant study assessing thirty-two lung cancer patients were included for the analysis. Each scan’s raw data were reconstructed into six imaging series using combinations of two reconstruction algorithms (Lung[L] and Standard[S]) and three slice thicknesses (1.25mm, 2.5mm and 5mm), i.e., 1.25L, 1.25S, 2.5L, 2.5S, 5L and 5S. For each imaging-setting, 89 well-defined QIFs were computed for each of the 32 tumors (one tumor per patient). The six settings led to 15 inter-setting comparisons (combinatorial pairs). To reduce QIF redundancy, hierarchical clustering was done. Concordance correlation coefficients (CCCs) were used to assess inter-setting agreement of the non-redundant feature groups. The CCC of each group was assessed by averaging CCCs of QIFs in the group.ResultsTwenty-three non-redundant feature groups were created. Across all feature groups, the best inter-setting agreements (CCCs>0.8) were 1.25S vs 2.5S, 1.25L vs 2.5L, and 2.5S vs 5S; the worst (CCCs<0.51) belonged to 1.25L vs 5S and 2.5L vs 5S. Eight of the feature groups related to size, shape, and coarse texture had an average CCC>0.8 across all imaging settings.ConclusionsVarying degrees of inter-setting disagreements of QIFs exist when features are computed from CT images reconstructed using different algorithms and slice thicknesses. Our findings highlight the importance of harmonizing imaging acquisition for obtaining consistent QIFs to study tumor imaging phonotype.

show abstract

“…Nonshape features such as textural‐ and intensity‐based features may be more sensitive to intensity and scanner variability . In other words, different CT scanners and scanning parameters such as slice thickness and reconstruction algorithms could affect the resulting textural features . Shape features on the other hand tend to be less sensitive to differences in image intensity and scanner platforms and potentially more predictive of lesion diagnosis compared to texture features.…”

Section: Introductionmentioning

confidence: 99%

An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT

et al. 2017

View full text Add to dashboard Cite

Purpose Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans. While there has been substantial interest in the use of textural analysis for radiomic characterization of lung nodules, relatively less work has been done in shape based characterization of lung nodules, particularly with respect to granulmoas and adenocarcinomas. The primary goal of this study is to evaluate the role of 3D shape features for discrimination of benign granulomas from malignant adenocarcinomas on lung CT images. Towards this end we present an integrated framework for segmentation, feature characterization and classification of these nodules on CT. Methods The nodule segmentation method starts with separation of lung regions from the surrounding lung anatomy. Next, the lung CT scans are projected into and represented in a three dimensional spectral embedding (SE) space, allowing for better determination of the boundaries of the nodule. This then enables the application of a gradient vector flow active contour (SEGvAC) model for nodule boundary extraction. A set of 24 shape features from both 2D slices and 3D surface of the segmented nodules are extracted, including features pertaining to the angularity, spiculation, elongation and nodule compactness. A feature selection scheme, PCA-VIP, is employed to identify the most discriminating set of features to distinguish granulmoas from adenocarcinomas within a learning set of 82 patients. The features thus identified were then combined with a support vector machine classifier and independently validated on a distinct test set comprising 67 patients. The performance of the classifier for both of the training and validation cohorts was evaluated by the area under receiver characteristic curve (ROC). Results We used 82 and 67 studies from two different institutions respectively for training and independent validation of the model and the shape features. The Dice coefficient between automatically segmented nodules by SEGvAC and the manual delineations by expert radiologists (readers) was 0.84 ± 0.04 whereas inter-reader segmentation agreement was 0.79 ± 0.12. We also identified a set of consistent features (Roughness, Convexity and Spherecity) that were found to be strongly correlated across both manual and automated nodule segmentations (R > 0.80, p < 0.0001) and capture the marginal smoothness and 3D compactness of the nodules. On the independent validation set of 67 studies our classifier yielded a ROC AUC of 0.72 and 0.64 for manually- and automatically segmented nodules respectively. On a subset of 20 studies, th...

show abstract

Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study

Abstract: CT slice thickness and reconstruction algorithm can significantly affect the quantification of image features. Thinner (1.25 and 2.5 mm) and thicker (5 mm) slice images should not be used interchangeably. Sharper and smoother reconstructions significantly affect the density-based features.

Cited by 111 publications

References 22 publications

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study

Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings

An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT

Contact Info

Product

Resources

About