Dataset Information

Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography.

ABSTRACT: OBJECTIVE:The aim of this study was to evaluate whether a computer-aided diagnosis (CAD) system improves interobserver agreement in the interpretation of lung nodules at low-dose computed tomography (CT) screening for lung cancer. MATERIALS AND METHODS:Baseline low-dose screening CT examinations from 134 participants enrolled in the National Lung Screening Trial were reviewed by 7 chest radiologists. All participants consented to the use of their deidentified images for research purposes. Screening results were classified as positive when noncalcified nodules larger than 4 mm in diameter were present. Follow-up evaluation was recommended according to the nodule diameter: 4 mm or smaller, more than 4 to 8 mm, and larger than 8 mm. When multiple nodules were present, recommendations were based on the largest nodule. Readers initially assessed the nodule presence visually and measured the average nodule diameter manually. Revision of their decisions after reviewing the CAD marks and size measurement was allowed. Interobserver agreement evaluated using multirater ? statistics was compared between initial assessment and that with CAD. RESULTS:Multirater ? values for the positivity of the screening results and follow-up recommendations were improved from moderate (? = 0.53-0.54) at initial assessment to good (? = 0.66-0.67) after reviewing CAD results. The average percentage of agreement between reader pairs on the positivity of screening results and follow-up recommendations per case was also increased from 77% and 72% at initial assessment to 84% and 80% with CAD, respectively. CONCLUSION:Computer-aided diagnosis may improve the reader agreement on the positivity of screening results and follow-up recommendations in the assessment of low-dose screening CT.

SUBMITTER: Jeon KN

PROVIDER: S-EPMC3501405 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography.

Jeon Kyung Nyeo KN Goo Jin Mo JM Lee Chang Hyun CH Lee Youkyung Y Choo Ji Yung JY Lee Nyoung Keun NK Shim Mi-Suk MS Lee In Sun IS Kim Kwang Gi KG Gierada David S DS Bae Kyongtae T KT

Investigative radiology 20120801 8

<h4>Objective</h4>The aim of this study was to evaluate whether a computer-aided diagnosis (CAD) system improves interobserver agreement in the interpretation of lung nodules at low-dose computed tomography (CT) screening for lung cancer.<h4>Materials and methods</h4>Baseline low-dose screening CT examinations from 134 participants enrolled in the National Lung Screening Trial were reviewed by 7 chest radiologists. All participants consented to the use of their deidentified images for research p ...[more]

PMID: 22717879

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Project description:OBJECTIVES: To compare the accuracy of pulmonary lobar volumetry using the conventional number of segments method and novel volumetric computer-aided diagnosis using 3D computed tomography images. METHODS: We acquired 50 consecutive preoperative 3D computed tomography examinations for lung tumours reconstructed at 1-mm slice thicknesses. We calculated the lobar volume and the emphysematous lobar volume < -950 HU of each lobe using (i) the slice-by-slice method (reference standard), (ii) number of segments method, and (iii) semi-automatic and (iv) automatic computer-aided diagnosis. We determined Pearson correlation coefficients between the reference standard and the three other methods for lobar volumes and emphysematous lobar volumes. We also compared the relative errors among the three measurement methods. RESULTS: Both semi-automatic and automatic computer-aided diagnosis results were more strongly correlated with the reference standard than the number of segments method. The correlation coefficients for automatic computer-aided diagnosis were slightly lower than those for semi-automatic computer-aided diagnosis because there was one outlier among 50 cases (2%) in the right upper lobe and two outliers among 50 cases (4%) in the other lobes. The number of segments method relative error was significantly greater than those for semi-automatic and automatic computer-aided diagnosis (P < 0.001). The computational time for automatic computer-aided diagnosis was 1/2 to 2/3 than that of semi-automatic computer-aided diagnosis. CONCLUSIONS: A novel lobar volumetry computer-aided diagnosis system could more precisely measure lobar volumes than the conventional number of segments method. Because semi-automatic computer-aided diagnosis and automatic computer-aided diagnosis were complementary, in clinical use, it would be more practical to first measure volumes by automatic computer-aided diagnosis, and then use semi-automatic measurements if automatic computer-aided diagnosis failed.

Project description:To evaluate the interobserver variability in descriptions of breast masses by dedicated breast imagers and radiology residents and determine how any differences in lesion description affect the performance of a computer-aided diagnosis (CAD) computer classification system.Institutional review board approval was obtained for this HIPAA-compliant study, and the requirement to obtain informed consent was waived. Images of 50 breast lesions were individually interpreted by seven dedicated breast imagers and 10 radiology residents, yielding 850 lesion interpretations. Lesions were described with use of 11 descriptors from the Breast Imaging Reporting and Data System, and interobserver variability was calculated with the Cohen κ statistic. Those 11 features were selected, along with patient age, and merged together by a linear discriminant analysis (LDA) classification model trained by using 1005 previously existing cases. Variability in the recommendations of the computer model for different observers was also calculated with the Cohen κ statistic.A significant difference was observed for six lesion features, and radiology residents had greater interobserver variability in their selection of five of the six features than did dedicated breast imagers. The LDA model accurately classified lesions for both sets of observers (area under the receiver operating characteristic curve = 0.94 for residents and 0.96 for dedicated imagers). Sensitivity was maintained at 100% for residents and improved from 98% to 100% for dedicated breast imagers. For residents, the computer model could potentially improve the specificity from 20% to 40% (P < .01) and the κ value from 0.09 to 0.53 (P < .001). For dedicated breast imagers, the computer model could increase the specificity from 34% to 43% (P = .16) and the κ value from 0.21 to 0.61 (P < .001).Among findings showing a significant difference, there was greater interobserver variability in lesion descriptions among residents; however, an LDA model using data from either dedicated breast imagers or residents yielded a consistently high performance in the differentiation of benign from malignant breast lesions, demonstrating potential for improving specificity and decreasing interobserver variability in biopsy recommendations.

Dataset Information

Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography.

Publications

Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography.

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