Test-Retest Reproducibility Analysis of Lung CT Image Features.
Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range...
| Publicado en: | Journal of Digital Imaging Vol. 27; no. 6; pp. 805 - 824 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103912431&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103912431 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2014 vid: 27 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103912431 99255675 10.1007/s10278-014-9716-x NLM24990346 103912431 ppf: 805 ppct: 19 formats: fmt: @attributes: type: P tig: atl: Test-Retest Reproducibility Analysis of Lung CT Image Features. aug: au: Balagurunathan, Yoganand Kumar, Virendra Gu, Yuhua Kim, Jongphil Wang, Hua Liu, Ying Goldgof, Dmitry Hall, Lawrence Korn, Rene Zhao, Binsheng Schwartz, Lawrence Basu, Satrajit Eschrich, Steven Gatenby, Robert Gillies, Robert affil: Department of Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center and Research Institute, Tampa USA sug: subj: Carcinoma, Non-Small-Cell Lung Diagnosis Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Methods Algorithms Reproducibility of Results Test-Retest Reliability Correlation Coefficient kappa Statistic Adult Middle Age Aged Aged, 80 and Over Female Male Human Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female Male ab: Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test-retest, the biological range and a feature independence measure. There were 66 (30.14 %) features with concordance correlation coefficient ≥ 0.90 across test-retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R ≥ 0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91 % for a size-based feature and 92 % for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test-retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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