Skip to main content
main-content

01.12.2014 | Ausgabe 6/2014

Journal of Digital Imaging 6/2014

Test–Retest Reproducibility Analysis of Lung CT Image Features

Zeitschrift:
Journal of Digital Imaging > Ausgabe 6/2014
Autoren:
Yoganand Balagurunathan, Virendra Kumar, Yuhua Gu, Jongphil Kim, Hua Wang, Ying Liu, Dmitry B. Goldgof, Lawrence O. Hall, Rene Korn, Binsheng Zhao, Lawrence H. Schwartz, Satrajit Basu, Steven Eschrich, Robert A. Gatenby, Robert J. Gillies
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1007/​s10278-014-9716-x) contains supplementary material, which is available to authorized users.

Abstract

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 2 Bet ≥ 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.

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

★ PREMIUM-INHALT
e.Med Interdisziplinär

Mit e.Med Interdisziplinär erhalten Sie Zugang zu allen CME-Fortbildungen und Fachzeitschriften auf SpringerMedizin.de. Zusätzlich können Sie eine Zeitschrift Ihrer Wahl in gedruckter Form beziehen – ohne Aufpreis.

Weitere Produktempfehlungen anzeigen
Zusatzmaterial
ESM 1 (PDF 626 kb)
10278_2014_9716_MOESM1_ESM.pdf
Literatur
Über diesen Artikel

Weitere Artikel der Ausgabe 6/2014

Journal of Digital Imaging 6/2014 Zur Ausgabe
  1. Sie können e.Med Radiologie 14 Tage kostenlos testen (keine Print-Zeitschrift enthalten). Der Test läuft automatisch und formlos aus. Es kann nur einmal getestet werden.

Neu im Fachgebiet Radiologie

Meistgelesene Bücher aus der Radiologie

2016 | Buch

Medizinische Fremdkörper in der Bildgebung

Thorax, Abdomen, Gefäße und Kinder

Dieses einzigartige Buch enthält ca. 1.600 hochwertige radiologische Abbildungen und Fotos iatrogen eingebrachter Fremdmaterialien im Röntgenbild und CT.

Herausgeber:
Dr. med. Daniela Kildal

2011 | Buch

Atlas Klinische Neuroradiologie des Gehirns

Radiologie lebt von Bildern! Der vorliegende Atlas trägt dieser Tatsache Rechnung. Sie finden zu jedem Krankheitsbild des Gehirns Referenzbilder zum Abgleichen mit eigenen Befunden.

Autoren:
Priv.-Doz. Dr. med. Jennifer Linn, Prof. Dr. med. Martin Wiesmann, Prof. Dr. med. Hartmut Brückmann

Mail Icon II Newsletter

Bestellen Sie unseren kostenlosen Newsletter Update Radiologie und bleiben Sie gut informiert – ganz bequem per eMail.

Bildnachweise