A 3-D Riesz-Covariance Texture Model for Prediction of Nodule Recurrence in Lung CT

This paper proposes a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature c...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2016-12, Vol.35 (12), p.2620-2630
Main Authors: Cirujeda, Pol, Cid, Yashin Dicente, Muller, Henning, Rubin, Daniel, Aguilera, Todd A., Loo, Billy W., Diehn, Maximilian, Binefa, Xavier, Depeursinge, Adrien
Format: Article
Language:English
Subjects:
Biomarkers
Biomedical imaging
Cancer
Cancer recurrence
Computed tomography
computer-aided diagnosis
Covariance
Differential geometry
Kernel functions
Lung cancer
Lung carcinoma
lung computed tomography
Lungs
Manifolds (mathematics)
Medical diagnosis
Non-small cell lung carcinoma
Prediction models
radiomics
Riesz-covariance model
Small cell lung carcinoma
Solids
Support vector machines
Texture
texture classification
Three dimensional models
Tumors
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Summary:This paper proposes a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter-variations of the feature space dimensions. When compared to the classical use of the average for feature aggregation, feature covariances preserve spatial co-variations between features. The obtained Riesz-covariance descriptors lie on a manifold governed by Riemannian geometry allowing geodesic measurements and differentiations. The latter property is incorporated both into a kernel for support vector machines (SVM) and a manifold-aware sparse regularized classifier. The effectiveness of the presented models is evaluated on a dataset of 110 patients with non-small cell lung carcinoma (NSCLC) and cancer recurrence information. Disease recurrence within a timeframe of 12 months could be predicted with an accuracy of 81.3-82.7%. The anatomical location of recurrence could be discriminated between local, regional and distant failure with an accuracy of 78.3-93.3%. The obtained results open novel research perspectives by revealing the importance of the nodular regions used to build the predictive models.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2016.2591921