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

作者：Cirujeda Pol^*; Cid Yashin Dicente; Muller Henning; Rubin Daniel; Aguilera Todd A; Loo Billy W Jr; Diehn Maximilian; Binefa Xavier; Depeursinge Adrien

来源：IEEE Transactions on Medical Imaging, 2016, 35(12): 2620-2630.

DOI：10.1109/TMI.2016.2591921

• Cancer recurrence
• computer-aided diagnosis
• lung computed tomography
• radiomics
• Riesz-covariance model
• texture classification

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.

• 出版日期2016-12