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Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma

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Abstract

Detection of early hepatocellular carcinoma (HCC) is responsible for increasing survival rates in up to 40%. One-class classifiers can be used for modeling early HCC in multidetector computed tomography (MDCT), but demand the specific knowledge pertaining to the set of features that best describes the target class. Although the literature outlines several features for characterizing liver lesions, it is unclear which is most relevant for describing early HCC. In this paper, we introduce an unconstrained GA feature selection algorithm based on a multi-objective Mahalanobis fitness function to improve the classification performance for early HCC. We compared our approach to a constrained Mahalanobis function and two other unconstrained functions using Welch’s t-test and Gaussian Data Descriptors. The performance of each fitness function was evaluated by cross-validating a one-class SVM. The results show that the proposed multi-objective Mahalanobis fitness function is capable of significantly reducing data dimensionality (96.4%) and improving one-class classification of early HCC (0.84 AUC). Furthermore, the results provide strong evidence that intensity features extracted at the arterial to portal and arterial to equilibrium phases are important for classifying early HCC.

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Acknowledgements

The authors would like to thank CNPq, CAPES, and FAPEMIG (Brazil) for the financial support.

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Authors and Affiliations

  1. Biomedical Engineering Lab, Faculty of Electrical Engineering, Federal University of Uberlândia, Av. João Naves de Ávila 2121, Uberlândia, MG, 38408-100, Brazil

    Ricardo de Lima Thomaz, Pedro Cunha Carneiro, Ana Claudia Patrocinio & Alcimar Barbosa Soares

  2. Department of Radiology, General Hospital of Uberlândia, Federal University of Uberlândia, Av. Pará 1720, Uberlândia, MG, 38405-320, Brazil

    João Eliton Bonin & Túlio Augusto Alves Macedo

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  1. Ricardo de Lima Thomaz
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  2. Pedro Cunha Carneiro
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Correspondence to Ricardo de Lima Thomaz.

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Thomaz, R.d., Carneiro, P.C., Bonin, J.E. et al. Novel Mahalanobis-based feature selection improves one-class classification of early hepatocellular carcinoma. Med Biol Eng Comput 56, 817–832 (2018). https://doi.org/10.1007/s11517-017-1736-5

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