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Distinguishing Functional from Non-functional Pituitary Macroadenomas with a Machine Learning Analysis

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XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 (MEDICON 2019)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 76))

Abstract

Pituitary adenomas are rare intracranial tumors that are often found incidentally in MR images. On the other hand, radiomics is a new field whose aim is converting images in mineable data; particularly, texture analysis is a postprocessing technique extracting quantitative parameters from the heterogeneity of pixel grey level. In this scenario, machine learning can be applied in order to classify these adenomas into functional and non-functional starting from features extracted through texture analysis on MRI images acquired through a protocol including a coronal T2-weighted Turbo Spin Echo sequence. The boosting of J48, a multinomial logistic regression and K nearest neighbour are implemented employing Knime analytics platform. Excluding J48 whose accuracy was 83.0%, multinomial logistic regression and K nearest neighbour achieved accuracies beyond 92.0% and the Area Under the Curve Receiving Characteristic Operator till 98.4%. Diagnosing correctly this delicate disease is crucial in order to achieve the best management as well as the most appropriate cure for patients. The novelty of this paper lies in proving the ability of the combination of radiomics and machine learning to pre-operatively predict tumoral behavior. Prior to this analysis it was believed that only blood tests or histopathological analysis could provide this information.

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Author information

Authors and Affiliations

  1. Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy

    Ricciardi Carlo, Cuocolo Renato, Ugga Lorenzo, Romeo Valeria & Guadagno Elia

  2. Department of Chemical, Materials and Production Engineering, University of Naples “Federico II”, Naples, Italy

    Cesarelli Giuseppe

  3. Istituto Italiano di Tecnologia, Naples, Italy

    Cesarelli Giuseppe

  4. Department of Public Health, University of Naples “Federico II”, Naples, Italy

    Improta Giovanni

  5. Department of Neurosciences, Reproductive and Odontostomatological Sciences, Division of Neurosurgery, University of Naples “Federico II”, Naples, Italy

    Solari Domenico & Cavallo Luigi Maria

  6. DIETI, University of Naples “Federico II”, Naples, Italy

    Cesarelli Mario

  7. Istituti Clinici Scientifici Maugeri IRCCS, Telese Terme, BN, Italy

    Cesarelli Mario

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  1. Ricciardi Carlo
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  2. Cuocolo Renato
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  3. Cesarelli Giuseppe
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  9. Cavallo Luigi Maria
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  10. Cesarelli Mario
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Correspondence to Ricciardi Carlo .

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Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    Jorge Henriques

  2. Universidade do Minho, Braga, Portugal

    Nuno Neves

  3. University of Coimbra, Coimbra, Portugal

    Paulo de Carvalho

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Carlo, R. et al. (2020). Distinguishing Functional from Non-functional Pituitary Macroadenomas with a Machine Learning Analysis. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_221

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  • DOI: https://doi.org/10.1007/978-3-030-31635-8_221

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31634-1

  • Online ISBN: 978-3-030-31635-8

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