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Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

To evaluate the potential value of machine learning (ML)-based histogram analysis (or first-order texture analysis) on T2-weighted magnetic resonance imaging (MRI) for predicting consistency of pituitary macroadenomas (PMA) and to compare it with that of signal intensity ratio (SIR) evaluation.

Methods

Fifty-five patients with 13 hard and 42 soft PMAs were included in this retrospective study. Histogram features were extracted from coronal T2-weighted original, filtered and transformed MRI images by manual segmentation. To achieve balanced classes (38 hard vs 42 soft), multiple samples were obtained from different slices of the PMAs with hard consistency. Dimension reduction was done with reproducibility analysis, collinearity analysis and feature selection. ML classifier was artificial neural network (ANN). Reference standard for the classifications was based on surgical and histopathological findings. Predictive performance of histogram analysis was compared with that of SIR evaluation. The main metric for comparisons was the area under the receiver operating characteristic curve (AUC).

Results

Only 137 of 162 features had excellent reproducibility. Collinearity analysis yielded 20 features. Feature selection algorithm provided six texture features. For histogram analysis, the ANN correctly classified 72.5% of the PMAs regarding consistency with an AUC value of 0.710. For SIR evaluation, accuracy and AUC values were 74.5% and 0.551, respectively. Considering AUC values, ML-based histogram analysis performed better than SIR evaluation (z = 2.312, p = 0.021).

Conclusion

ML-based T2-weighted MRI histogram analysis might be a useful technique in predicting the consistency of PMAs, with a better predictive performance than that of SIR evaluation.

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Funding

No funding was received for this study.

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Authors and Affiliations

  1. Department of Radiology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey

    Amalya Zeynalova, Naci Kocer, Osman Kizilkilic & Civan Islak

  2. Department of Radiology, Istanbul Training and Research Hospital, Istanbul, Turkey

    Burak Kocak

  3. Department of Radiology, Buyukcekmece Mimar Sinan State Hospital, Istanbul, Turkey

    Emine Sebnem Durmaz

  4. Department of Pathology, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey

    Nil Comunoglu, Kerem Ozcan & Gamze Ozcan

  5. Department of Neurosurgery, Istanbul Training and Research Hospital, Istanbul, Turkey

    Okan Turk

  6. Department of Neurosurgery, Cerrahpasa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey

    Necmettin Tanriover

Authors
  1. Amalya Zeynalova
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  2. Burak Kocak
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Correspondence to Burak Kocak.

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All procedures performed in the studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Zeynalova, A., Kocak, B., Durmaz, E.S. et al. Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI. Neuroradiology 61, 767–774 (2019). https://doi.org/10.1007/s00234-019-02211-2

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  • DOI: https://doi.org/10.1007/s00234-019-02211-2

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