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01-07-2019 | Magnetic Resonance Imaging | Diagnostic Neuroradiology

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

Authors: Amalya Zeynalova, Burak Kocak, Emine Sebnem Durmaz, Nil Comunoglu, Kerem Ozcan, Gamze Ozcan, Okan Turk, Necmettin Tanriover, Naci Kocer, Osman Kizilkilic, Civan Islak

Published in: Neuroradiology | Issue 7/2019

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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Title: Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI
Authors: Amalya Zeynalova
Burak Kocak
Emine Sebnem Durmaz
Nil Comunoglu
Kerem Ozcan
Gamze Ozcan
Okan Turk
Necmettin Tanriover
Naci Kocer
Osman Kizilkilic
Civan Islak
Publication date: 01-07-2019
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Artificial Intelligence
Published in: Neuroradiology / Issue 7/2019
Print ISSN: 0028-3940
Electronic ISSN: 1432-1920
DOI: https://doi.org/10.1007/s00234-019-02211-2

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Purpose

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

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