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Discrimination between non-functioning pituitary adenomas and hypophysitis using machine learning methods based on magnetic resonance imaging‑derived texture features

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Abstract

Purpose

Hypophysitis is a heterogeneous condition that includes inflammation of the pituitary gland and infundibulum, and it can cause symptoms related to mass effects and hormonal deficiencies. We aimed to evaluate the potential role of machine learning methods in differentiating hypophysitis from non-functioning pituitary adenomas.

Methods

The radiomic parameters obtained from T1A-C images were used. Among the radiomic parameters, parameters capable of distinguishing between hypophysitis and non-functioning pituitary adenomas were selected. In order to avoid the effects of confounding factors and to improve the performance of the classifiers, parameters with high correlation with each other were eliminated. Machine learning algorithms were performed with the combination of gray-level run-length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray-level co-occurrence entropy.

Results

A total of 34 patients were included, 17 of whom had hypophysitis and 17 had non-functioning pituitary adenomas. Among the 38 radiomics parameters obtained from post-contrast T1-weighted images, 10 tissue features that could differentiate the lesions were selected. Machine learning algorithms were performed using three selected parameters; gray level run length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray level co-occurrence entropy. Error matrices were calculated by using the machine learning algorithm and it was seen that support vector machines showed the best performance in distinguishing the two lesion types.

Conclusions

Our analysis reported that support vector machines showed the best performance in distinguishing hypophysitis from non-functioning pituitary adenomas, emphasizing the importance of machine learning in differentiating the two lesions.

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Authors and Affiliations

  1. Division of Endocrinology and Metabolism, Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey

    Serdar Sahin, Emre Durcan, Hande Mefkure Ozkaya & Pinar Kadioglu MD

  2. Department of Radiology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey

    Gokcen Yildiz, Orkun Civan & Osman Kizilkilic

  3. Division of Endocrinology and Metabolism, Department of Internal Medicine, Faculty of Medicine, Hacettepe University, Ankara, Turkey

    Seda Hanife Oguz, Selçuk Dagdelen & Tomris Erbas

  4. Department of Internal Medicine, Cerrahpasa Faculty of Medicine, Istanbul University- Cerrahpasa, Istanbul, Turkey

    Ebru Cicek

  5. Department of Pathology, Cerrahpasa Faculty of Medicine, Istanbul University-Cerrahpasa, Istanbul, Turkey

    Nil Comunoglu & Aysim Buge Oz

  6. Department of Pathology, Faculty of Medicine, Hacettepe University, Ankara, Turkey

    Figen Soylemezoglu

  7. Department of Radiology, Faculty of Medicine, Hacettepe University, Ankara, Turkey

    Kader Karli Oguz

  8. Cerrahpasa Medical Faculty, Department of Internal Medicine, Division of Endocrinology and Metabolism, Istanbul University-Cerrahpasa, Kocamustafapasa Street No: 53, 34098, Fatih, Istanbul, Turkey

    Pinar Kadioglu MD

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  1. Serdar Sahin
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  2. Gokcen Yildiz
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  10. Figen Soylemezoglu
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  11. Kader Karli Oguz
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  12. Selçuk Dagdelen
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  15. Pinar Kadioglu MD
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Corresponding author

Correspondence to Pinar Kadioglu MD.

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Ethical approval:

This study was approved by the Local Ethics Committee of Cerrahpasa Faculty of Medicine. The study adhered to the Tenets of Helsinki.

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The authors declare they have no financial interests.

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The authors declare that there is no conflict of interest.

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All participants were informed about the study protocol and written informed consent was obtained from each individual before enrolment in the study.

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Sahin, S., Yildiz, G., Oguz, S.H. et al. Discrimination between non-functioning pituitary adenomas and hypophysitis using machine learning methods based on magnetic resonance imaging‑derived texture features. Pituitary 25, 474–479 (2022). https://doi.org/10.1007/s11102-022-01213-3

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  • DOI: https://doi.org/10.1007/s11102-022-01213-3

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