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Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas

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Abstract

Purpose

To investigate whether radiomic features from magnetic resonance image (MRI) can predict the granulation pattern of growth hormone (GH)-secreting pituitary adenoma patients.

Methods

Sixty-nine pathologically proven acromegaly patients (densely granulated [DG] = 50, sparsely granulated [SG] = 19) were included. Radiomic features (n = 214) were extracted from contrast-enhancing and total tumor portions from T2-weighted (T2) MRIs. Imaging features were selected using a least absolute shrinkage and selection operator (LASSO) logistic regression model with fivefold cross-validation. Diagnostic performance for predicting granulation pattern was compared with that for qualitative T2 signal intensity assessment and T2 relative signal intensity (rSI) using the area under the receiver operating characteristics curve (AUC).

Results

Four significant radiomic features from the contrast-enhancing tumor (1 from shape, 1 from first order feature, and 2 from second order features) were selected by LASSO for model construction. The radiomics model showed an AUC, accuracy, sensitivity, and specificity of 0.834 (95% confidence interval [CI] 0.738–0.930), 73.7%, 74.0%, and 73.9%, respectively. The radiomics model showed significantly better performance than the model using qualitative T2 signal intensity assessment (AUC 0.597 [95% CI 0.447–0.747], P = 0.009) and T2 rSI (AUC 0.647 [95% CI 0.523–0.759], P = 0.037).

Conclusion

Radiomic features may be useful biomarkers to differentiate granulation pattern of GH-secreting pituitary adenoma patients, and showed better performance than qualitative assessment or rSI evaluation.

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Data availability

The datasets generated during and analyzed during the current study are not publicly available due its proprietary nature, but are available from the corresponding author for validation of our results upon request with permission from all of the co-authors.

Code availability

The code for statistical analysis performed by statistical software R (version 4.0.1; R Foundation for Statistical Computing, Vienna, Austria) is available from the corresponding author for validation of our results upon request with permission from all of the co-authors.

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Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1I1A1A01071648).

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Authors and Affiliations

  1. Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea

    Yae Won Park, Sung Soo Ahn & Seung-Koo Lee

  2. Pituitary Tumor Center, Severance Hospital, Seoul, Korea

    Yae Won Park, Sung Soo Ahn, Cheol Ryong Ku, Eui Hyun Kim, Eun Jig Lee & Seung-Koo Lee

  3. Integrated Science and Engineering Division, Underwood International College, Yonsei University, Seoul, Korea

    Yunjun Kang

  4. Department of Endocrinology, Yonsei University College of Medicine, Seoul, Korea

    Cheol Ryong Ku & Eun Jig Lee

  5. Department of Neurosurgery, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea

    Eui Hyun Kim

  6. Department of Pathology, Yonsei University College of Medicine, Seoul, Korea

    Se Hoon Kim

  7. Department of Neurosurgery, Ewha Womans University College of Medicine, Seoul, Korea

    Sun Ho Kim

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  1. Yae Won Park
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Contributions

YWP and EHK designed the study. CRK, EHK, SHK, EJL, and SHK managed the patient recruitment and data acquisition. YK, SSA, and S-KL designed the radiomics pipeline and performed the radiomics analyses. YWP wrote the first draft of the manuscript and performed statistical analysis. EHK provided the critical revision of the manuscript. SHK supervised the manuscript. All authors contributed to and approved the final manuscript.

Corresponding author

Correspondence to Eui Hyun Kim.

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All authors declare that they have no conflict of interest.

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The Yonsei University Institutional Review Board waived the need for obtaining informed patient consent for this retrospective study. All methods were carried out in accordance with relevant guidelines and regulation.

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Informed consent was waived by the IRB due to the determination of minimal risk of the study.

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Park, Y.W., Kang, Y., Ahn, S.S. et al. Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas. Pituitary 23, 691–700 (2020). https://doi.org/10.1007/s11102-020-01077-5

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