Radiomics model predicts granulation pattern in growth hormone-secreting pituitary adenomas
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Radiomics model predicts granulation pattern in growth hormone‑secreting pituitary adenomas Yae Won Park1,2 · Yunjun Kang3 · Sung Soo Ahn1,2 · Cheol Ryong Ku2,4 · Eui Hyun Kim2,5 · Se Hoon Kim6 · Eun Jig Lee2,4 · Sun Ho Kim7 · Seung‑Koo Lee1,2
© Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Acromegaly · Granulation pattern · Growth hormone-secreting pituitary adenoma · Magnetic resonance imaging · Pituitary neoplasms · Radiomics
Introduction
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11102-020-01077-5) contains supplementary material, which is available to authorized users. * Eui Hyun Kim [email protected] 1
Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
2
Pituitary Tumor Center, Severance Hospital, Seoul, Korea
3
Integrated Science and Engineering Division, Underwood International College, Yonsei University, Seoul, Korea
Acromegaly is most frequently caused by growth hormone (GH)-secreting pituitary adenomas, and is associated with increased morbidity and mortality [1]. Radical surgical resection is considered as the first-line treatment option [1]. However, in case of patients who are not suitable for surgery, with 4
Department of Endocrinology, Yonsei University Colle
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