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Radiomic analysis of preoperative magnetic resonance imaging for the prediction of pituitary adenoma consistency

Radiomic analysis of preoperative magnetic resonance imaging for the prediction of pituitary... BackgroundThe consistency of pituitary adenomas affects the course of surgical treatment.PurposeTo evaluate the diagnostic capabilities of radiomics based on T1-weighted (T1W) and T2-weighted (T2W) magnetic resonance imaging (MRI) in conjunction with two machine-learning (ML) techniques (support vector machine [SVM] and random forest classifier [RFC]) for assessing the consistency of pituitary adenomas.Material and MethodsThe institutional database was retrospectively scanned for patients who underwent surgical excision of pituitary adenomas. Surgical notes were accepted as a reference for the adenoma consistency. Radiomics analysis was performed on preoperative coronal 3.0T T1W and T2W images. First- and second-order parameters were calculated. Inter-observer reproducibility was assessed with Spearman's Correlation (ρ) and intra-observer reproducibility was evaluated with the intraclass correlation coefficient (ICC). Least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. SVM and RFC were used as ML methods.ResultsA total of 52 patients who produced 206 regions of interest (ROIs) were included. Twenty adenomas that produced 88 ROIs had firm consistency. There was both inter-observer and intra-observer reproducibility. Ten parameters that were based on T2W images with high discriminative power and without correlation were chosen by LASSO. The diagnostic performance of SVM and RFC was as follows: sensitivity = 95.580% and 92.950%, specificity = 83.670% and 88.420%, area under the curve = 0.956 and 0.904, respectively.ConclusionRadiomics analysis based on T2W MRI combined with various ML techniques, such as SVM and RFC, can provide preoperative information regarding pituitary adenoma consistency with high diagnostic accuracy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Radiologica SAGE

Radiomic analysis of preoperative magnetic resonance imaging for the prediction of pituitary adenoma consistency

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References (35)

Publisher
SAGE
Copyright
© The Foundation Acta Radiologica 2023
ISSN
0284-1851
eISSN
1600-0455
DOI
10.1177/02841851231174462
Publisher site
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Abstract

BackgroundThe consistency of pituitary adenomas affects the course of surgical treatment.PurposeTo evaluate the diagnostic capabilities of radiomics based on T1-weighted (T1W) and T2-weighted (T2W) magnetic resonance imaging (MRI) in conjunction with two machine-learning (ML) techniques (support vector machine [SVM] and random forest classifier [RFC]) for assessing the consistency of pituitary adenomas.Material and MethodsThe institutional database was retrospectively scanned for patients who underwent surgical excision of pituitary adenomas. Surgical notes were accepted as a reference for the adenoma consistency. Radiomics analysis was performed on preoperative coronal 3.0T T1W and T2W images. First- and second-order parameters were calculated. Inter-observer reproducibility was assessed with Spearman's Correlation (ρ) and intra-observer reproducibility was evaluated with the intraclass correlation coefficient (ICC). Least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. SVM and RFC were used as ML methods.ResultsA total of 52 patients who produced 206 regions of interest (ROIs) were included. Twenty adenomas that produced 88 ROIs had firm consistency. There was both inter-observer and intra-observer reproducibility. Ten parameters that were based on T2W images with high discriminative power and without correlation were chosen by LASSO. The diagnostic performance of SVM and RFC was as follows: sensitivity = 95.580% and 92.950%, specificity = 83.670% and 88.420%, area under the curve = 0.956 and 0.904, respectively.ConclusionRadiomics analysis based on T2W MRI combined with various ML techniques, such as SVM and RFC, can provide preoperative information regarding pituitary adenoma consistency with high diagnostic accuracy.

Journal

Acta Radiologica – SAGE

Published: Aug 1, 2023

Keywords: Pituitary adenoma; radiomics; magnetic resonance imaging; machine learning

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