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

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dc.authoridMendi, Bokebatur Ahmet Rasit/0000-0002-6102-2188
dc.authoridBatur, Halitcan/0000-0002-4743-7926
dc.contributor.authorMendi, Boekebatur Ahmet Rasit
dc.contributor.authorBatur, Halitcan
dc.contributor.authorCay, Nurdan
dc.contributor.authorCakir, Banu Topcu
dc.date.accessioned2024-11-07T13:34:39Z
dc.date.available2024-11-07T13:34:39Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractBackground The consistency of pituitary adenomas affects the course of surgical treatment. Purpose To 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 Methods The 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 (rho) 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. Results A 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. Conclusion Radiomics 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.
dc.identifier.doi10.1177/02841851231174462
dc.identifier.endpage2478
dc.identifier.issn0284-1851
dc.identifier.issn1600-0455
dc.identifier.issue8
dc.identifier.pmid37170546
dc.identifier.scopus2-s2.0-85159111265
dc.identifier.scopusqualityQ3
dc.identifier.startpage2470
dc.identifier.urihttps://doi.org/10.1177/02841851231174462
dc.identifier.urihttps://hdl.handle.net/11480/16103
dc.identifier.volume64
dc.identifier.wosWOS:000986281700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSage Publications Ltd
dc.relation.ispartofActa Radiologica
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectPituitary adenoma
dc.subjectradiomics
dc.subjectmagnetic resonance imaging
dc.subjectmachine learning
dc.titleRadiomic analysis of preoperative magnetic resonance imaging for the prediction of pituitary adenoma consistency
dc.typeArticle

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