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

Abstract

The consistency of pituitary adenomas affects the course of surgical treatment. 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. 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 (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. 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. 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.