A Preoperative MRI-Based Radiomics-Clinicopathological Classifier to Predict the Recurrence of Pituitary Macroadenoma Within 5 Years

Yu Zhang; Yuqi Luo; Xin Kong; Tao Wan; Yunling Long; Jun Ma

doi:10.3389/fneur.2021.780628

A Preoperative MRI-Based Radiomics-Clinicopathological Classifier to Predict the Recurrence of Pituitary Macroadenoma Within 5 Years

Front Neurol. 2022 Jan 5:12:780628. doi: 10.3389/fneur.2021.780628. eCollection 2021.

Authors

Yu Zhang¹, Yuqi Luo¹, Xin Kong¹, Tao Wan², Yunling Long^{3

4}, Jun Ma¹

Affiliations

¹ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
² School of Biomedical Science and Medical Engineering, Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.
³ Department of Biomedical Engineering, School of Biomedical Engineering, Capital Medical University, Beijing, China.
⁴ Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Beijing, China.

Abstract

Objective: To investigate the ability of a MRI-based radiomics-clinicopathological model to predict pituitary macroadenoma (PMA) recurrence within 5 years. Materials and Methods: We recruited 74 recurrent and 94 non-recurrent subjects, following first surgery with 5-year follow-up data. Univariate and multivariate analyses were conducted to identify independent clinicopathological risk factors. Two independent and blinded neuroradiologists used 3D-Slicer software to manually delineate whole tumors using preoperative axial contrast-enhanced T1WI (CE-T1WI) images. 3D-Slicer was then used to extract radiomics features from segmented tumors. Dimensionality reduction was carried out by the least absolute shrinkage and selection operator (LASSO). Two multilayer perceptron (MLP) models were established, including independent clinicopathological risk factors (Model 1) and a combination of screened radiomics features and independent clinicopathological markers (Model 2). The predictive performance of these models was evaluated by receiver operator characteristic (ROC) curve analysis. Results: In total, 1,130 features were identified, and 4 of these were selected by LASSO. In the test set, the area under the curve (AUC) of Model 2 was superior to Model 1 {0.783, [95% confidence interval (CI): 0.718-.860] vs. 0.739, (95% CI: 0.665-0.818)}. Model 2 also yielded the higher accuracy (0.808 vs. 0.692), sensitivity (0.826 vs. 0.652), and specificity (0.793 vs. 0.724) than Model 1. Conclusions: The integrated classifier was superior to a clinical classifier and may facilitate the prediction of individualized prognosis and therapy.

Keywords: deep learning; multilayer perceptron; pituitary macroadenoma; predictive model; recurrence.