Peoperative prediction of pituitary neuroendocrine tumors consistency based on multiparametric MRI radiomics: a multicenter study

Background To investigate the clinical value of preoperative prediction of pituitary neuroendocrine tumors (PitNETs) consistency based on multiparametric magnetic resonance imaging (mpMRI) radiomics and to use a multicenter validation set to evaluate the effectiveness of the prediction model. Methods The clinical data of 137 patients with PitNETs who underwent preoperative mpMRI were retrospectively analyzed, and tumor consistency was classified as soft or hard according to the surgical records. The patients were randomly divided into a training set (n = 108) and an internal validation set (n = 29). Preoperative baseline T1- and T2-weighted (T1/T2WI) and contrast-enhanced (CE) T1 images of the pituitary gland were collected, and each tumor was manually segmented to generate two-dimensional (2D) and three-dimensional (3D) regions of interest (ROIs). Radiomics features were extracted, and predictive features were screened using the variance threshold, single variable selection, and least absolute shrinkage and selection operator methods. Single and multifactorial factors were used to analyze the high-risk clinical risk factors and establish clinical models. A logistic regression classifier was used to construct a radiomics signature based on the 2D and 3D ROIs. A combined model of the clinical characteristics and radiomics features was constructed, and a nomogram was drawn. The robustness and accuracy of the prediction model were tested using multicenter clinical data as an external validation set. A receiver operating characteristic (ROC) curve was used to evaluate the predictive effectiveness of the models, and the area under the curve (AUC), accuracy, sensitivity, and specificity of each model were analyzed and compared. Calibration curves and decision curve analysis (DCA) were used to evaluate the clinical reliability of the predictive models. Results In total, 4224 and 5061 radiomics features were extracted and 28 and 15 predictive features were selected based on the 2D and 3D ROIs, respectively. The 3D-multi (T1WI + T2WI + CE-T1) radiomics signature had the highest prediction efficiency. AUCs of the training and the internal validation sets were 0.793 (95% confidence interval(CI): 0.711–0.859) and 0.798 (95% CI: 0.643–0.942), respectively. The 2D and 3D ROI combined clinical-radiomics models had the highest prediction efficiency, with AUCs of 0.894 (95% CI: 0.832–0.942) and 0.813 (95% CI: 0.667–0.926) in the training and internal validation sets, respectively. Compared with the clinical model, the combined clinical-radiomics model and radiomics signature were more effective in predicting tumor consistency. In addition, the results of the external validation set showed that the prediction model was highly robust, and the DCA of the calibration curve showed that the prediction model had good clinical application value. Conclusions The mpMRI (T1WI + T2WI + CE-T1) radiomics model effectively and accurately predicted PitNET consistency before surgery, and the prediction efficiencies of the radiomics models based on 2D and 3D ROIs were different.