Machine-Learning Classifiers in Discrimination of Lesions Located in the Anterior Skull Base

Abstract

The aim of this study was to investigate the diagnostic value of machine-learning models with radiomic features and clinical features in preoperative differentiation of common lesions located in the anterior skull base.Methods: A total of 235 patients diagnosed with pituitary adenoma, meningioma, craniopharyngioma, or Rathke cleft cyst were enrolled in the current study. The discrimination was divided into three groups: pituitary adenoma vs. craniopharyngioma, meningioma vs. craniopharyngioma, and pituitary adenoma vs. Rathke cleft cyst. In each group, five selection methods were adopted to select suitable features for the classifier, and nine machine-learning classifiers were employed to build discriminative models. The diagnostic performance of each combination was evaluated with area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity calculated for both the training group and the testing group. Results:In each group, several classifiers combined with suitable selection methods represented feasible diagnostic performance with AUC of more than 0.80. Moreover, the combination of least absolute shrinkage and selection operator as the feature-selection method and linear discriminant analysis as the classification algorithm represented the best comprehensive discriminative ability. Conclusion:Radiomics-based machine learning could potentially serve as a novel method to assist in discriminating common lesions in the anterior skull base prior to operation.

Development of a Nomogram Based on Preoperative Bi-Parametric MRI and Blood Indices for the Differentiation Between Cystic-Solid Pituitary Adenoma and Craniopharyngioma

“…In the same year, their team used 5 machine learning algorithms to establish different differential diagnosis models for the two tumors based on 17 different features. The best AUC value of the training group was 0.804 and the test group was 0.845, which achieved good diagnostic results (13). Therefore, the non-invasive radiomic features based on the freely available images can be used as a more convenient biomarker for identifying these two tumors.…”

“…Radiomics is an emerging method for such tasks (12). Radiomics can extract a large number of image features in a high-throughput manner from medical images, which can quantitatively and objectively reflect tumor texture and heterogeneity (13)(14)(15). These features are usually impossible to be directly detected by the naked eye.…”

Preoperative Prediction of Meningioma Consistency via Machine Learning-Based Radiomics

“…Radiomics technique and machine learning algorithm have been widely used in many tumors' differential diagnosis and consistency prediction before operation (17)(18)(19)(20). Yang Zhang et al developed a radiomics model that could be used in discrimination of lesions located in the anterior skull base (8). In glioblastoma, Xi Zhang reported a radiomics nomogram including 25 selected features, which performing better than clinical risk factors in survival stratification, and the C-index reached up to 0.974 (21).…”

“…Yang Zhang et al. developed a radiomics model that could be used in discrimination of lesions located in the anterior skull base ( 8 ). In glioblastoma, Xi Zhang reported a radiomics nomogram including 25 selected features, which performing better than clinical risk factors in survival stratification, and the C-index reached up to 0.974 ( 21 ).…”

“…Previously, tumor consistency was predicted according to the signal intensity of T2 weighted images or Fluid attenuated inversion recovery images, but the accuracy was low (7). Radiomics has been considered as a potent approach for noninvasive high-throughput mining of tumor characteristics, which has been applied in many tumors, such as pituitary adenomas, gliomas (8,9). Nonetheless, few studies have focused on radiomics signatures to predict meningioma consistency.…”