Diagnostic performance of MRI-based radiomics models using machine learning approaches for the triple classification of parotid tumors.

ABSTRACT:

Rationale and objectives

Preoperative differentiation of malignant tumors (MT), pleomorphic adenomas (PA), and other benign tumors of the parotid gland is critical to clinical strategy, this study aimed to develop and validate a T2-weighted image (T2WI) based radiomics model through machine learning approaches for the triple classification of parotid gland tumors.

Materials and methods

We retrospectively enrolled 147 patients from January 2010 to July 2022. T2WIs were used to extract radiomics features. Max-Relevance and Min-Redundancy (mRMR) and Extreme Gradient Boosting (XGBoost) algorithms were used to select features. Using a 5-fold cross-validation strategy, radiomics models were constructed using a Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest N

SUBMITTER: Guo J 

PROVIDER: S-EPMC11387325 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature