A radiomics-based interpretable model to predict the pathological grade of pancreatic neuroendocrine tumors.

ABSTRACT:

Objectives

To develop a computed tomography (CT) radiomics-based interpretable machine learning (ML) model to predict the pathological grade of pancreatic neuroendocrine tumors (pNETs) in a non-invasive manner.

Methods

Patients with pNETs who underwent contrast-enhanced abdominal CT between 2010 and 2022 were included in this retrospective study. Radiomics features were extracted, and five radiomics-based ML models, namely logistic regression (LR), random forest (RF), support vector machine (SVM), XGBoost, and GaussianNB, were developed. The performance of these models was evaluated using a time-independent testing set, and metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) were calculated. The accuracy of

SUBMITTER: Ye JY 

PROVIDER: S-EPMC10873440 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature