Combined clinical and specific positron emission tomography/computed tomography-based radiomic features and machine-learning model in prediction of thymoma risk groups

Conclusions This small dataset study has proposed a machine-learning model by MLP Classifier (ANN) analysis on 18F-FDG PET/CT images, which can predict low risk and high-risk thymoma. This study also demonstrated that the combination of clinical data and specific PET/CT-based radiomic features with image variables can predict thymoma risk groups. However, these results should be supported by studies with larger dataset.
Source: Nuclear Medicine Communications - Category: Nuclear Medicine Tags: Original Articles Source Type: research