MRI-based model for accurate prediction of P53 gene status in gliomas

Abstract

The accurate diagnosis and treatment of gliomas depends largely on the understanding of the P53 gene status. In our study, we presented a robust deep learning model, CTD-RegNet (improved RegNet integrating CNN, vision transformer, and truth discovery), tailored for predicting P53 gene status in gliomas. Our model addressed common challenges of existing deep learning models, such as incomplete feature extraction and uncertainty. First, the model used the RegNet network as a basis for predicting P53 gene mutations by skillfully extracting heterogeneous features. Next, the RegNet network was enhanced by integrating the CNN and ViT modules to optimise feature extraction and computational efficiency. Finally, using the truth discovery algorithm, we iteratively refined model uncertainties, thereby improving prediction accuracy. Our experiments demonstrated the effectiveness of the CTD-RegNet model, achieving an impressive accuracy of 95.57% and an AUC score of 0.9789, outperforming existing P53 gene status prediction models. The non-invasive nature of our model minimised the economic burden and physical and psychological stress on patients, while providing critical insights for accurate clinical diagnosis and treatment of gliomas.

Keywords

• glioma
• p53
• deep learning
• truth discovery
• uncertainty calibration