Joint prediction of glioma molecular marker status based on GDI-PMNet.

Background: Determining the status of glioma molecular markers is a problem of clinical importance in medicine. Current medical-imaging-based approaches for this problem suffer from various limitations, such as incomplete fine-grained feature extraction of glioma imaging data and low prediction accuracy of molecular marker status.

Methods: To address these issues, a deep learning method is presented for the simultaneous joint prediction of multi-label statuses of glioma molecular markers. Firstly, a Gradient-aware Spatially Partitioned Enhancement algorithm (GASPE) is proposed to optimize the glioma MR image preprocessing method and to enhance the local detail expression ability; secondly, a Dual Attention module with Depthwise Convolution (DADC) is constructed to improve the fine-grained feature extraction ability by combining channel attention and spatial attention; thirdly, a hybrid model PMNet is proposed, which combines the Pyramid-based Multi-Scale Feature Extraction module (PMSFEM) and the Mamba-based Projection Convolution module (MPCM) to achieve effective fusion of local and global information; finally, an Iterative Truth Calibration algorithm (ITC) is used to calibrate the joint state truth vector output by the model to optimize the accuracy of the prediction results.

Results: Based on GASPE, DADC, ITC and PMNet, the proposed method constructs the Gradient-Aware Dual Attention Iteration Truth Calibration-PMNet (GDI-PMNet) to simultaneously predict the status of glioma molecular markers (IDH1, Ki67, MGMT, P53), with accuracies of 98.31%, 99.24%, 97.96% and 98.54% respectively, achieving non-invasive preoperative prediction, thereby capable of assisting doctors in clinical diagnosis and treatment.

Conclusions: The GDI-PMNet method demonstrates high accuracy in predicting glioma molecular markers, addressing the limitations of current approaches by enhancing fine-grained feature extraction and prediction accuracy. This non-invasive preoperative prediction tool holds significant potential to assist clinicians in glioma diagnosis and treatment, ultimately improving patient outcomes.