Non-invasive prediction of Ki-67 and p53 biomarkers in spinal ependymoma via deep learning: using multimodal magnetic resonance imaging and clinical data

This study developed a deep learning framework for non-invasive prediction of Ki-67 and p53 in spinal ependymomas, integrating multimodal MRI and clinical data. The SegFormer model achieved high-precision segmentation, ensuring robust feature extraction. LGBMNet, combining Multilayer Perceptron and Light Gradient Boosting Machine, demonstrated strong predictive performance. Our results confirm that deep learning can effectively predict tumor biomarkers preoperatively, aiding precision neurosurgery.