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

ABSTRACT:

Background

Spinal ependymoma prognosis is closely correlated with tumor malignancy and biomarker levels, such as Ki-67 and p53, which reflect cellular proliferation and genetic instability. Despite their clinical significance, current methods to assess these biomarkers rely on invasive postoperative immunohistochemistry (IHC), delaying critical treatment decisions and limiting preoperative planning. While deep learning have revolutionized biomarker prediction in brain tumors, their application to spinal ependymomas remains underexplored due to the rarity of these tumors, insufficient datasets, and the technical challenges of analyzing spinal cord MRI. We used a deep learning model to predict molecular markers for spinal ependymomas using preoperative magnetic resonance imaging (MRI

SUBMITTER: Ma C 

PROVIDER: S-EPMC12777245 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature