A deep learning model to predict glioma recurrence using integrated genomic and clinical data.

ABSTRACT:

Background

Gliomas account for approximately 25.5% of all primary brain and central nervous system (CNS) tumors and 80.8% of malignant brain and CNS tumors. The prognosis varies considerably; patients with low-grade gliomas (LGGs) have 5-year survival rates of up to 80%, while patients with higher-grade gliomas (HGGs) often experience rates below 5%. Recurrence is a common challenge, occurring in 52% to 62% of patients with LGGs and 90% of patients with HGGs, complicating clinical management and treatment planning. Currently, no widely available models exist for reliably predicting early glioma recurrence, which is critical for optimizing patient outcomes. Machine learning (ML) and deep learning (DL) techniques have shown promise in predicting recurrence for various cancers, with t

SUBMITTER: Patricoski-Chavez JA 

PROVIDER: S-EPMC12365200 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature