Upfront Therapy of Aggressive/High-Risk Low-Grade Glioma: Single-Institution Outcome Analysis of Temozolomide-Based Radio-Chemotherapy and Adjuvant Chemotherapy

Predicting response to chemotherapy in brain tumor patients based on MRI features

Chemotherapy in brain tumors is tailored based on tumor type, grade, and molecular markers, which are crucial for predicting responses and survival outcomes. This review summarizes the role of chemotherapy in gliomas, glioneuronal and neuronal tumors, ependymomas, choroid plexus tumors, medulloblastomas, and meningiomas, discussing standard treatment protocols and recent developments in targeted therapies.Furthermore, the studies reporting the integration of MRI-based radiomics and deep learning models for predicting treatment outcomes are reviewed. Advances in MRI-based radiomics and deep learning models have significantly enhanced the prediction of chemotherapeutic benefits, survival prediction following chemotherapy, and differentiating tumor progression with psuedoprogression. These non-invasive techniques offer valuable insights into tumor characteristics and treatment responses, facilitating personalized therapeutic strategies. Further research is warranted to refine these models and expand their applicability across different brain tumor types.