Abstract：
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.
摘要：
脑肿瘤的化疗是根据肿瘤类型量身定制的，grade,和分子标记，这对于预测反应和生存结果至关重要。本文综述了化疗在胶质瘤中的作用。神经胶质神经和神经元肿瘤，室管膜瘤,脉络丛肿瘤,髓母细胞瘤,和脑膜瘤，讨论标准治疗方案和靶向治疗的最新进展。此外，我们对基于MRI的影像组学和深度学习模型整合预测治疗结果的研究进行了综述.基于MRI的影像组学和深度学习模型的进展显着增强了对化疗益处的预测，化疗后的生存预测，并将肿瘤进展与假性进展区分开来。这些非侵入性技术提供了对肿瘤特征和治疗反应的宝贵见解，促进个性化治疗策略。需要进一步的研究来完善这些模型并扩大其在不同脑肿瘤类型中的适用性。
