肿瘤是威胁人类生命健康的主要疾病之一。尽管放射治疗(放疗)在许多恶性肿瘤的治疗中取得了显著成效,但由于正常组织的损伤和肿瘤细胞的放射抗性,放疗效果常常受到限制。传统的放疗方法存在靶向性差、治疗效果有限、对正常组织的辐射损伤等问题,这使得其治疗效果面临诸多挑战。为了克服这些缺陷并提高治疗效果,放疗增敏策略应运而生。近年来,纳米材料作为一种新型的放疗增敏剂,凭借其优异的物理化学特性和靶向性,成为了放疗增敏研究中的重要方向。纳米材料能够通过多种机制增强肿瘤细胞对辐射的敏感性,从而提高放疗的治疗效果,并减少对正常组织的损伤。本综述回顾了纳米材料在放疗增敏中的作用机制和研究进展,并总结了不同类型的纳米材料的优势与挑战,探讨了其在肿瘤放疗中的应用前景。Radiotherapy (RT) is a common treatment for various malignant tumors;however, its effectiveness is often limited by normal tissue damage and the radiation resistance of tumor cells. In recent years, the application of nanomaterials in tumor radiotherapy has become a research hotspot, particularly in radiosensitization. Nanomaterials can enhance the sensitivity of tumor cells to radiation through various mechanisms, thereby improving the therapeutic effects of RT. This review summarizes the mechanisms and recent advances in the use of different types of nanomaterials (such as metallic nanoparticles, carbon-based nanomaterials, and nano-drug carriers) in radiosensitization. Through strategies such as surface modification, drug loading, and targeted delivery, nanomaterials can enable precise targeted therapy, enhancing the efficacy of radiotherapy.
上尿路上皮癌(UTUC)是一种异质性较高的恶性肿瘤,占尿路上皮肿瘤的5%~10%,其预后受到患者特征、肿瘤病理特性及治疗方式等多种因素的综合影响。尽管根治性肾输尿管切除术(RNU)是治疗UTUC的金标准,但术后复发率和远期生存率差异显著。构建个体化的预后模型对于优化临床决策具有重要意义。近年来,诺模图、机器学习驱动模型、分子生物标志物模型及联合影像学与临床数据的多变量模型在UTUC预后预测中逐渐应用。其中,诺模图凭借直观性和高整合性成为临床预测的常用工具,机器学习模型在处理多模态数据方面表现出优势,分子生物标志物模型揭示了疾病的分子机制,而联合影像学模型通过融合影像和临床数据进一步提升了预测精准性。然而,现有模型的普适性和动态预测能力仍面临挑战,模型依赖于高质量的大规模数据,而临床实践中数据获取和整合存在难点。未来研究应聚焦于多中心、大样本的前瞻性研究以验证模型的可靠性,同时深入探索UTUC的分子机制,开发新的分子标志物,优化辅助治疗的适应症,并推动影像学技术与分子诊断手段的结合,为UTUC患者的精准医学和个体化治疗提供更可靠的工具和方法。Upper tract urothelial carcinoma (UTUC) is a highly heterogeneous malignancy, accounting for 5%~10% of urothelial tumors. Its prognosis is influenced by a combination of patient characteristics, tumor pathology, and treatment strategies. Despite radical nephroureterectomy (RNU) being the gold standard treatment for UTUC, significant variability in postoperative recurrence rates and long-term survival outcomes exists. Developing individualized prognostic models is crucial for optimizing clinical decision-making. Recently, nomograms, machine learning-based models, biomarker-driven molecular models, and multivariate models integrating imaging and clinical data have been increasingly utilized in UTUC prognostic prediction. Among these, nomograms have become widely used for their intuitive and integrative capabilities, machine learning models excel in handling multimodal data, biomarker-driven models uncover the molecular mechanisms of disease, and imaging-based models improve prediction accuracy by combining radiological and clinical data. However, existing models face challenges regarding generalizability and dynamic prediction capabilities, as they often rely on large-scale, high-quality datasets, which are difficult to obtain and integrate in clinical practice. Future research should focus on conducting multicenter, large-scale prospective studies to validate model reliability, exploring molecular mechanisms of UTUC, developing novel biomarkers, optimizing indications for adjuvant therapies, and pro
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