利妥昔单抗时代弥漫性大b细胞淋巴瘤的风险分层

O. Markovic
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摘要

介绍。弥漫性大b细胞淋巴瘤是一组以病理和生物学异质性和不同临床结局为特征的实体。由于明显的异质性,预后生物标志物在识别高风险患者方面非常重要,这些患者可能从更积极的方法或新的治疗方式中受益。一些预后评分系统已经建立并应用于预测弥漫性b大细胞淋巴瘤患者的生存。第一个建立的NHL患者预后系统是国际预后指数,其变体修订国际预后指数和国家综合癌症网络-国际预后指数随后在免疫化疗时代被引入。由于临床评分的辨别力不是最优的,为了改善风险分层,特别是在治疗失败风险最高的高危人群中,人们探索了其他策略。在这方面,有一种趋势是将遗传和分子生物标志物以及预后体细胞突变整合到标准化和个性化的风险分层模型中,这将在常规临床实践中得到广泛应用。最近基于机器学习方法的研究结果表明,结合临床、遗传和分子参数,以及临床参数与新的定量正电子发射断层扫描参数(如代谢肿瘤体积和传播特征以及循环肿瘤DNA水平分析)相结合,可以实现最佳的风险分层。本文对这些新的风险分层模型的研究进行了综述。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Risk-stratification in diffuse large B-cell lymphoma in the rituximab era
Introduction. Diffuse large B-cell lymphoma represents a group of entities characterized by pathological and biological heterogeneity and different clinical outcomes. Due to pronounced heterogeneity, prognostic biomarkers are of great importance in identifying high-risk patients who might benefit from more aggressive approaches or new therapeutic modalities. Several prognostic score systems have been established and applied to predict the survival of patients with diffuse B-large cell lymphoma. The first established prognostic system for NHL patients is the International Prognostic Index, its variations Revised International Prognostic Index and National Comprehensive Cancer Network- International Prognostic Index were subsequently introduced in the era of immunochemotherapy. As the discriminative power of clinical scores is suboptimal, other strategies have been explored in order to improve risk stratification, especially in the high-risk group of patients who have the highest risk of treatment failure. In this regard, there is a tendency to integrate genetic and molecular biomarkers and prognostic somatic mutations into standardized and personalized models for risk stratification that would have a wide application in routine clinical practice. The results of recent studies based on machine learning methods have shown that the best risk stratification is achieved by a combination of clinical, genetic and molecular parameters, as well as a combination of clinical parameters with new quantitative Positron Emission Tomography parameters, such as Metabolic Tumor Volume and dissemination features and analysis of circulating tumor DNA levels. This paper provides an overview of studies in which these new risk stratification models were analyzed.
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