4.2 Review

Updates in Risk Stratification in Myelodysplastic Syndromes

期刊

CANCER JOURNAL
卷 29, 期 3, 页码 138-142

出版社

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/PPO.0000000000000654

关键词

Clonal dynamics; cytogenetics; h-MDS; IPSS; IPSS-M; IPSS-R; myelodysplastic syndromes; mutations; risk stratification; t-MDS

类别

向作者/读者索取更多资源

Risk stratification is crucial for treatment planning in myelodysplastic syndromes. The traditional International Prognostic Scoring System and its revised version rely on laboratory and cytogenetic data to estimate prognosis and guide treatment. However, advancements in DNA sequencing techniques and understanding of clonal dynamics have allowed the identification of molecular markers that were not accounted for in older models. The Molecular International Prognostic Scoring System integrates clinical, cytogenetic, and molecular data to create a more accurate prognostic tool.
Risk stratification plays an essential role in treatment planning in myelodysplastic syndromes. For decades, the International Prognostic Scoring System and its revised version have provided unified consensus for clinical trial enrollment and design. These models relied on laboratory and cytogenetic data to estimate prognosis and dictate treatment paradigms. Critical developments in DNA sequencing techniques in recent years, as well as our growing understanding of the clonal dynamics of myelodysplastic syndromes and the role that specific mutations have in shaping disease-specific phenotypes and treatment susceptibilities, have made it possible to identify molecular markers that carry critical diagnostic and therapeutic relevance and remained unaccounted for in the older models. The Molecular International Prognostic Scoring System is a novel risk stratification model that integrates clinical, cytogenetic, and molecular data to devise a more refined prognostic tool that builds on the accuracy of the traditional models.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.2
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据