定义与骨髓肿瘤 (MN) 相关的染色体和分子变化可以通过改善诊断、预后、治疗计划和患者监测来优化临床护理。这篇综述将简明地描述当今临床上用于分析 MN 的技术，并描述挑战和可能很快成为护理标准的新兴方法。在这篇综述中，作者讨论了使用非测序技术（包括传统技术）对 MN 进行分子评估细胞遗传学分析、荧光原位杂交、染色体基因组微阵列检测；以及基于 DNA 或 RNA 的下一代测序 (NGS) 检测；以及通过数字 PCR 或可测量残留病检测进行连续监测。作者解释了为什么区分体细胞和种系等位基因对于最佳管理至关重要。最后，他们介绍了新兴技术，例如长读长、全外显子组/基因组和单细胞测序，这些技术目前仅用于研究目的，但很快将成为临床测试。作者描述了采用全面基因组测试的挑战那些在资源有限的环境中并纳入临床试验的项目。未来，在电信快速发展的推动下，患者护理的各个方面都可能受到人工智能和数学模型的影响。

Defining the chromosomal and molecular changes associated with myeloid neoplasms (MNs) optimizes clinical care through improved diagnosis, prognosis, treatment planning, and patient monitoring. This review will concisely describe the techniques used to profile MNs clinically today, with descriptions of challenges and emerging approaches that may soon become standard-of-care.In this review, the authors discuss molecular assessment of MNs using non-sequencing techniques, including conventional cytogenetic analysis, fluorescence in situ hybridization, chromosomal genomic microarray testing; as well as DNA- or RNA-based next-generation sequencing (NGS) assays; and sequential monitoring via digital PCR or measurable residual disease assays. The authors explain why distinguishing somatic from germline alleles is critical for optimal management. Finally, they introduce emerging technologies, such as long-read, whole exome/genome, and single-cell sequencing, which are reserved for research purposes currently but will become clinical tests soon.The authors describe challenges to the adoption of comprehensive genomic tests for those in resource-constrained environments and for inclusion into clinical trials. In the future, all aspects of patient care will likely be influenced by the adaptation of artificial intelligence and mathematical modeling, fueled by rapid advances in telecommunications.