Yuan Junyun, Zhang Ya, Wang Xin
Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Department of Hematology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China.
Digit Health. 2024 Apr 16;10:20552076241247963. doi: 10.1177/20552076241247963. eCollection 2024 Jan-Dec.
In the past decade, digitization of medical records and multiomics data analysis in lymphoma has led to the accessibility of high-dimensional records. The digitization of medical records, the visualization of extensive volume data extracted from medical images, and the integration of multiomics methods into clinical decision-making have produced many datasets. As a promising auxiliary tool, machine learning (ML) intends to extract homologous features in large-scale data sets and encode them into various patterns to complete complicated tasks. At present, artificial intelligence and digital mining have shown promising prospects in the field of lymphoma pathological image analysis. The paradigm shift from qualitative analysis to quantitative analysis makes the pathological diagnosis more intelligent and the results more accurate and objective. ML can promote accurate lymphoma diagnosis and provide patients with prognostic information and more individualized treatment options. Based on the above, this comprehensive review of the general workflow of ML highlights recent advances in ML techniques in the diagnosis, treatment, and prognosis of lymphoma, and clarifies the boundedness and future orientation of the ML technique in the clinical practice of lymphoma.
在过去十年中，淋巴瘤病历的数字化和多组学数据分析使得高维记录变得可获取。病历的数字化、从医学图像中提取的大量数据的可视化以及多组学方法在临床决策中的整合产生了许多数据集。作为一种有前景的辅助工具，机器学习（ML）旨在在大规模数据集中提取同源特征并将其编码为各种模式以完成复杂任务。目前，人工智能和数字挖掘在淋巴瘤病理图像分析领域已展现出有前景的前景。从定性分析到定量分析的范式转变使病理诊断更智能化，结果更准确、客观。ML可促进淋巴瘤的准确诊断，并为患者提供预后信息和更个性化的治疗选择。基于上述内容，这篇对ML一般工作流程的全面综述突出了ML技术在淋巴瘤诊断、治疗和预后方面的最新进展，并阐明了ML技术在淋巴瘤临床实践中的局限性和未来方向。
