Machine-learning-based classification of diffuse large B-cell lymphoma patients by a 7-mRNA signature enriched with immune infiltration and cell cycle

Zhuang, Xujie; Liu, Bo; Long, Junqi; Wang, Huina; Yu, Jiangyong; Ji, Xinchan; Li, Jinmeng; Zhu, Nian; Li, Lujia; Chen, Yuhaoran; Liu, Zhidong; Zhao, Shuangtao

doi:10.1007/s12094-023-03326-y

Cited by 1 publication

(1 citation statement)

References 31 publications

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“…Using ML, DLBCL subtypes are differentiated based on mRNA features abundant in immune infiltration and cell cycle. 31 Gene expression profile can divide DLBCL into germinal center B-cell-like, active B-cell-like, and unclassified subtypes. Different subtypes have different prognoses.…”

Section: Applications Of ML In Lymphoma Diagnosismentioning

confidence: 99%

Application of machine learning in the management of lymphoma: Current practice and future prospects

Yuan,

Zhang,

Wang

2024

DIGITAL HEALTH

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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.

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Section: Applications Of ML In Lymphoma Diagnosismentioning

confidence: 99%