Scholarlink

<p>Flow cytometry (FC) is essential for detecting measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL), but its use is limited by the expertise and time required for manual analysis. We developed an artificial intelligence (AI) pipeline, Clustering/Classification of All Events, Dimensionality reduction, Downsampling, and Aberrancy Scaling (CCADDAS), to automatically enhance raw FC files, streamlining CLL MRD detection using a single-tube 10-color panel. FC files from 166 MRD-positive and 61 MRD-negative cases were processed in a cloud environment. Automated steps included error correction (FlowCut), clustering (PARC), dimensionality reduction (UMAP), anomaly detection against negative controls, and cluster-informed downsampling that preserved rare MRD events. A deep neural network trained on expert-defined normal subsets enabled automated gating. AI-enhanced files were analyzed in standard FC software, yielding results highly concordant with conventional expert review (R² = 0.98). Downsampling reduced cellularity by 85% and file size by 78%, while retaining low-level MRD events. An AI-generated aberrancy scale distinguished CLL MRD from background B cells with excellent performance (AUC = 0.98). Manual analysis time decreased from 9.0 to 0.9 min per case (90% reduction). CCADDAS provides a largely unsupervised, software-agnostic method that accelerates and simplifies CLL MRD detection without compromising test performance compared to conventional analysis, enabling broader adoption of FC-based MRD testing.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial intelligence-enhancement of flow cytometry data accelerates the identification of measurable residual chronic lymphocytic leukemia

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

查看全文
  • April Chiu,
  • Jansen N. Seheult,
  • Min Shi,
  • Dragan Jevremovic,
  • Clarissa E. Jordan,
  • Mathew J. Weybright,
  • Michael M. Timm,
  • Gregory E. Otteson,
  • Horatiu Olteanu,
  • Sameer A. Parikh,
  • Pedro Horna

摘要

Flow cytometry (FC) is essential for detecting measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL), but its use is limited by the expertise and time required for manual analysis. We developed an artificial intelligence (AI) pipeline, Clustering/Classification of All Events, Dimensionality reduction, Downsampling, and Aberrancy Scaling (CCADDAS), to automatically enhance raw FC files, streamlining CLL MRD detection using a single-tube 10-color panel. FC files from 166 MRD-positive and 61 MRD-negative cases were processed in a cloud environment. Automated steps included error correction (FlowCut), clustering (PARC), dimensionality reduction (UMAP), anomaly detection against negative controls, and cluster-informed downsampling that preserved rare MRD events. A deep neural network trained on expert-defined normal subsets enabled automated gating. AI-enhanced files were analyzed in standard FC software, yielding results highly concordant with conventional expert review (R² = 0.98). Downsampling reduced cellularity by 85% and file size by 78%, while retaining low-level MRD events. An AI-generated aberrancy scale distinguished CLL MRD from background B cells with excellent performance (AUC = 0.98). Manual analysis time decreased from 9.0 to 0.9 min per case (90% reduction). CCADDAS provides a largely unsupervised, software-agnostic method that accelerates and simplifies CLL MRD detection without compromising test performance compared to conventional analysis, enabling broader adoption of FC-based MRD testing.