Detection of measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL) is an important prognostic marker. The most common CLL MRD method in current use is multiparameter flow cytometry, but availability is limited by the need for expert manual analysis. Automated analysis has the potential to expand access to CLL MRD testing. We evaluated the performance of an artificial intelligence (AI)-assisted multiparameter flow cytometry (MFC) workflow for CLL MRD. We randomly selected 113 CLL MRD FCS files and divided them into training and validation sets. The training set (n = 41) was gated by expert manual analysis and used to train the AI model. We then compared the validation set (n = 72) MRD results obtained by the AI-assisted analysis versus those by expert manual analysis using the Pearson correlation coefficient and Bland–Altman plot method. In the validation set, the AI-assisted analysis correctly categorized cases as MRD-negative versus MRD-positive in 96% of cases. When comparing the AI-assisted analysis versus the expert manual analysis, the Pearson r was 0.8650, mean bias was 0.2237 log₁₀ units, and the 95% limit of agreement (LOA) was ±1.0282 log₁₀ units. The AI-assisted analysis performed sub-optimally in atypical immunophenotype CLL and in cases lacking residual normal B cells. When excluding these outlier cases, the mean bias improved to 0.0680 log₁₀ units and the 95% LOA to ±0.2926 log₁₀ units. An automated AI-assisted workflow allows for the quantification of MRD in CLL with typical immunophenotype. Further work is required to improve performance in atypical immunophenotype CLL.

中文翻译：

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使用人工智能辅助工作流程自动量化慢性淋巴细胞白血病中可测量的残留病灶

检测慢性淋巴细胞白血病 (CLL) 中的可测量残留病灶 (MRD) 是重要的预后标志物。目前使用的最常见的 CLL MRD 方法是多参数流式细胞术，但可用性受到专家手动分析需求的限制。自动化分析有可能扩大对 CLL MRD 测试的访问。我们评估了用于 CLL MRD 的人工智能 (AI) 辅助多参数流式细胞术 (MFC) 工作流程的性能。我们随机选择了 113 个 CLL MRD FCS 文件并将它们分为训练集和验证集。训练集（n  = 41）由专家手动分析门控并用于训练 AI 模型。然后我们比较了验证集 ( n = 72) 通过 AI 辅助分析获得的 MRD 结果与使用 Pearson 相关系数和 Bland-Altman 图方法的专家手动分析获得的 MRD 结果。在验证集中，AI 辅助分析在 96% 的病例中将病例正确分类为 MRD 阴性和 MRD 阳性。将 AI 辅助分析与专家手动分析进行比较时，Pearson r为 0.8650，平均偏差为 0.2237 log ₁₀单位，95% 一致限度 (LOA) 为 ±1.0282 log ₁₀单位。人工智能辅助分析在非典型免疫表型 CLL 和缺乏残留正常 B 细胞的情况下表现不佳。当排除这些异常值时，平均偏差提高到 0.0680 log ₁₀单位，95% LOA 提高到 ±0.2926 log_10个单位。自动化的 AI 辅助工作流程允许对具有典型免疫表型的 CLL 中的 MRD 进行量化。需要进一步的工作来提高非典型免疫表型 CLL 的性能。