发表机构
Univ. Bordeaux; CNRS; Inria; Bordeaux INP; CHU Bordeaux; INSERM（波尔多大学; 法国国家科学研究中心; 法国国家信息与自动化研究所; 波尔多国立理工学院; 波尔多大学附属医院; 法国国家健康与医学研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于决策树的可解释多实例学习模型，利用常规流式细胞术数据在数小时内预测AML的NPM1和FLT3-ITD突变，在独立队列上达到高AUROC，并恢复已知免疫表型特征，支持早期治疗决策。
AI 中文摘要
背景：针对NPM1和FLT3-ITD突变的分子检测指导急性髓系白血病（AML）中关键的早期治疗决策，但结果可能需要数周时间，远迟于这些决策必须做出的时间点。流式细胞术在入院后数小时内作为常规护理的一部分已经完成，可能携带足够的信号来直接预测这些突变，而无需额外成本或延迟。方法：我们开发了一种基于决策树的可解释多实例学习分类器，其中每个患者样本被建模为单个细胞的集合，突变状态从细胞水平预测中推断。该模型与基于临床变量训练的随机森林以及为多管流式细胞术数据改编的深度卷积神经网络进行了基准比较。性能通过在197名患者的发现队列上的交叉验证进行评估，并在161名患者的独立队列上进行测试，使用受试者工作特征曲线下面积（AUROC）和阳性预测值。结果：在发现队列的交叉验证中，MIL模型对NPM1和FLT3-ITD的平均AUROC分别为0.96（标准差=0.05）和0.86（标准差=0.10），优于临床基线并与深度学习方法相当。该模型随后成功泛化到161名患者的独立测试队列，达到NPM1的AUROC为0.90，FLT3-ITD的AUROC为0.82，阳性预测值分别为0.87和0.68。细胞水平解释恢复了已确立的免疫表型特征（NPM1突变病例的CD33阳性/CD34阴性，FLT3-ITD的CD33阳性/低侧向散射），将模型预测与已知生物学直接联系起来。结论：这些结果表明，应用于常规护理中已收集数据的可解释模型可以在数小时内预测AML分子状态，为更早、基于生物学信息的治疗决策提供了一条实用途径。
英文摘要
Background: Molecular testing for NPM1 and FLT3-ITD mutations guides critical early treatment decisions in acute myeloid leukemia (AML), but results can take weeks, long after these decisions must be made. Flow cytometry, already performed within hours of admission as part of routine care, may carry enough signal to predict these mutations directly, without added cost or delay. Methods: We developed an interpretable multi-instance learning classifier based on a decision tree, in which each patient sample is modeled as a collection of individual cells and mutation status is inferred from cell-level predictions. The model was benchmarked against a random forest trained on clinical variables and a deep convolutional neural network adapted for multitube flow cytometry data. Performance was assessed by cross-validation on a discovery cohort of 197 patients and tested on an independent cohort of 161 patients, using the area under the receiver operating characteristic curve (AUROC) and positive predictive value. Results: In cross-validation on the discovery cohort, the MIL model achieved mean AUROCs of 0.96 (SD=0.05) for NPM1 and 0.86 (SD=0.10) for FLT3-ITD, outperforming the clinical baseline and matching deep learning approaches. The model then successfully generalized to the independent test cohort of 161 patients, reaching AUROCs of 0.90 (NPM1) and 0.82 (FLT3-ITD), with positive predictive values of 0.87 and 0.68, respectively. Cell-level interpretation recovered established immunophenotypic signatures (CD33${}^{+}$ /CD34___ for NPM1-mutated cases, CD33${}^{+}$ /low side-scatter for FLT3-ITD), directly linking model predictions to known biology. Conclusions: These results show that an interpretable model applied to data already collected in routine care can predict AML molecular status within hours, offering a practical route to earlier, biology-informed treatment decisions.
