The evaluation of bone marrow morphology by experienced hematopathologists is essential in the diagnosis of acute myeloid leukemia (AML); however, it suffers from a lack of standardization and inter-observer variability. Deep learning (DL) can process medical image data and provides data-driven class predictions. Here, we apply a multi-step DL approach to automatically segment cells from bone marrow images, distinguish between AML samples and healthy controls with an area under the receiver operating characteristic (AUROC) of 0.9699, and predict the mutation status of Nucleophosmin 1 (NPM1)—one of the most common mutations in AML—with an AUROC of 0.92 using only image data from bone marrow smears. Utilizing occlusion sensitivity maps, we observed so far unreported morphologic cell features such as a pattern of condensed chromatin and perinuclear lightening zones in myeloblasts of NPM1-mutated AML and prominent nucleoli in wild-type NPM1 AML enabling the DL model to provide accurate class predictions.

由经验丰富的血液病理学家评估骨髓形态对于急性髓性白血病 (AML) 的诊断至关重要；然而，它缺乏标准化和观察者间的可变性。深度学习 (DL) 可以处理医学图像数据并提供数据驱动的类别预测。在这里，我们应用多步 DL 方法从骨髓图像中自动分割细胞，区分 AML 样本和健康对照，接受者操作特征 (AUROC) 为 0.9699 的区域，并预测核磷蛋白 1 ( NPM1 ) 的突变状态) — AML 中最常见的突变之一 — AUROC 为 0.92，仅使用来自骨髓涂片的图像数据。利用遮挡敏感性图，我们观察到迄今为止未报告的形态细胞特征，例如 NPM1 突变 AML 的成髓细胞中的浓缩染色质和核周亮区模式以及野生型NPM1 AML 中突出的核仁，使 DL 模型能够提供准确的类别预测。