Department of Internal Medicine I, University Hospital Carl Gustav Carus, Dresden, Germany.
Institute of Circuits and Systems, Technical University Dresden, Dresden, Germany.
Leukemia. 2022 Jan;36(1):111-118. doi: 10.1038/s41375-021-01408-w. Epub 2021 Sep 8.
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.
经验丰富的血液病理学家对骨髓形态的评估对急性髓系白血病 (AML) 的诊断至关重要；然而，它存在缺乏标准化和观察者间变异性的问题。深度学习 (DL) 可以处理医学图像数据，并提供数据驱动的分类预测。在这里，我们应用一种多步骤的 DL 方法，从骨髓图像中自动分割细胞，使用骨髓涂片的图像数据区分 AML 样本和健康对照，接收者操作特征曲线下的面积 (AUROC) 为 0.9699，并预测核仁磷酸蛋白 1 (NPM1) - AML 中最常见的突变之一 - 的突变状态，AUROC 为 0.92。利用遮挡敏感图，我们观察到迄今为止在 NPM1 突变型 AML 的原始细胞中未报告的形态学细胞特征，如浓缩染色质模式和核周光带，以及在野生型 NPM1 AML 中明显的核仁，这使得 DL 模型能够提供准确的分类预测。
