Identification and Staging of B-Cell Acute Lymphoblastic Leukemia Using Quantitative Phase Imaging and Machine Learning
Identification and Staging of B-Cell Acute Lymphoblastic Leukemia Using Quantitative Phase Imaging and Machine Learning
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DOI：
10.1021/acssensors.0c01811
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发表时间：
2020-10-23
期刊：
影响因子：
8.9
通讯作者：
Pandey, Rishikesh
中科院分区：
文献类型：
--
作者：
Ayyappan, Vinay;Chang, Alex;Pandey, Rishikesh
以快速和无标记的方式鉴定和分类白血病细胞在临床上具有挑战性，因此为实施新的诊断工具提供了一个主要的舞台。定量相位成像，它映射了由标本引入的光路长度延迟，已经被证明基于不同的形态属性来识别细胞表型。快速获取能力和高信息量的无标签图像的可用性使研究人员能够使用机器学习(ML)来揭示潜在特征。我们开发了一套ML分类器，包括卷积神经网络，用于区分健康的B细胞和淋巴母细胞，并对B细胞急性淋巴细胞性白血病进行分期。在这里，我们发现正常B细胞的平均干重和体积低于癌细胞，并且这些形态参数随着疾病的进展而进一步增加。我们发现，ML方法放宽的训练要求有利于细胞类型的分类，而空间、训练时间和内存要求都最小。我们的发现为对急性淋巴细胞白血病的临床样本进行更大规模的研究铺平了道路，总体目标是将其更广泛地用于血液病理学，在血液病理学中，以最少的样本准备进行客观诊断的前景仍然是非常可取的。
Identification and classification of leukemia cells in a rapid and label-free fashion is clinically challenging and thus presents a prime arena for implementing new diagnostic tools. Quantitative phase imaging, which maps optical path length delays introduced by the specimen, has been demonstrated to discern cellular phenotypes based on differential morphological attributes. Rapid acquisition capability and the availability of label-free images with high information content have enabled researchers to use machine learning (ML) to reveal latent features. We developed a set of ML classifiers, including convolutional neural networks, to discern healthy B cells from lymphoblasts and classify stages of B cell acute lymphoblastic leukemia. Here, we show that the average dry mass and volume of normal B cells are lower than those of cancerous cells and that these morphologic parameters increase further alongside disease progression. We find that the relaxed training requirements of a ML approach are conducive to the classification of cell type, with minimal space, training time, and memory requirements. Our findings pave the way for a larger study on clinical samples of acute lymphoblastic leukemia, with the overarching goal of its broader use in hematopathology, where the prospect of objective diagnoses with minimal sample preparation remains highly desirable.