Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well-recognized prognostic biomarkers at diagnosis, the most powerful independent prognostic factor is the response of the leukemia to induction chemotherapy (Campana and Pui: Blood 129 (2017) 1913-1918). Given the potential for machine learning to improve precision medicine, we tested its capacity to monitor disease in children undergoing ALL treatment. Diagnostic and on-treatment bone marrow samples were labeled with an ALL-discriminating antibody combination and analyzed by imaging flow cytometry. Ignoring the fluorescent markers and using only features extracted from bright-field and dark-field cell images, a deep learning model was able to identify ALL cells at an accuracy of >88%. This antibody-free, single cell method is cheap, quick, and could be adapted to a simple, laser-free cytometer to allow automated, point-of-care testing to detect slow early responders. Adaptation to other types of leukemia is feasible, which would revolutionize residual disease monitoring. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

中文翻译：

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通过计算机视觉进行无标记白血病监测。

急性淋巴细胞白血病 (ALL) 是最常见的儿童癌症。虽然在诊断时有许多公认的预后生物标志物，但最强大的独立预后因素是白血病对诱导化疗的反应（Campana 和 Pui：Blood 129 (2017) 1913-1918）。鉴于机器学习在改善精准医学方面的潜力，我们测试了它在接受 ALL 治疗的儿童中监测疾病的能力。诊断和治疗中的骨髓样品用 ALL 鉴别抗体组合进行标记，并通过成像流式细胞术进行分析。忽略荧光标记并仅使用从明场和暗场细胞图像中提取的特征，深度学习模型能够以 > 88% 的准确度识别所有细胞。这种无抗体的单细胞方法便宜、快速、并且可以适用于简单的无激光细胞仪，以允许自动化的床旁检测来检测早期反应缓慢的患者。适应其他类型的白血病是可行的，这将彻底改变残留疾病的监测。© 2020 作者。Cytometry Part A 由 Wiley Periodicals, Inc. 代表 International Society for Advancement of Cytometry 出版。