Deep-UV Microscopy for Real-Time Adequacy Analysis of Bone Marrow Aspirates

项目摘要/摘要 骨髓抽吸物对于诊断、分期和监测血液学状况和 癌症(如白血病、再生障碍性贫血、镰状细胞病和实体瘤转移)，但8%-50% 由于操作员技术、血液稀释或潜在的病理原因，心跳不成功。因为这件事 流程是手动的且容易出错，因此有机会通过提供实时 以及对样品质量的自动反馈。Cellia Science将提高产品的质量和可靠性 通过开发一种护理点式筛查仪进行骨髓抽吸程序。我们的方法是 基于最近开发的用于细胞成像和分析的无标记深紫外光(UV)技术。 初步数据表明，骨髓抽提物中存在的针状体很容易通过它们的 未染色的假彩色UV图像中的特征深蓝色调--这是由于在 255 nm，骨针。产生的深紫外光图像可以在3分钟内生成，使 技术适合于在抽吸过程中实时使用，并且几乎与Giemsa- 染色的幻灯片，需要超过45分钟的处理时间。无标签深紫外光成像可与机器结合 特征提取和分类的学习技术，这将使自动质量评估成为可能 在没有病理技师的情况下抽吸涂片。我们将利用这项技术开发一种骨髓 抽吸过程中使用的抽吸质量筛选装置。为了实现这一目标，我们提出了量化 深紫外光显微镜检测毛刺的灵敏度和特异度及其一致性评价 吸气评估，由训练有素的技术人员进行目测评估，Giemsa染色的样本用作 最基本的事实。我们还将开发用于自动针灸充分性评估的原型仪器，以 无需经过专门培训的病理技术人员即可采用该技术。使充分性自动化 评估中，我们将使用机器学习技术进行特征提取和分类，检测 样品中有一个或多个针状物。这一设备的成功实施预计将 增加成功手术的比例，这将极大地提高对患者的护理质量。

Project Summary/Abstract Bone marrow aspirates are critical to the diagnosis, staging, and monitoring of hematologic conditions and cancers (e.g., leukemia, aplastic anemia, sickle cell disease, and metastasis of solid tumors), but 8-50% of aspirations are unsuccessful due to operator technique, hemodilution, or underlying pathology. Because this process is manual and error-prone, there is an opportunity to improve patient outcomes by providing real-time and automated feedback on the sample quality. Cellia Science will enable improved quality and reliability of bone marrow aspiration procedures by developing a point-of-care screening instrument. Our approach is based on a recently developed label-free, deep-ultraviolet (UV) technique for cell imaging and analysis. Preliminary data has shown that the spicules present in a bone marrow aspirate are easily identifiable by their characteristic deep blue hue in the unstained pseudocolorized UV image—a result of strong light attenuation at 255nm by bone spicules. The resulting deep-UV images can be generated in under 3 minutes, making the technique suitable for real-time use during aspiration procedures, and are nearly identical to the Giemsa- stained slides, which take over 45 min to process. Label-free deep-UV imaging can be combined with machine learning techniques for feature extraction and classification, which will enable automated quality assessment of aspirate smears without a pathology technician. We will leverage this technology to develop a bone marrow aspirate quality screening device for use during aspiration procedures. Towards this goal, we propose quantify sensitivity and specificity of spicule detection by deep-UV microscopy and evaluate concordance of deep-UV aspirate assessment with visual assessment by trained technician, with Giemsa-stained samples serving as the ground truth. We will also develop prototype instrument for automated spicule adequacy assessment to enable adoption of this technique without a specially trained pathology technician. To automate the adequacy assessment, we will use machine learning techniques for feature extraction and classification, detect the presence of one or more spicules in the sample. Successful implementation of this device is expected to increase the fraction of successful procedures, which will drastically improve the quality of care for the patient.