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

用于骨髓抽吸物实时充分性分析的深紫外显微镜

基本信息

  • 批准号:
    10761397
  • 负责人:
  • 金额:
    $ 27.58万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-08-01 至 2024-07-31
  • 项目状态:
    已结题

项目摘要

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.
项目总结/摘要 骨髓穿刺对于血液学状况的诊断、分期和监测至关重要, 癌症(例如,白血病、再生障碍性贫血、镰状细胞病和实体瘤转移),但8-50%的 由于操作者技术、血液稀释或潜在病理学,抽吸不成功。因为这 过程是手动的,容易出错,有机会通过提供实时的 以及对样品质量的自动反馈。Cellia Science将提高 通过开发一种即时筛查仪器,我们的做法是 基于最近开发的用于细胞成像和分析的无标记、深紫外(UV)技术。 初步数据表明,骨髓穿刺液中存在的骨针很容易通过其 在未染色的假彩色UV图像中的特征深蓝色调- 骨针255 nm。由此产生的深紫外图像可以在3分钟内生成, 适用于抽吸过程中实时使用的技术,与Giemsa几乎相同, 染色的载玻片,需要45分钟以上的处理时间。无标签深紫外成像可与机器结合 特征提取和分类的学习技术,这将使自动质量评估, 在没有病理学技术人员的情况下抽吸涂片。我们将利用这项技术开发一种骨髓 在抽吸过程中使用的抽吸质量筛选装置。为了实现这一目标,我们建议量化 深紫外显微镜检测骨针敏感性和特异性,并评价深紫外显微镜的一致性 由经过培训的技术人员通过目视评估进行抽吸评估,Giemsa染色样本作为 地面真相我们亦会发展自动化骨针充足性评估的原型仪器, 使得能够在没有经过专门训练的病理学技术人员的情况下采用该技术。自动化的充分性 评估,我们将使用机器学习技术进行特征提取和分类,检测 样品中存在一个或多个针状体。该设备的成功实施预计将 增加成功手术的比例,这将大大提高患者的护理质量。

项目成果

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Francisco E Robles其他文献

Francisco E Robles的其他文献

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{{ truncateString('Francisco E Robles', 18)}}的其他基金

Accessible label-free optical microscopy with quantitative molecular and functional contrast
易于使用的无标记光学显微镜,具有定量分子和功能对比
  • 批准号:
    10501498
  • 财政年份:
    2022
  • 资助金额:
    $ 27.58万
  • 项目类别:
Accessible label-free optical microscopy with quantitative molecular and functional contrast
易于使用的无标记光学显微镜,具有定量分子和功能对比
  • 批准号:
    10707486
  • 财政年份:
    2022
  • 资助金额:
    $ 27.58万
  • 项目类别:
Stimulated Raman scattering spectroscopic optical coherence tomography (SRS-SOCT) for label-free molecular imaging of brain tumor pathology
受激拉曼散射光谱光学相干断层扫描 (SRS-SOCT) 用于脑肿瘤病理学无标记分子成像
  • 批准号:
    9443282
  • 财政年份:
    2018
  • 资助金额:
    $ 27.58万
  • 项目类别:
Multi-modality optical molecular imaging for melanoma tumor margin assessment
用于黑色素瘤肿瘤边缘评估的多模态光学分子成像
  • 批准号:
    8649948
  • 财政年份:
    2014
  • 资助金额:
    $ 27.58万
  • 项目类别:
Multi-modality optical molecular imaging for melanoma tumor margin assessment
用于黑色素瘤肿瘤边缘评估的多模态光学分子成像
  • 批准号:
    8874744
  • 财政年份:
    2014
  • 资助金额:
    $ 27.58万
  • 项目类别:

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