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

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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.