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13 March 2024 Label-free cytological characterization of blasts with NPM1 mutation using holotomography and machine learning
Hyunji Kim, Geon Kim, Mahn Jae Lee, Seongsoo Jang, YongKeun Park
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Proceedings Volume PC12852, Quantitative Phase Imaging X; PC128520L (2024) https://doi.org/10.1117/12.3004388
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
The Nucleophosmin 1 (NPM1) mutation rapidly progresses to acute myeloid leukemia, emphasizing the need for early diagnosis, especially in cases with low blast counts. Some low blast count instances may not undergo next-generation sequencing, causing delays and necessitating swift NPM1 mutation screening. This study utilizes cutting-edge label-free three-dimensional imaging with holotomography (HT) to identify NPM1 mutation in blasts. Machine learning and deep learning algorithms achieve precise single-cell and patient-specific predictions. HT's accurate detection of protein movement associated with NPM1 mutation holds great promise as a reliable and efficient tool for early detection in hematologic malignancy patients with low blast counts.
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Hyunji Kim, Geon Kim, Mahn Jae Lee, Seongsoo Jang, and YongKeun Park "Label-free cytological characterization of blasts with NPM1 mutation using holotomography and machine learning", Proc. SPIE PC12852, Quantitative Phase Imaging X, PC128520L (13 March 2024); https://doi.org/10.1117/12.3004388
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KEYWORDS
Machine learning
Data modeling
Deep learning
Image segmentation
Leukemia
Microscopy
Proteins
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