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

doi:10.1117/12.3004388

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

Author Affiliations +

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.

Conference Presentation

Citation Download Citation

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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