Integrating holotomography and deep learning for rapid detection of NPM1 mutations in AML.

ABSTRACT: Rapid and accurate diagnosis of acute myeloid leukemia (AML) remains a significant challenge, particularly in the context of myelodysplastic syndrome (MDS) or MDS/myeloproliferative neoplasm with NPM1 mutations. This study introduces an innovative approach using holotomography (HT), a 3D label-free quantitative phase imaging technique, to detect NPM1 mutations. We analyzed a dataset of 2073 HT myeloblast images from 48 individuals, including both NPM1 wild-type and mutated samples, to distinguish subcellular morphological changes associated with NPM1 mutations. Employing a convolutional neural network, we analyzed 3D cell morphology, focusing on refractive index distributions. The machine learning model showed high accuracy, with an area under the receiver operating characteristic curve of

SUBMITTER: Kim H 

PROVIDER: S-EPMC11467337 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature