Identification and Staging of B-Cell Acute Lymphoblastic Leukemia Using Quantitative Phase Imaging and Machine Learning.
Vinay AyyappanAlex ChangChi ZhangSantosh Kumar PaidiRosalie BordettTiffany LiangIshan BarmanRishikesh PandeyPublished in: ACS sensors (2020)
Identification and classification of leukemia cells in a rapid and label-free fashion is clinically challenging and thus presents a prime arena for implementing new diagnostic tools. Quantitative phase imaging, which maps optical path length delays introduced by the specimen, has been demonstrated to discern cellular phenotypes based on differential morphological attributes. Rapid acquisition capability and the availability of label-free images with high information content have enabled researchers to use machine learning (ML) to reveal latent features. We developed a set of ML classifiers, including convolutional neural networks, to discern healthy B cells from lymphoblasts and classify stages of B cell acute lymphoblastic leukemia. Here, we show that the average dry mass and volume of normal B cells are lower than those of cancerous cells and that these morphologic parameters increase further alongside disease progression. We find that the relaxed training requirements of a ML approach are conducive to the classification of cell type, with minimal space, training time, and memory requirements. Our findings pave the way for a larger study on clinical samples of acute lymphoblastic leukemia, with the overarching goal of its broader use in hematopathology, where the prospect of objective diagnoses with minimal sample preparation remains highly desirable.
Keyphrases
- acute lymphoblastic leukemia
- label free
- machine learning
- deep learning
- high resolution
- convolutional neural network
- induced apoptosis
- allogeneic hematopoietic stem cell transplantation
- artificial intelligence
- cell cycle arrest
- acute myeloid leukemia
- oxidative stress
- working memory
- loop mediated isothermal amplification
- virtual reality
- endoplasmic reticulum stress
- healthcare
- pet ct
- genome wide
- photodynamic therapy
- dna methylation
- optical coherence tomography
- high speed
- gene expression
- health information
- molecularly imprinted
- fluorescence imaging
- current status
- tandem mass spectrometry
- sensitive detection
- liquid chromatography