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Updated: Dec 27, 2025

Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia

Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia

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Label-Free Leukemia Monitoring by Computer Vision.

Minh Doan¹, Marian Case², Dino Masic²

¹Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology

|February 25, 2020

Summary

This summary is machine-generated.

A new deep learning model can identify childhood acute lymphoblastic leukemia (ALL) cells using only cell images, achieving over 88% accuracy. This antibody-free method enables rapid, low-cost monitoring of treatment response in pediatric leukemia patients.

Keywords:

computer vision deep learning imaging flow cytometry label-free leukemia machine learning neural networks

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Published on: March 26, 2018

Area of Science:

Pediatric Oncology
Computational Biology
Medical Diagnostics

Background:

Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer.
Treatment response is a critical prognostic factor, but monitoring can be challenging.
Machine learning offers potential for improved precision medicine in cancer care.

Purpose of the Study:

To evaluate a deep learning model's capacity to monitor childhood ALL during treatment.
To develop an antibody-free method for identifying leukemia cells using imaging flow cytometry.
To assess the feasibility of automated, point-of-care testing for early detection of slow responders.

Main Methods:

Bone marrow samples from children with ALL were analyzed using imaging flow cytometry.
A deep learning model was trained on bright-field and dark-field cell images, ignoring fluorescent markers.
The model's accuracy in identifying ALL cells was evaluated.

Main Results:

The deep learning model achieved >88% accuracy in identifying ALL cells.
The method successfully identified leukemia cells using only image features, without fluorescent markers.
The approach is described as cheap, quick, and adaptable for point-of-care testing.

Conclusions:

An antibody-free, deep learning approach can accurately identify ALL cells from cell images.
This method has the potential for revolutionizing residual disease monitoring in leukemia.
Adaptation to other leukemia types and integration with laser-free cytometers are feasible.