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5025 Deep Learning for Circulating Multiple Myeloma Cell (CMMC) Enumeration with the Cellsearch System

Program: Oral and Poster Abstracts
Session: 803. Emerging Tools, Techniques and Artificial Intelligence in Hematology: Poster III
Hematology Disease Topics & Pathways:
artificial intelligence (AI), assays, emerging technologies, Technology and Procedures, imaging, machine learning
Monday, December 11, 2023, 6:00 PM-8:00 PM

Luca Biasiolli, DPhil1*, Arianna Campione2*, Pietro Ansaloni1*, Nicolò Gentili1*, Ramona Miserendino, PhD1*, Giulio Signorini1* and Gianni Medoro, PhD1*

1Menarini Silicon Biosystems, Bologna, Italy
2Polytechnic University of Bari, Bari, Italy


Circulating Multiple Myeloma Cell (CMMC) enumeration in blood samples with the CELLSEARCH® system is a minimally invasive assay used to quantify plasma cells in patients with Multiple Myeloma (MM) and in precursor stages MGUS and SMM1 particularly during disease stages when a bone marrow aspirate is not desirable (Foulk et al. 2017). Currently, CMMC identification is performed by human reviewers through visual assessment, which is a time-consuming procedure that could be affected by subjective interpretations. Modern Deep Learning (DL) methods can provide a solution by automating the process of CMMC identification, thus eliminating subjectivity and producing faster and more reproducible results. The purpose of this study was to train and test an automated algorithm (for research use only) based on DL models for CMMC identification in CELLSEARCH® fluorescent images.


The DL algorithm was designed to analyze images using a segmentation and a classification network as presented in recent studies (Zeune et al. Nature MI 2020; Coumans et al. ACTC 2021). The DL classification model was trained on 5139 CMMC and 16235 non-CMMC images (training set) of fully anonymized blood samples from the CoMMpass study (NCT01454297 sponsored by the Multiple Myeloma Research Foundation) scanned by CELLTRACKS ANALYZER II® (CTAII). The test set was composed of 75 samples from the CoMMpass study with 1000 images per sample reviewed by an operator (with training and experience of reviewing CTAII images) and processed by the algorithm, for a total of 75000 images. The algorithm was compared with the human reviewer in terms of overall agreement and Cohen’s Kappa of the image classification results and of correlation for the CMMC enumeration results per sample.


In the test dataset, out of 75000 images in total the reviewer identified 32403 CMMCs and the algorithm 32842 CMMCs. The DL algorithm and the reviewer provided the same classification for 71711 images, thus showing very high agreement = 95.6% (Cohen’s Kappa = 0.91). CMMC counts per sample and linear regression are shown in the figure: slope = 0.95 (95% CI = 0.02), intercept = 26.99 (95% CI = 11.56) and coefficient of determination R2 = 0.99.


These preliminary results show the potential of applying automated algorithms to CMMC identification with the CELLSEARCH® system in order to remove human subjectivity from the review process, thus maximizing standardization among different research centers.

1) The CMMC assay is run in a CLIA/CAP certified laboratory in the US and is also New York State CLEP approved. In the EU, the CMMC assay is research use only (RUO).

Disclosures: Biasiolli: Menarini Silicon Biosystems: Current Employment. Ansaloni: Menarini Silicon Biosystems: Current Employment. Gentili: Menarini Silicon Biosystems: Current Employment. Miserendino: Menarini Silicon Biosystems: Current Employment. Signorini: Menarini Silicon Biosystems: Current Employment. Medoro: Menarini Silicon Biosystems: Current Employment.

*signifies non-member of ASH