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Related Experiment Video

Updated: Jun 16, 2025

Interphase Fluorescence in situ Hybridization of Bone Marrow Smears of Multiple Myeloma
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Deep-Learning-Based Prediction of t(11;14) in Multiple Myeloma H&E-Stained Samples.

Nadav Kerner1, Dov Hershkovitz1,2, Svetlana Trestman1,3

  • 1Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel.

Cancers
|June 13, 2025
PubMed
Summary

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Talquetamab-Daratumumab in Relapsed or Refractory Myeloma.

The New England journal of medicine·2026
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Daratumumab in Transplant-Ineligible or -Deferred Newly Diagnosed Multiple Myeloma: Minimal Residual Disease in CEPHEUS.

Blood advances·2026
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Histologic Transformation in Follicular Lymphoma: Real-World Outcomes with Rituximab vs. Obinutuzumab-Based Combinations.

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The Clinical Characteristics and Outcomes of Multiple Myeloma Patients With Oligo or Non-Secretory Relapse-A Retrospective Cohort Study.

Hematological oncology·2026

An artificial intelligence (AI) algorithm shows promise for rapidly detecting the chromosome 11;14 translocation in multiple myeloma (MM) using standard bone marrow biopsy scans. This AI tool could aid in faster treatment decisions for patients with this common genetic abnormality.

Area of Science:

  • Hematology
  • Oncology
  • Computational Biology

Background:

  • The chromosome 11;14 translocation [t(11;14)] is the most frequent primary translocation in multiple myeloma (MM).
  • Patients with t(11;14) respond well to BCL-2 inhibitors, making its rapid detection crucial for guiding treatment.
  • Current gold-standard fluorescence in situ hybridization (FISH) methods face limitations in speed, accessibility, and cost.

Purpose of the Study:

  • To evaluate a deep-learning-based artificial intelligence (AI) method for detecting t(11;14) in multiple myeloma.
  • To assess the AI algorithm's performance using H&E-stained bone marrow biopsy scans.

Main Methods:

  • The study analyzed H&E-stained bone marrow biopsy scans from 268 untreated multiple myeloma patients.
  • A deep-learning algorithm was developed and tested for its ability to detect the t(11;14) translocation.
Keywords:
14)bone marrow biopsydeep learningdetectionmultiple myelomat(11

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  • Performance metrics including sensitivity, specificity, accuracy, and AUROC were calculated.

    • Main Results:

    • The AI algorithm achieved 88% sensitivity, 83.1% specificity, and 84.3% accuracy, with an AUROC of 0.85.
    • The algorithm's performance was significantly associated with a higher percentage of plasma cells, active MM, lytic lesions, and lower hemoglobin levels.
    • FISH analysis identified t(11;14) in 27% of cases.

    Conclusions:

    • The AI approach demonstrates potential for rapid screening of t(11;14) in multiple myeloma.
    • Further development is needed to optimize the AI method for clinical integration in MM management.
    • This AI tool could complement FISH analysis for faster patient stratification.