ArticleCancers2025
Deep-Learning-Based Prediction of t(11;14) in Multiple Myeloma H&E-Stained Samples.
Article in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Morphology-based cytogenetic risk prediction in multiple myeloma from bone marrow smears.Frontiers in oncology · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTranslocation of chromosomes 11 and 14 [t(11;14)(q13;32)] is the most common primary translocation in multiple myeloma (MM). Patients harboring t(11;14) exhibit high response rates to BCL-2 inhibitors, underscoring the importance of rapid detection to guide treatment decisions. While fluorescence in situ hybridization (FISH) remains the gold standard for detecting this abnormality, its application is limited by challenges related to speed, accessibility, and cost. OBJECTIVES AND
methodsThis study evaluated a deep-learning-based method for detecting t(11;14) using scans of H&E-stained bone marrow biopsies from 268 untreated MM patients (147 males and 121 females).
resultsAmong these patients, 47 (17.5%) were diagnosed with smoldering MM, while 218 (81.4%) had active MM, including 22 (8.2%) that presented with concomitant amyloidosis. FISH analysis detected cytogenetic abnormalities in 191 cases (71%), with t(11;14) identified in 73 cases (27%) and a median of 26% positive cells in t(11;14)-positive cases. The AI algorithm achieved 88% sensitivity, 83.1% specificity, 84.3% accuracy, and an area under the receiver operating characteristic curve (AUROC) of 0.85 in conclusive results. The algorithm's performance was positively influenced by a higher percentage of plasma cells in the bone marrow (
conclusionsThese findings suggest that this AI approach could facilitate rapid screening for FISH analysis, although further enhancements are necessary for its clinical application in MM management.
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