{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:00:24Z","timestamp":1782316824607,"version":"3.54.5"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>\n                    Cytogenetic abnormalities determine prognosis and treatment selection in multiple myeloma (MM). However, genetic testing via fluorescence\n                    <jats:italic>in situ<\/jats:italic>\n                    hybridization remains expensive and inaccessible in many clinical settings. Prior studies have established that specific chromosomal alterations correlate with distinct plasma cell morphological features. This suggests that visual analysis of bone marrow aspirate (BMA) smears could enable genetic risk prediction. We developed a computational framework that predicts high-risk cytogenetic status directly from digitized BMA smear images. Our approach employs DinoBloom, an established hematology-specific vision transformer pretrained on bone marrow and peripheral blood cell images, to extract morphological features from individual plasma cells. These embeddings are aggregated through attention-based multiple instance learning, which requires only patient-level labels rather than cell-by-cell annotations. The attention mechanism identifies which cells drive each prediction and links morphological phenotypes to genetic subtypes. Evaluated on a multi-center MM cohort with matched genetic results, the model achieved area under the curve values ranging from 0.76 to 0.85 across multiple cytogenetic markers. Attention maps highlighted plasmablastic features in high-risk predictions and mature plasma cell characteristics in standard-risk cases, consistent with established morphology-genetics correlations. This approach offers a rapid, cost-effective screening tool that could extend genetic risk stratification to resource-limited settings.\n                  <\/jats:p>","DOI":"10.3389\/fonc.2026.1790130","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T05:37:14Z","timestamp":1773812234000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Morphology-based cytogenetic risk prediction in multiple myeloma from bone marrow smears"],"prefix":"10.3389","volume":"16","author":[{"given":"Minjie","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Hematology, The First Affiliated Hospital of Ningbo University","place":["Ningbo, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Hematology, The First Affiliated Hospital of Ningbo University","place":["Ningbo, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine","place":["Jinhua, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guifang","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Department of Hematology, The First Affiliated Hospital of Ningbo University","place":["Ningbo, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangguang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Finance and Information, Ningbo University of Finance & Economics","place":["Ningbo, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shikun","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Finance and Information, Ningbo University of Finance & Economics","place":["Ningbo, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1038\/nrclinonc.2011.63","article-title":"Treatment of multiple myeloma","volume":"8","author":"Rajkumar","year":"2011","journal-title":"Nat Rev Clin Oncol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2015.61.2267","article-title":"Revised international staging system for multiple myeloma: a report from international myeloma working group","volume":"33","author":"Palumbo","year":"2015","journal-title":"J Clin Oncol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2016-01-631200","article-title":"Treatment of multiple myeloma with high-risk cytogenetics: a consensus of the international myeloma working group","volume":"127","author":"Sonneveld","year":"2016","journal-title":"Blood J Am Soc Hematol"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(20)30525-8","article-title":"Venetoclax or placebo in combination with bortezomib and dexamethasone in patients with relapsed or refractory multiple myeloma (bellini): a randomised, double-blind, multicentre, phase 3 trial","volume":"21","author":"Kumar","year":"2020","journal-title":"Lancet Oncol"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1038\/sj.leu.2403091","article-title":"t (11;14) and t (4; 14) translocations correlated with mature lymphoplasmacytoid and immature morphology, respectively, in multiple myeloma","volume":"17","author":"Garand","year":"2003","journal-title":"Leukemia"},{"key":"B6","article-title":"Plasmablastic morphology\u2014an independent prognostic factor with clinical and laboratory correlates: Eastern cooperative oncology group (ecog) myeloma trial e9486 report by the ecog myeloma laboratory group","volume":"91","author":"Greipp","year":"1998","journal-title":"Blood J Am Soc Hematol"},{"key":"B7","doi-asserted-by":"publisher","first-page":"5789","DOI":"10.1182\/blood-2025-5789","article-title":"Quantitative morphological parameters of bone marrow plasma cells are predictive of outcomes in plasma cell dyscrasias","volume":"146","author":"Wang","year":"2025","journal-title":"Blood"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0101-9","article-title":"Human-level recognition of blast cells in acute myeloid leukaemia with convolutional neural networks","volume":"1","author":"Matek","year":"2019","journal-title":"Nat Mach Intell"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1182\/blood.2020010568","article-title":"Highly accurate differentiation of bone marrow cell morphologies using deep neural networks on a large image data set","volume":"138","author":"Matek","year":"2021","journal-title":"Blood J Am Soc Hematol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-019-0508-1","article-title":"Clinical-grade computational pathology using weakly supervised deep learning on whole slide images","volume":"25","author":"Campanella","year":"2019","journal-title":"Nat Med"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-020-00682-w","article-title":"Data-efficient and weakly supervised computational pathology on whole-slide images","volume":"5","author":"Lu","year":"2021","journal-title":"Nat Biomed Eng"},{"key":"B12","article-title":"Transmil: Transformer based correlated multiple instance learning for whole slide image classification","volume":"34","author":"Shao","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"B13","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-72390-2_49","article-title":"Dinobloom: a foundation model for generalizable cell embeddings in hematology","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Koch","year":"2024"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2023011076","article-title":"Deep learning predicts therapy-relevant genetics in acute myeloid leukemia from pappenheim-stained bone marrow smears","volume":"8","author":"Kockwelp","year":"2024","journal-title":"Blood Adv"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2025.112998","article-title":"Predicting runx1:: Runx1t1 genetic abnormalities in acute myeloid leukemia from bone marrow smears: Can artificial intelligence do better","volume":"28","author":"Cheng","year":"2025","journal-title":"Iscience"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1002\/ctm2.1783","article-title":"Rapid screening of acute promyelocytic leukaemia in daily batch specimens: A novel artificial intelligence-enabled approach to bone marrow morphology","volume":"14","author":"Xiao","year":"2024","journal-title":"Clin Trans Med"},{"key":"B17","doi-asserted-by":"publisher","first-page":"4378","DOI":"10.1038\/s41467-023-39676-y","article-title":"Computational analysis of peripheral blood smears detects disease-associated cytomorphologies","volume":"14","author":"de Almeida","year":"2023","journal-title":"Nat Commun"},{"key":"B18","doi-asserted-by":"publisher","first-page":"2230","DOI":"10.1182\/blood-2024-207126","article-title":"Leveraging ai for predicting genetic alterations in multiple myeloma through morphological analysis of bone marrow aspirates","volume":"144","author":"Bermejo-Pel\u00e1ez","year":"2024","journal-title":"Blood"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.3390\/cancers17111733","article-title":"Deep-learning-based prediction of t (11; 14) in multiple myeloma H&E-stained samples","volume":"17","author":"Kerner","year":"2025","journal-title":"Cancers"},{"key":"B20","doi-asserted-by":"publisher","first-page":"7163","DOI":"10.1182\/blood-2023-188324","article-title":"Artificial intelligence of the 2-d and 3-d bone marrow microenvironment to identify cytogenetic subtypes of multiple myeloma","volume":"142","author":"Mason","year":"2023","journal-title":"Blood"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.35202","article-title":"Detection of circulating plasma cells in peripheral blood using deep learning-based morphological analysis","volume":"130","author":"Chen","year":"2024","journal-title":"Cancer"},{"key":"B22","doi-asserted-by":"publisher","first-page":"102677","DOI":"10.1016\/j.media.2022.102677","article-title":"Segpc-2021: A challenge & dataset on segmentation of multiple myeloma plasma cells from microscopic images","volume":"83","author":"Gupta","year":"2023","journal-title":"Med Image Anal"},{"key":"B23","doi-asserted-by":"publisher","first-page":"105020","DOI":"10.1016\/j.cmpb.2019.105020","article-title":"Recognition of peripheral blood cell images using convolutional neural networks","volume":"180","author":"Acevedo","year":"2019","journal-title":"Comput Methods programs biomedicine"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.adq2162","article-title":"Deepheme, a high-performance, generalizable deep ensemble for bone marrow morphometry and hematologic diagnosis","volume":"17","author":"Sun","year":"2025","journal-title":"Sci Trans Med"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02857-3","article-title":"Towards a general-purpose foundation model for computational pathology","volume":"30","author":"Chen","year":"2024","journal-title":"Nat Med"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-02856-4","article-title":"A visual-language foundation model for computational pathology","volume":"30","author":"Lu","year":"2024","journal-title":"Nat Med"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-024-03141-0","article-title":"A foundation model for clinical-grade computational pathology and rare cancers detection","volume":"30","author":"Vorontsov","year":"2024","journal-title":"Nat Med"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07441-w","article-title":"A whole-slide foundation model for digital pathology from real-world data","volume":"630","author":"Xu","year":"2024","journal-title":"Nature"},{"key":"B29","doi-asserted-by":"publisher","first-page":"3640","DOI":"10.1038\/s41467-025-58796-1","article-title":"A clinical benchmark of public self-supervised pathology foundation models","volume":"16","author":"Campanella","year":"2025","journal-title":"Nat Commun"},{"key":"B30","article-title":"Attention-based deep multiple instance learning","author":"Ilse","year":"2018"},{"key":"B31","article-title":"Attention-challenging multiple instance learning for whole slide image classification","volume-title":"European Conference on Computer Vision (ECCV)","author":"Zhang","year":"2024"},{"key":"B32","doi-asserted-by":"crossref","DOI":"10.1109\/BIBM62325.2024.10822552","article-title":"Mammil: Multiple instance learning for whole slide images with state space models","author":"Fang","year":"2024"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0177-5","article-title":"Classification and mutation prediction from non\u2013small cell lung cancer histopathology images using deep learning","volume":"24","author":"Coudray","year":"2018","journal-title":"Nat Med"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1038\/s43018-020-0087-6","article-title":"Pan-cancer image-based detection of clinically actionable genetic alterations","volume":"1","author":"Kather","year":"2020","journal-title":"Nat Cancer"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1038\/s41375-021-01408-w","article-title":"Deep learning detects acute myeloid leukemia and predicts npm1 mutation status from bone marrow smears","volume":"36","author":"Eckardt","year":"2022","journal-title":"Leukemia"},{"key":"B36","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-00934-2_30","article-title":"Cell detection with star-convex polygons","volume-title":"International conference on medical image computing and computer-assisted intervention","author":"Schmidt","year":"2018"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01018-x","article-title":"Cellpose: a generalist algorithm for cellular segmentation","volume":"18","author":"Stringer","year":"2021","journal-title":"Nat Methods"},{"key":"B38","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2017.74","article-title":"Grad-cam: Visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"},{"key":"B39","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(14)70442-5","article-title":"International myeloma working group updated criteria for the diagnosis of multiple myeloma","volume":"15","author":"Rajkumar","year":"2014","journal-title":"Lancet Oncol"},{"key":"B41","doi-asserted-by":"publisher","first-page":"101544","DOI":"10.1016\/j.media.2019.101544","article-title":"Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology","volume":"58","author":"Tellez","year":"2019","journal-title":"Med image Anal"},{"key":"B42","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2017","journal-title":"arXiv preprint arXiv:1711.05101"},{"key":"B43","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2017.324","article-title":"Focal loss for dense object detection","author":"Lin","year":"2017"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1790130\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T05:37:15Z","timestamp":1773812235000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1790130\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":43,"alternative-id":["10.3389\/fonc.2026.1790130"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1790130","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,18]]},"article-number":"1790130"}}