{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T21:00:40Z","timestamp":1792184440413,"version":"4.4.0"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"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. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      The bone marrow immune microenvironment (BMME) shapes treatment response in multiple myeloma (MM), yet routine diagnostic workup primarily assesses tumor burden rather than immune competence. We developed ImmunoCast-MM, a cross-modal deep learning framework that extracts immunologically relevant signals from two examinations routinely performed at diagnosis: Wright\u2013Giemsa-stained bone marrow aspirate smears and whole-body\n                      <jats:sup>18<\/jats:sup>\n                      F-FDG PET\/CT.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The cytomorphology branch used DinoBloom embeddings to classify individual cells across five hierarchical levels. The PET\/CT branch generated a multi-organ inflammation fingerprint from tumor, spleen, lymph node, and diffuse bone marrow compartments. A contrastive fusion module aligned the two imaging modalities with a flow cytometry reference panel and generated an Immune Dysfunction Index (IDI) along a learned effector\u2013suppressor axis. ImmunoCast-MM was evaluated retrospectively in 243 patients with newly diagnosed MM. Associations with flow cytometric measurements, progression-free survival, and daratumumab response were assessed, with adjustment for International Staging System stage, cytogenetic risk, and age.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      On a held-out validation subset, decoded cytomorphologic indices correlated with matched flow cytometric fractions, with Spearman\n                      <jats:italic>\u03c1<\/jats:italic>\n                      values of 0.68\u20130.81 across three decoded index\u2013panel pairs; all Benjamini\u2013Hochberg-adjusted (p) values were (&amp;lt;0.001). Unsupervised clustering of the fused embeddings identified immune-competent and immune-exhausted archetypes that differed in progression-free survival and response to daratumumab. Separately, adding the IDI to conventional risk markers increased the concordance index from 0.58 to 0.75 ((p&amp;lt;0.001)) and improved the area under the receiver operating characteristic curve for daratumumab response from 0.55 to 0.81 ((p&amp;lt;0.001)).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>ImmunoCast-MM reframes two standard diagnostic examinations as non-invasive profilers of the BMME. The framework may support risk assessment and immunotherapy stratification, particularly in centers without access to high-dimensional flow cytometry.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1856163","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:55:17Z","timestamp":1782280517000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Cross-modal fusion of cytomorphology and 18F-FDG PET\/CT for non-invasive bone marrow immune microenvironment decoding in multiple myeloma"],"prefix":"10.3389","volume":"17","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":"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 Artificial Intelligence, Ningbo University of Finance and Economics","place":["Ningbo, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shikun","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Ningbo University of Finance and Economics","place":["Ningbo, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaihong","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Hematology, The First Affiliated Hospital of Ningbo University","place":["Ningbo, Zhejiang, 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