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Histology remains the gold standard, but it is invasive, costly, and not always accessible. This pilot study introduces a contrastive learning framework to address two challenges: (1) developing an AI system for accurate tumor classification and grading using radiographs alone, and (2) creating an enhanced multimodal pipeline when histology is available.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed radiographs from 188 patients and histology images from 63 patients at a tertiary academic medical center in the United States. The stepwise framework included: (1) unimodal classification, (2) contrastive learning to align cross-modal embeddings, and (3) classification using enhanced representations. Models were trained with 5-fold cross-validation and evaluated using AUC, accuracy, sensitivity, and specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our framework demonstrates promising performance across all tasks. The radiograph-based model achieved an AUC of 0.91 (95%CI: 0.82\u20131.00) in distinguishing tumors from avascular necrosis and fibrous dysplasia. For grading, contrastive learning improved radiograph-only performance from AUC 0.86 to 0.95 (95%CI: 0.85\u20131.00). The histology-only model improved from AUC 0.73 to 0.83 with contrastive enhancement. Multimodal integration achieved perfect discrimination (AUC\u2009=\u20091.00) on the available subset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study establishes proof-of-concept that contrastive learning can effectively bridge radiographic and histological representations for tumor assessment. The framework offers clinical potential by enabling non-invasive classification in resource-limited settings while allowing multimodal enhancement. These results warrant validation in larger, multi-institutional cohorts.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12880-026-02239-w","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T18:05:18Z","timestamp":1771610718000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal contrastive learning for non-invasive chondroid bone tumor classification and grading using radiographs"],"prefix":"10.1186","volume":"26","author":[{"given":"Peiying","family":"Hua","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica M.","family":"Sin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric R.","family":"Henderson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Ha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Darcy A.","family":"Kerr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed","family":"Hassanpour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,20]]},"reference":[{"key":"2239_CR1","doi-asserted-by":"publisher","unstructured":"Gereige R, Kumar M. 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Informed consent was waived due to the retrospective design and use of de-identified clinical data. Patient confidentiality was strictly maintained, and all data were handled in accordance with applicable privacy regulations. The authors remain accountable for all aspects of the work and will address any questions regarding the integrity or accuracy of the research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors approved the final manuscript and consent to its publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"159"}}