25dkou471 10.3205/25dkou471 urn:nbn:de:0183-25dkou4711 Meeting Abstract Gillani Gillani Amna Ahmed AA

Technical University of Munich, Munich, Deutschland

author von Eisenhart-Rothe von Eisenhart-Rothe Rüdiger R

Technical University of Munich, Munich, Deutschland

author Gersing Gersing Alexandra A

Technical University of Munich, Munich, Deutschland

author Rueckert Rueckert Daniel D

Technical University of Munich, Munich, Deutschland

author Hinterwimmer Hinterwimmer Florian F

Technical University of Munich, Munich, Deutschland

author German Medical Science GMS Publishing House

Düsseldorf

610 20251031 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0634 471 Deutsche Gesellschaft für Orthopädie und Unfallchirurgie Deutsche Gesellschaft für Orthopädie und Orthopädische Chirurgie Deutsche Gesellschaft für Unfallchirurgie Berufsverband für Orthopädie und Unfallchirurgie Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2025) Abstracts | Digitalisierung 2 Berlin 20251028 20251031 AB74-4322 TextObjectives and questions: Tumors of the musculoskeletal system are rare and heterogeneous which leads to limited experience among non-tumor experts. Three entities — Enchondroma, Chondrosarcoma, and Atypical Cartilaginous Tumor — are of particular clinical significance due to their distinct prognoses and treatment requirements. Misdiagnoses or failure to diagnose can lead to unnecessary interventions or treatment delays. Although Deep Learning (DL) has shown potential in cancer classification, it is still in its infancy in orthopedic oncology. This study explores the potential of multi-modal DL models integrating CT, T1, and T2-weighted MRI to improve diagnostic accuracy for these tumors. Specifically, we aim to determine whether (1) a combination of modalities is more diagnostically useful than a single modality, (2) models trained on data from one hospital can generalize to another, and (3) the DL model’s performance is comparable with radiologists.Material and methods: This retrospective study used data from 369 patients from Institute-1 and 198 patients from Institute-2. Models were developed using ResNet feature extraction and an intermediate fusion strategy based on the EmbraceNet architecture to integrate different modalities. Both uni-modal and multi-modal approaches were explored, considering individual modalities as well as their combinations. The DL models were trained on data from Institute-1 and evaluated on both a test dataset from Institute-1 and the dataset from Institute-2. Performance metrics, including F1 score, accuracy, sensitivity, and specificity were calculated and compared with those of senior and junior radiologists.Results: The multi-modal model combining CT and T1-weighted MRI outperformed all three uni-modal models and the model utilizing all three modalities (CT, T1, T2). This model achieved an F1 score of 0.68 on the test dataset from Institute-1 which was comparable to senior radiologists. The performance on the external dataset from Institute-2 was lower (F1 score: 0.54) which was comparable to junior radiologists. This difference was due to the inter-institutional variability in the datasets which was mathematically demonstrated.Discussion and conclusions: The study demonstrates that multi-modal deep-learning models can achieve performance similar to clinicians in differentiating Enchondromas, Chondrosarcomas, and ACT. Combining CT and T1-weighted MRI proved more effective than either modality alone. Interestingly, T2-weighted MRI did not provide diagnostic value for the model whereas radiologists rely on it for diagnosis. The results show the potential for integrating DL in clinical workflows for musculoskeletal tumor detection to reduce diagnostic delays. Future work should focus on enhancing model generalization by training on diverse datasets. 0 0 0 0