Can radiomics outperform radiologists in predicting Ewing sarcoma response to neoadjuvant chemotherapy from longitudinal MRI?
Authors
Affiliations (14)
Affiliations (14)
- Department of Radiation Oncology, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany. [email protected].
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universität München, Boltzmannstr. 3, 85748, Munich, Germany. [email protected].
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch- Weg 15, 55128, Mainz, Germany. [email protected].
- Department of Radiation Oncology, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Ismaninger Straße 22, 81675, Munich, Germany.
- Musculoskeletal Oncology, Department of Orthopaedic and Trauma Surgery, Physical Medicine and Rehabilitation, University Hospital Munich (LMU), Marchioninistrasse 15, 81377, Munich, Germany.
- Department of Informatics, Bioinformatics and Computational Biology-i12, Technische Universität München, Boltzmannstr. 3, 85748, Munich, Germany.
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Munich (LMU), Marchioninistrasse 15, 81377, Munich, Germany.
- Orthopaedic Oncology, Department of Orthopaedics and Trauma Surgery, LMU University Hospital, Marchioninistrasse 15, 81377, Munich, Germany.
- Department of Diagnostic and Interventional Radiology, School of Medicine and Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany.
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Hanns-Dieter-Hüsch- Weg 15, 55128, Mainz, Germany.
- Department of Orthopedics and Sports Orthopedics, Klinikum Rechts der Isar, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany.
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Munich, Germany.
- German Research Center for Environmental Health, Helmholtz Zentrum München (HMGU) GmbH, Institute of Radiation Medicine (IRM), Munich, Germany.
Abstract
This retrospective, multicenter study evaluated whether MRI-based radiomics can predict the histological response to neoadjuvant chemotherapy in Ewing sarcoma patients and compared their performance to that of radiological findings. Definitive histological assessment is only possible during surgery, potentially delaying therapy changes and increasing metastatic risk; therefore, a positive outcome would enable early adaptations. Radiomics features were extracted from pre- and post-neoadjuvant (after first chemotherapy cycle) chemotherapy scans of patients treated from 2004 to 2020. Four machine learning models were trained using nested cross-validation to predict histological response, and their performance was compared to models trained on semantic features derived from expert radiological readings. Diagnostic performance was measured by the area under the receiver operating characteristic curve (AUROC). 96 patients (mean age, 19.9 ± 11.7 years; 41 women) with Ewing sarcoma were evaluated. Radiomics features related to low gray level intensity changes in T1 fat-saturated contrast-enhanced sequences showed the highest responder identification performance (AUROC, 0.62; 95% CI: 0.59, 0.65), surpassing the best radiological model, which relied on the relative change in maximum diameter of extratumoral soft tissue components (AUROC, 0.58; 95% CI: 0.56, 0.60). Radiomics models relying on pre-treatment T2-weighted data performed similarly (AUROC, 0.61; 95% CI: 0.58, 0.64), outperforming the clinical baseline (AUROC, 0.53; 95% CI: 0.51, 0.55). MRI-based radiomics moderately identified responders to neoadjuvant chemotherapy, suggesting a role for earlier, tailored therapeutic strategies in Ewing sarcoma. Larger studies to validate these findings within a multimodal framework are warranted. Radiomics extracted from MRI scans may aid with non-size tissue signals in early prediction of neoadjuvant chemotherapy response in Ewing sarcoma patients, potentially guiding personalized treatment adaptations, reducing delays for non-responders, and improving patient outcomes. These exploratory findings are complementary to standard size-based assessment from clinical routine, and warrant larger, prospective trials to be established.