ArticleFrontiers in oncology2026
Construction and validation of a multimodal MRI-based quantitative feature prediction model for the prognosis of non-metastatic primary osteosarcoma.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: Accurate prognosis assessment of non-metastatic primary osteosarcoma is essential for treatment decisions. This study aimed to develop and validate a pre-treatment predictive model using multimodal magnetic resonance imaging (MRI) quantitative parameters. Methods: This retrospective study included patients with non-metastatic primary osteosarcoma who received treatment at our hospital. Patients were divided into good or poor prognosis groups based on Response Evaluation Criteria in Solid Tumors at 30-month follow-up. We analyzed multimodal MRI data of these patients. Evaluated parameters included intramedullary extension measured by T2-weighted imaging, pure diffusion coefficient (D value), pseudo-diffusion coefficient (D* value), and apparent diffusion coefficient (ADC value) from diffusion-weighted imaging, and the contrast agent back-flux rate constant (Kep) from dynamic contrast-enhanced MRI. All these parameters were assessed as pre-treatment. Results: The training cohort included 169 good prognosis and 52 poor prognosis patients. Good prognosis patients showed significantly higher Kep (1.32 ± 0.24 vs 1.21 ± 0.21, P = 0.006), D value (0.95 ± 0.13 vs 0.84 ± 0.25, P = 0.003), D* value (19.78 ± 5.45 vs 17.34 ± 4.34, P = 0.004), and ADC value (1.11 ± 0.17 vs 1.01 ± 0.16, P<0.001), but lower intramedullary extension (9.62 ± 1.22 vs 10.50 ± 2.33, P = 0.012) compared to those with poor prognosis. The area under the curve (AUC) of the multivariate model integrating these features was 0.836. External validation confirmed the model's discriminatory ability (AUC = 0.812) and reproduced significant differences in Kep, intramedullary extension, D value, D* value, and ADC value between groups. Conclusion: This study developed a predictive model based on multimodal MRI quantitative features that effectively identified poor prognosis in non-metastatic primary osteosarcoma, providing a non-invasive assessment tool to optimize treatment strategies.
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