ArticleFrontiers in oncology2026
MRI-based intratumoral and peritumoral radiomics predicting neoadjuvant chemotherapy response in 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. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- AI in Musculoskeletal Imaging: An End-to-End Perspective.Journal of clinical medicine · 2026Review
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10 authors.
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Abstract
Objectives: To evaluate the predictive performance of a nomogram that integrates intratumoral and peritumoral MRI-based radiomics with clinical variables for assessing the efficacy of neoadjuvant chemotherapy (NAC) in patients with osteosarcoma (OS). Methods: This retrospective study included 93 patients with pathologically confirmed OS who underwent standard NAC. Intratumoral regions were manually segmented on axial T2-weighted fat-suppressed (T2WI-FS) images using ITK-SNAP, and peritumoral regions were generated semi-automatically by isotropic expansions of 2 mm, 4 mm, and 6 mm. Random forest classifiers were constructed separately for intratumoral, peritumoral, and combined intratumoral-peritumoral radiomics features. The optimal radiomics model was incorporated with significant clinical predictors to build an individualized nomogram. Model performance was assessed through the F1 score, Delong's test and receiver operating characteristic (ROC) curve analysis. Decision curve analysis (DCA) was applied to assess the model's clinical utility. Results: Multivariate logistic regression identified alkaline phosphatase (ALP) (OR = 1.003, 95% CI: 1.000 ~ 1.006, P = 0.031) and pathological fracture (PF)(OR = 2.575, 95% CI: 1.036 ~ 6.401, P = 0.042) as independent predictors of NAC response. Among all radiomics models, the Model_rad-intra + peri Conclusion: We developed and validated a nomogram that combines intratumoral and peritumoral MRI radiomics with clinical variables for predicting NAC efficacy in OS. The model demonstrated robust performance and may support early, individualized treatment evaluation and clinical decision-making in patients undergoing NAC.
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