Habitat-based MRI heterogeneity radiomics for predicting neoadjuvant chemotherapy response in osteosarcoma.

OBJECTIVES

Osteosarcoma is a highly heterogeneous malignant tumor with varied responses to neoadjuvant chemotherapy (NAC). This study developed heterogeneity radiomics (H-radiomics) based on habitat imaging to predict the treatment response of osteosarcoma patients after NAC.

METHODS

This study retrospectively included MRI scans (T1-weighted and T2-weighted) of osteosarcoma patients who underwent NAC and surgery at two centers between April 2015 and September 2024. Conventional radiomics (C-radiomics) features and habitat imaging-based H-radiomics features were extracted, with 2236 features obtained for each. Unsupervised reproducibility feature correlation analysis and the least absolute shrinkage and selection operator (LASSO) were used for feature selection, which resulted in 5 features being selected. Support vector machines (SVM) served as the classifier. C-radiomics, H-radiomics, and combined models were developed, and their performance was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

The training set included 57 patients (mean age, 17 years ± 11 [SD], 29 men) from Center 1, while the external validation set included 48 patients (mean age, 18 years ± 14 [SD], 28 men) from Center 2. In the external test set, the H-radiomics model achieved an AUC of 0.86, outperforming the C-radiomics model, which had an AUC of 0.79. The combined model demonstrated the best performance, with an AUC of 0.91. Additionally, the combined model achieved an accuracy of 85%, sensitivity of 88%, and specificity of 83%.

CONCLUSION

The combined model of H-radiomics and C-radiomics from multiparametric MRI demonstrates good performance in predicting the treatment response after NAC in osteosarcoma patients.