Pathology-interpretable radiomic model for predicting clinical outcome in patients with osteosarcoma: a retrospective, multicenter study

BACKGROUND: Osteosarcoma is the most prevalent primary malignant bone tumor. Radiomic models show promise in globally evaluating the prognosis of osteosarcoma; however, they lack biological interpretability. We aimed to develop a radiomic model using MRI to predict disease-free survival (DFS) in osteosarcoma patients and provide the underlying pathobiology of the model. METHODS: This retrospective study included 270 patients (training set, n = 166; external test set 1, n = 56; external test set 2, n = 48) with surgically treated and histology-proven osteosarcoma from 14 tertiary centers. A total of 1130 radiomic features were extracted from baseline MRI data. After dimensionality reduction, the radiomic model was developed on the training set and tested on the external test sets. The radiomics interpretability study used the hematoxylin and eosin (H&E) and immunohistochemistry (IHC) stained whole slide images (WSIs) of patients from the testing sets. Ten types of nuclear morphological features were extracted from each nucleus in H&E WSIs and aggregated into 150 patient-level features. Furthermore, five immune- and hypoxia-related IHC biomarkers, including CD3, CD8, CD68, FOXP3, and CAIX, were quantified from IHC WSIs. The correlation between the radiomic features and histopathologic markers was assessed using Spearman or Pearson correlation analysis, with multiple hypothesis testing controlled by the false discovery rate. RESULTS: The radiomic model comprising 12 features yielded a time-dependent AUC of 0.916 (95% CI: 0.893-0.939), 0.802 (95% CI: 0.763-0.840), and 0.895 (95% CI: 0.869-0.920) in the training set, external test set 1, and external test set 2, respectively. Notably, nine out of 12 radiomic features exhibited significant correlations with 17 cellular features, resulting in 32 pairs. Specifically, there were four (12.5%) pairs with absolute coefficient r (|r|) between 0.3 and 0.4, 22 (68.8%) pairs between 0.4 and 0.5, and six (18.8%) pairs exceeding 0.5. Four radiomic features were correlated with CD3 (r = 0.50-0.75), two features with CD8 (r = 0.46 and 0.60), and three features with CD8/FOXP3 (r = 0.69-0.81). CONCLUSIONS: The MRI-based radiomic model shows potential in predicting DFS in osteosarcoma patients. Most radiomic features show only moderate associations with H&E-derived nuclear morphological features; they exhibit higher correlations with immune-related biomarkers.