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

Background: Osteosarcoma is the most prevalent primary malignant bone tumor. Radiomic models demonstrate 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 to provide 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 centres. A total of 1130 radiomic features were extracted from pre-treatment MRI. After dimensionality reduction, radiomic model was built on the training set and tested on the external test sets. Radiomics interpretability study leveraged 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. Moreover, five immune- and hypoxia-related IHC biomarkers—CD3, CD8, CD68, FOXP3, and CAIX—were quantified from IHC WSIs. The correlation between the radiomic features and histopathologic biomarkers was assessed using Spearman correlation analysis. Results: The radiomic model including 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. All 12 radiomic features exhibited significant correlations with 109-133 cellular features, totaling 1460 (81.1%) pairs. In detail, there were 574 pairs with absolute coefficient r (|r|) between 0 and 0.1, 516 pairs between 0.1 and 0.2, 241 pairs between 0.2 and 0.3, 99 pairs between 0.3 and 0.4, and 30 pairs exceeding 0.4. Six radiomic features were correlated with CAIX (|r| = 0.03-0.17), 10 features with CD3 (|r| = 0.02-0.71), eight features with CD8 (|r| = 0.05-0.42), nine features with FOXP3 (|r| = 0.01-0.55), 11 features with CD8 / FOXP3 ratio (|r| = 0.004-0.74), and 11 features with CD68 (|r| = 0.02-0.47). Conclusions: The MRI-based radiomic model effectively predicts DFS in osteosarcoma patients. The correlation strength between radiomic features and histopathologic biomarkers varies.