Abstract
Background: No robust biomarkers have been identified to predict the efficacy of programmed cell death protein 1 (PD-1) inhibitors in patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). We aimed to develop radiomic models using pre-immunotherapy MRI to predict the response to PD-1 inhibitors and the patient prognosis. Methods: This study included 246 LANPC patients (training cohort, n = 117; external test cohort, n = 129) from 10 centers. The best-performing machine learning classifier was employed to create the radiomic models. A combined model was constructed by integrating clinical and radiomic data. A radiomic interpretability study was performed with whole slide images (WSIs) stained with hematoxylin and eosin (H&E) and immunohistochemistry (IHC). A total of 150 patient-level nuclear morphological features (NMFs) and 12 cell spatial distribution features (CSDFs) were extracted from WSIs. The correlation between the radiomic and pathological features was assessed using Spearman correlation analysis. Results: The radiomic model outperformed the clinical and combined models in predicting treatment response (area under the curve: 0.760 vs. 0.559 vs. 0.652). For overall survival estimation, the combined model performed comparably to the radiomic model but outperformed the clinical model (concordance index: 0.858 vs. 0.812 vs. 0.664). Six treatment response-related radiomic features correlated with 50 H&E-derived (146 pairs, |r|= 0.31 to 0.46) and 2 to 26 IHC-derived NMF, particularly for CD45RO (69 pairs, |r|= 0.31 to 0.48), CD8 (84, |r|= 0.30 to 0.59), PD-L1 (73, |r|= 0.32 to 0.48), and CD163 (53, |r| = 0.32 to 0.59). Eight prognostic radiomic features correlated with 11 H&E-derived (16 pairs, |r|= 0.48 to 0.61) and 2 to 31 IHC-derived NMF, particularly for PD-L1 (80 pairs, |r|= 0.44 to 0.64), CD45RO (65, |r|= 0.42 to 0.67), CD19 (35, |r|= 0.44 to 0.58), CD66b (61, |r| = 0.42 to 0.67), and FOXP3 (21, |r| = 0.41 to 0.71). In contrast, fewer CSDFs exhibited correlations with specific radiomic features. Conclusion: The radiomic model and combined model are feasible in predicting immunotherapy response and outcomes in LANPC patients. The radiology-pathology correlation suggests a potential biological basis for the predictive models.