Narrow band imaging-based radiogenomics for predicting radiosensitivity in nasopharyngeal carcinoma.

Objectives: This study aims to assess the efficacy of narrow band imaging (NBI) endoscopy in utilizing radiomics for predicting radiosensitivity in nasopharyngeal carcinoma (NPC), and to explore the associated molecular mechanisms. Materials: The study included 57 NPC patients who were pathologically diagnosed and underwent RNA sequencing. They were categorized into complete response (CR) and partial response (PR) groups after receiving radical concurrent chemoradiotherapy. We analyzed 267 NBI images using ResNet50 for feature extraction, obtaining 2048 radiomic features per image. Using Python for deep learning and least absolute shrinkage and selection operator for feature selection, we identified differentially expressed genes associated with radiomic features. Subsequently, we conducted enrichment analysis on these genes and validated their roles in the tumor immune microenvironment through single-cell RNA sequencing. Results: After feature selection, 54 radiomic features were obtained. The machine learning algorithm constructed from these features showed that the random forest algorithm had the highest average accuracy rate of 0.909 and an area under the curve of 0.961. Correlation analysis identified 30 differential genes most closely associated with the radiomic features. Enrichment and immune infiltration analysis indicated that tumor-associated macrophages are closely related to treatment responses. Three key NBI differentially expressed immune genes (NBI-DEIGs), namely CCL8, SLC11A1, and PTGS2, were identified as regulators influencing treatment responses through macrophages. Conclusion: NBI-based radiomics models introduce a novel and effective method for predicting radiosensitivity in NPC. The molecular mechanisms may involve the functional states of macrophages, as reflected by key regulatory genes.