Non-invasive CT-based Deep Learning for Human Papillomavirus Status Prediction in Oropharyngeal Cancer.

RATIONALE AND

OBJECTIVES

Human papillomavirus (HPV) status is a critical biomarker for treatment planning and patient management in oropharyngeal cancer (OPC). The purpose of this study was to develop a non-invasive and rapid alternative that achieves performance comparable to established clinical standards while enabling HPV classification without requiring additional examinations beyond the routine diagnostic and treatment workflow.

MATERIALS AND

METHODS

We employed a siamese neural network framework with 3D DenseNet backbones in each branch, trained in an end-to-end manner. To further enhance model performance, tumor mask volumes and frequency-domain representations of CT images were incorporated as additional modalities. A total of 1612 valid cases from three independent datasets were used to evaluate model performance, including internal validation on a large-scale dataset and three external validation experiments to ensure robustness and generalizability.

RESULTS

The proposed methods achieved an average AUC of 0.875 (95% confidence intervals (CI), [0.863,0.887]), an average Precision of 0.81 (95% CI, [0.8-0.82]), an average Recall of 0.89 (95% CI, [0.867,0.913]), and an average AUPRC of 0.922 (95% CI, [0.91,0.934]), proposed method achieved best result in all four metrics compared with reference methods. Pre-trained model achieved average AUC of 0.767 (95% CI, [0.735,0.781]), 0.726 (95% CI, [0.709,0.741]) and 0.839 (95% CI, [0.821,0.856]) in three external validations.

CONCLUSION

The proposed method achieved SOTA performance among CT-based HPV prediction methods for OPC and demonstrated performance comparable to established clinical methods. Furthermore, in accordance with FDA guidelines for Software as a Medical Device, the proposed radiogenomics approach demonstrated "good-enough" performance to support patient-informed decision-making in future clinical trials.