Abstract
BackgroundHuman papillomavirus (HPV)‐positive oropharyngeal squamous cell carcinoma (OPSCC) have better prognosis and treatment response compared to HPV‐negative OPSCC. This study aims to noninvasively predict HPV status of OPSCC using clinical and/or radiological variables.MethodsSeventy‐seven magnetic resonance radiomic features were extracted from T1‐weighted postcontrast images of the primary tumor of 153 patients. Logistic regression models were created to predict HPV status, determined with immunohistochemistry, based on clinical variables, radiomic features, and its combination. Model performance was evaluated using area under the curve (AUC).ResultsModel performance showed AUCs of 0.794, 0.764, and 0.871 for the clinical, radiomic, and combined models, respectively. Smoking, higher T‐classification (T3 and T4), larger, less round, and heterogeneous tumors were associated with HPV‐negative tumors.ConclusionModels based on clinical variables and/or radiomic tumor features can predict HPV status in OPSCC patients with good performance and can be considered when HPV testing is not available.
Highlights
Background: Human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) have better prognosis and treatment response compared to HPV-negative OPSCC
Models based on clinical variables and/or radiomic tumor features can predict HPV status in OPSCC patients with good performance and can be considered when HPV testing is not available
This study shows that logistic regression models based on clinical variables, MR-based radiomic features, or a combination of clinical and radiomic features can accurately predict HPV status in OPSCC patients
Summary
Human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) have better prognosis and treatment response compared to HPV-negative OPSCC. This study aims to noninvasively predict HPV status of OPSCC using clinical and/or radiological variables. Methods: Seventy-seven magnetic resonance radiomic features were extracted from T1-weighted postcontrast images of the primary tumor of 153 patients. Logistic regression models were created to predict HPV status, determined with immunohistochemistry, based on clinical variables, radiomic features, and its combination. Model performance was evaluated using area under the curve (AUC). Results: Model performance showed AUCs of 0.794, 0.764, and 0.871 for the clinical, radiomic, and combined models, respectively. Higher T-classification (T3 and T4), larger, less round, and heterogeneous tumors were associated with HPV-negative tumors. Conclusion: Models based on clinical variables and/or radiomic tumor features can predict HPV status in OPSCC patients with good performance and can be considered when HPV testing is not available
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