Applying multisequence MRI radiomics of the primary tumor and lymph node to predict HPV-related p16 status in patients with oropharyngeal squamous cell carcinoma.

Abstract

Human papillomavirus (HPV)-related oropharyngeal squamous cell carcinoma (OPSCC) is currently rising in incidence; however, a noninvasive approach to predicting HPV status that strongly correlates with p16 expression is lacking. This study aimed to develop a radiomics model based on multisequence magnetic resonance imaging (MRI) of primary tumor (PT) and lymph node (LN)-fused imaging features for the prediction of p16 status in OPSCC. In this retrospective study, 141 patients (comprising 116 patients in the training cohort and 25 patients in the testing cohort) with histopathologically confirmed (OPSCC) were enrolled consecutively from Fudan University Shanghai Cancer Center and Shanghai Ninth People's Hospital between January 2011 and December 2020. HPV status was determined by p16 immunohistochemistry analysis. A total of 2092 radiomics features were initially computed and extracted from the 3D-segmented PT and largest LN based on contrast-enhanced T1-weighted imaging (CE-T1WI) and T2-weighted imaging (T2WI). A support vector machine classifier was employed to build the machine learning-based classification models dependent on p16 status. The models were validated in the testing cohort. The area under the receiver operating characteristic curve (AUC) was computed to assess the performance of each model. This diagnostic study was not registered on the clinical trial platform. In the testing cohort, fusion models yielded better performance (AUC) compared with models based on a sole PT/LN [CE-T1WI: 0.80 (95% CI: 0.55-0.94) vs. 0.71 (95% CI: 0.47-0.88)/0.73 (95% CI: 0.48-0.90); T2WI: 0.74 (95% CI: 0.51-0.95) vs. 0.64 (95% CI: 0.38-0.85)/0.71 (95% CI: 0.48-0.88)]. Models based on multisequence imaging outperformed single CE-T1WI/T2WI models [PT: 0.74 (95% CI: 0.46-0.91) vs. 0.71 (95% CI: 0.47-0.88)/0.64 (95% CI: 0.38-0.85); LN: 0.78 (95% CI: 0.55-0.75) vs. 0.73 (95% CI: 0.48-0.90)/0.71 (95% CI: 0.48-0.88)]. Finally, the PT-LN fusion model based on multisequencing yielded the best classification performance with the highest AUC value of 0.91 (95% CI: 0.72-0.98) for the prediction of p16 expression. The differences between the performance of the final model and the other 8 models were significant (all P values <0.05). The results demonstrated that (I) the PT-LN fusion radiomics models improved the classification performance of the sole use of PT or LN for the prediction of p16 status, (II) the radiomics models based on multisequences outperformed the single-sequence models in the prediction of p16 status, and (III) the PT-LN fusion model based on multisequence MRI radiomics features could serve as a noninvasive method for acquiring the molecular information of patients with OPSCC, potentially assisting oncologists with their clinical decision-making.