Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status

Abstract

We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). This retrospective study collected data of 60 patients (48 HPV-positive and 12 HPV-negative) with newly diagnosed histopathologically proved OPSCC, who underwent head and neck MRIs consisting of axial T1WI, T2WI, CE-T1WI, and apparent diffusion coefficient (ADC) maps from diffusion-weighted imaging (DWI). The median age was 59 years (the range being 35 to 85 years), and 83.3% of patients were male. The imaging data were randomised into a training set (32 HPV-positive and 8 HPV-negative OPSCC) and a test set (16 HPV-positive and 4 HPV-negative OPSCC) in each fold. 1618 quantitative features were extracted from manually delineated regions-of-interest of primary tumour and one definite lymph node in each sequence. After feature selection by using the least absolute shrinkage and selection operator (LASSO), three different machine-learning classifiers (logistic regression, random forest, and XG boost) were trained and compared in the setting of various combinations between four sequences. The highest diagnostic accuracies were achieved when using all sequences, and the difference was significant only when the combination did not include the ADC map. Using all sequences, logistic regression and the random forest classifier yielded higher accuracy compared with the that of the XG boost classifier, with mean area under curve (AUC) values of 0.77, 0.76, and 0.71, respectively. The machine-learning classifier of non-invasive and quantitative radiomics signature could guide the classification of the HPV status.

Diagnostic Value of Magnetic Resonance Elastography Radiomics Analysis for the Assessment of Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease

“…To overcome this limitation, we analyzed the results using 20 repeated 10-fold strati ed cross-validation. This method, as shown in previous studies [32][33][34][35], was the best way to increase the reliability of the evaluation using limited data. Third, external validation for machine learning was not performed, limiting the generalizability of our results.…”

The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment

“…No researcher registered a prospective study in a trial database or performed a cost-effectiveness analysis, but 4 investigators did share the data obtained. Elhalawani shared the dataset generated for the study in Figshare repository [ 22 ], other authors share the code [ 23 , 24 ], while Suh shared the datasets and the analysis on reasonable request [ 25 ].…”

“…The resulting relevant radiomic features extracted from CT images have shown potential for HPV status prediction either in internal [ 8 , 35 – 38 ] or external validation [ 39 ]. Other authors employed T1-weighted post contrast [ 25 , 40 , 41 ] and ADC [ 25 , 42 ] images on MRI, and a combination of PET-based and CT-based radiomics on PET/CT [ 23 ]. In some cases, specific steps within the radiomic pipeline were also explored, such as comparison between 2 and 3D segmentation [ 43 ] and variations due to different CT scanners [ 44 ].…”

Treatment response prediction using MRI‐based pre‐, post‐, and delta‐radiomic features and machine learning algorithms in colorectal cancer

“…They reported that using random forest as a feature selector and classifier resulted in the highest prognostic performance. In another study, Suh et al 57 compared the predictive performance of different algorithms, including logistic regression, random forest, and XGboost for human papillomavirus status classification using MRI radiomic features. They reported that logistic regression and random forest provided the highest accuracy.…”