Dataset Information

Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner.

ABSTRACT: Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner models), and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on computed tomography (CT)-derived radiomic features to predict the association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). This experiment was performed on CT image datasets acquired from two different scanner manufacturers. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments reveal the effect of scanner manufacturer on the robustness of radiomic features, and the extent of this dependency is reflected in the performance of HPV prediction models. The results of this study highlight the importance of implementing an appropriate approach to reducing the impact of imaging parameters on radiomic features and consequently on the machine learning models, without removing features which are deemed non-robust but may contain learning information.

SUBMITTER: Reiazi R

PROVIDER: S-EPMC8125906 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Publications

Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner.

Reiazi Reza R Arrowsmith Colin C Welch Mattea M Abbas-Aghababazadeh Farnoosh F Eeles Christopher C Tadic Tony T Hope Andrew J AJ Bratman Scott V SV Haibe-Kains Benjamin B

Cancers 20210508 9

Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner models), and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on computed tomography (CT)-derived radiomic features to predict the association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). Th ...[more]

PMID: 34066857

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Project description:Background and purposeHuman papillomavirus-associated oropharyngeal squamous cell carcinoma is increasing in prevalence and typically occurs in younger patients than human papillomavirus-negative squamous cell carcinoma. While imaging features of human papillomavirus-positive versus human papillomavirus-negative squamous cell carcinoma nodal metastases have been described, characteristics distinguishing human papillomavirus-positive from human papillomavirus-negative primary squamous cell carcinomas have not been well established. The purpose of this project was to evaluate the use of texture features to distinguish human papillomavirus-positive and human papillomavirus-negative primary oropharyngeal squamous cell carcinoma.Materials and methodsFollowing institutional review board approval, 40 patients with primary oropharyngeal squamous cell carcinoma and known human papillomavirus status who underwent contrast-enhanced CT between December 2009 and October 2013 were included in this study. Segmentation of the primary lesion was manually performed with a semiautomated graphical-user interface. Following segmentation, an in-house-developed texture analysis program extracted 42 texture features from each segmented volume. A t test was used to evaluate differences in texture parameters between human papillomavirus-positive and human papillomavirus-negative squamous cell carcinomas.ResultsOf the 40 included patients, 29 had human papillomavirus-positive oropharyngeal squamous cell carcinoma and 11 had human papillomavirus-negative oropharyngeal squamous cell carcinoma. Significant differences were seen in the histogram parameters median (P = .006) and entropy (P = .016) and squamous cell carcinoma entropy (P = .043).ConclusionsThere are statistically significant differences in some texture features between human papillomavirus-positive and human papillomavirus-negative oropharyngeal tumors. Texture analysis may be considered an adjunct to the evaluation of human papillomavirus status and characterization of squamous cell carcinoma.

Dataset Information

Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner.

Publications

Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner.

Similar Datasets

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