Background and purpose: Computed tomography (CT) radiomics of head and neck cancer (HNC) images is susceptible to dental implant artifacts. This work devised and validated an automated algorithm to detect CT metal artifacts and investigate their impact on subsequent radiomics analyses. A new method based on features from total variation, gradient directional distribution, and Hough transform was developed and evaluated.
Materials and methods: Two HNC datasets were analyzed: a training set of 131 patients for developing the detection algorithm and a testing set of 220 patients. Seven designated features were extracted from ROIs (regions of interest) and machine learning with random forests was used for building the artifact detection algorithm. Performance was assessed using the area under the receiver operating characteristics curve (AUC).
Results: The testing results of artifacts detection yielded a cross-validated AUC of 0.91 (95% CI: 0.89-0.94), and a test AUC of 0.89. External testing validation yielded an accuracy of 0.82. For radiomics model prediction, training with artifacts yielded an AUC of 0.64 (95% CI: 0.63-0.65), while training on images without artifacts improved the AUC to 0.75 (95% CI: 0.74-0.76). This was compared to visual inspection of artifacts (AUC = 0.71 [95% CI: 0.69-0.73]).
Conclusion: We developed a new method for automated and efficient detection of streak artifacts. We also showed that such streak artifacts in HNC CT images can worsen the performance of radiomics modeling.
Keywords: Artifact detection; Machine learning; Radiomics.
© 2019 The Authors.