Automated detection of dental artifacts for large-scale radiomic analysis in radiation oncology. (April 2021)

• Record Type: Journal Article
• Authors: Arrowsmith, Colin; Reiazi, Reza; Welch, Mattea L.; Kazmierski, Michal; Patel, Tirth; Rezaie, Aria; Tadic, Tony; Bratman, Scott; Haibe-Kains, Benjamin
• Is Part Of: Physics and imaging in radiation oncology. Volume 18(2021)
• Page Start: 41
• Page End: 47
• Publication Date: 2021-04
• Subjects: Radiomics -- Computed tomography -- Metal artifact detection -- Deep learning; Radiotherapy -- Periodicals; Radiation dosimetry -- Periodicals; Cancer -- Imaging -- Periodicals; Oncology -- Periodicals; 615.842
• DOI: 10.1016/j.phro.2021.04.001
• Languages: English
• ISSNs: 2405-6316

Abstract

Abstract: Background and purpose: Computed tomography (CT) is one of the most common medical imaging modalities in radiation oncology and radiomics research, the computational voxel-level analysis of medical images. Radiomics is vulnerable to the effects of dental artifacts (DA) caused by metal implants or fillings and can hamper future reproducibility on new datasets. In this study we seek to better understand the robustness of quantitative radiomic features to DAs. Furthermore, we propose a novel method of detecting DAs in order to safeguard radiomic studies and improve reproducibility. Materials and methods: We analyzed the correlations between radiomic features and the location of dental artifacts in a new dataset containing 3D CT scans from 3211 patients. We then combined conventional image processing techniques with a pre-trained convolutional neural network to create a three-class patient-level DA classifier and slice-level DA locator. Finally, we demonstrated its utility in reducing the correlations between the location of DAs and certain radiomic features. Results: We found that when strong DAs were present, the proximity of the tumour to the mouth was highly correlated with 36 radiomic features. We predicted the correct DA magnitude yielding a Matthews correlation coefficient of 0.73 and location of DAs achieving the same level of agreement as human labellers. Conclusions: Removing radiomic features or CT slices containing DAs could reduce the unwanted correlations between the location of DAs and radiomic features. Automated DA detection can be used to improve the reproducibility of radiomic studies; an important step towards creating effective radiomic models for use in clinical radiation oncology.