Detailed DescriptionImage Statistics |
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Modalities | CT, RTSTRUCT | Number of Patients | 3346 | Number of Studies | 3346 | Number of Series5203 | 9693 | Number of Images | 618837 | | Images Size (GB) | 334 |
Several studies have tried to address this data cleaning challenge using different approaches (Ger et al. 2018; Gjesteby et al. 2016). Recently, a convolutional neural network (CNN) was used to detect patient CT volumes containing artifacts with a precision-recall area under the curve (AUC) of (0.92) and accuracy of (98.4%) (Welch et al. 2020). However, to the author’s knowledge, no work has been done to differentiate between dental artifacts (DA) of different magnitudes or to quantify how the location of DAs could affect quantitative imaging features used to train radiomic models. Furthermore, previous DA detection studies have classified hand-drawn regions of interest (ROI) as DA positive or DA negative (REF) but have not examined the correlation between radiomic features in a given ROI and its distance from the DA source. These methods, even if effective at screening datasets for artifacts, could cause vast amounts of data to be unnecessarily marked as unclean, even if the artifacts do not homogeneously affect radiomic features in the patient’s image volume. In this study, we propose a novel two-step combinatorial algorithm to detect DAs on a per-slice basis in CT image datasets. Conventional image processing methods based on histogram-based thresholding and the CT sinogram are combined with a previously-published CNN network in order to create a three-class DA classifier and DA location detector for large radiomic datasets. This algorithm works on patient CT volumes with minimal preprocessing or manual annotation. Finally, we examined the correlation between quantitative imaging features and the physical distance between the DA and the gross tumour volume (GTV). Inclusion: The dataset used for this study consists of 4130 head and neck cancer CT image volumes collected from 2005 to 2007 treated with definitive RT at the University Health Network (UHN) in Toronto, Canada |