Radiomics in Head and Neck Radiation Therapy: Impact of Metal Artifact Reduction

Block, Alec M.; Cozzi, Francesca; Patel, Rakesh; Sürücü, Murat; Hurst, Newton J.; Emami, Bahman; Roeske, John C.

doi:10.1016/j.ijrobp.2017.06.2146

Cited by 6 publications

(10 citation statements)

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“…These methods commonly reduce the impact of DAs by interpolating voxel HUs within the image sinogram (Abdoli et al 2010), but research is also being done to salvage image signal using deep learning methods (Zhang and Yu 2018, Huang et al 2018, Hu et al 2019. Although these methods are designed to reduce the qualitative impact of the artifact, it is possible for new artifacts to be generated in the image that mitigate its benefits in fields such as radiomics (Block et al 2016). In the future, a classification model, such as the one presented in this paper, could determine whether these methods are effectively removing the DA prior to utilizing the data for other purposes.…”

Section: Discussionmentioning

confidence: 99%

Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth

et al. 2020

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“…Our study accounted for the fact that artifacts from metal dental fillings are known to encumber target delineation and subsequent radiomics analysis [59,60]. For this purpose, the presence of visible dental 1 artifacts effect anywhere in the slices that encompassed 'ORN' or 'Control' VOIs at any time point precluded the integration of this scan and hence the patient's data as an input to the model.…”

Section: Discussionmentioning

confidence: 99%

Discovering early imaging biomarkers of osteoradionecrosis in oropharyngeal cancer by characterization of temporal changes in computed tomography mandibular radiomic features

Barua

Elhalawani

Volpe

et al. 2020

Preprint

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Osteoradionecrosis (ORN) is a major side-effect of radiation therapy in oropharyngeal cancer (OPC) patients. In this study, we demonstrate that early prediction of ORN is possible by analyzing the temporal evolution of mandibular subvolumes receiving radiation. For our analysis, we use computed tomography (CT) scans from 21 OPC patients treated with Intensity Modulated Radiation Therapy (IMRT) with subsequent radiographically-proven ≥ grade II ORN, at three different time points: pre-IMRT, 2-months, and 6-months post-IMRT. For each patient, radiomic features were extracted from a mandibular subvolume that developed ORN and a control subvolume that received the same dose but did not develop ORN. We used a Multivariate Functional Principal Component Analysis (MFPCA) approach to characterize the temporal trajectories of these features. The proposed MFPCA model performs the best at classifying ORN vs Control subvolumes with an area under curve (AUC) = 0.74 (95% confidence interval (C.I.): 0.61-0.90), significantly outperforming existing approaches such as a pre-IMRT features model or a delta model based on changes at intermediate time points, i.e. at 2- and 6-month follow-up. This suggests that temporal trajectories of radiomics features derived from sequential pre- and post-RT CT scans can provide markers that are correlates of RT-induced mandibular injury, and consequently aid in earlier management of ORN.

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“…Moreover, head and neck radiomics are subject to the effects of image artifacts from intrinsic patient factors, such as metal dental implants and bone. The effects of resulting streak artifacts and beam-hardening artifacts on robustness of extracted radiomics features have been reported ( 3 , 40 ). Our approach within this study was to remove slices of the GTV on computed tomography that were affected by artifacts.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

et al. 2018

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Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings.

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Radiomics in Head and Neck Radiation Therapy: Impact of Metal Artifact Reduction

Cited by 6 publications

References 0 publications

Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth

Automatic classification of dental artifact status for efficient image veracity checks: effects of image resolution and convolutional neural network depth

Discovering early imaging biomarkers of osteoradionecrosis in oropharyngeal cancer by characterization of temporal changes in computed tomography mandibular radiomic features

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

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