Literature DB >> 30268005

Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis.

Rachel B Ger1, Daniel F Craft2, Dennis S Mackin2, Shouhao Zhou3, Rick R Layman4, A Kyle Jones4, Hesham Elhalawani5, Clifton D Fuller6, Rebecca M Howell2, Heng Li2, R Jason Stafford4, Laurence E Court7.   

Abstract

Radiomics studies have demonstrated the potential use of quantitative image features to improve prognostic stratification of patients with head and neck cancer. Imaging protocol parameters that can affect radiomics feature values have been investigated, but the effects of artifacts caused by intrinsic patient factors have not. Two such artifacts that are common in patients with head and neck cancer are streak artifacts caused by dental fillings and beam-hardening artifacts caused by bone. The purpose of this study was to test the impact of these artifacts and if needed, methods for compensating for these artifacts in head and neck radiomics studies. The robustness of feature values was tested by removing slices of the gross tumor volume (GTV) on computed tomography images from 30 patients with head and neck cancer; these images did not have streak artifacts or had artifacts far from the GTV. The range of each feature value over a percentage of the GTV was compared to the inter-patient variability at full volume. To determine the effects of beam-hardening artifacts, we scanned a phantom with 5 cartridges of different materials encased in polystyrene buildup. A cylindrical hole through the cartridges contained either a rod of polylactic acid to simulate water or a rod of polyvinyl chloride to simulate bone. A region of interest was drawn in each cartridge flush with the rod. Most features were robust with up to 50% of the original GTV removed. Most feature values did not significantly differ when measured with the polylactic acid rod or the polyvinyl chloride rod. Of those that did, the size of the difference did not exceed the inter-patient standard deviation in most cases. We conclude that simply removing slices affected by streak artifacts can enable these scans to be included in radiomics studies and that contours of structures can abut bone without being affected by beam hardening if needed.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Artifacts; Head and neck; Quantitative imaging; Radiomics; Texture

Mesh:

Year:  2018        PMID: 30268005      PMCID: PMC6217839          DOI: 10.1016/j.compmedimag.2018.09.002

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  22 in total

1.  On the impact of smoothing and noise on robustness of CT and CBCT radiomics features for patients with head and neck cancers.

Authors:  Hassan Bagher-Ebadian; Farzan Siddiqui; Chang Liu; Benjamin Movsas; Indrin J Chetty
Journal:  Med Phys       Date:  2017-04-17       Impact factor: 4.071

2.  Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma.

Authors:  Marta Bogowicz; Oliver Riesterer; Kristian Ikenberg; Sonja Stieb; Holger Moch; Gabriela Studer; Matthias Guckenberger; Stephanie Tanadini-Lang
Journal:  Int J Radiat Oncol Biol Phys       Date:  2017-06-15       Impact factor: 7.038

3.  Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma.

Authors:  Marta Bogowicz; Oliver Riesterer; Luisa Sabrina Stark; Gabriela Studer; Jan Unkelbach; Matthias Guckenberger; Stephanie Tanadini-Lang
Journal:  Acta Oncol       Date:  2017-08-18       Impact factor: 4.089

4.  Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to Human Papillomavirus status.

Authors:  Dan Ou; Pierre Blanchard; Silvia Rosellini; Antonin Levy; France Nguyen; Ralph T H Leijenaar; Ingrid Garberis; Philippe Gorphe; François Bidault; Charles Ferté; Charlotte Robert; Odile Casiraghi; Jean-Yves Scoazec; Philippe Lambin; Stephane Temam; Eric Deutsch; Yungan Tao
Journal:  Oral Oncol       Date:  2017-06-26       Impact factor: 5.337

5.  Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels.

Authors:  Muhammad Shafiq-Ul-Hassan; Geoffrey G Zhang; Kujtim Latifi; Ghanim Ullah; Dylan C Hunt; Yoganand Balagurunathan; Mahmoud Abrahem Abdalah; Matthew B Schabath; Dmitry G Goldgof; Dennis Mackin; Laurence Edward Court; Robert James Gillies; Eduardo Gerardo Moros
Journal:  Med Phys       Date:  2017-03       Impact factor: 4.071

Review 6.  Radiomics: extracting more information from medical images using advanced feature analysis.

Authors:  Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts
Journal:  Eur J Cancer       Date:  2012-01-16       Impact factor: 9.162

7.  Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study.

Authors:  Binsheng Zhao; Yongqiang Tan; Wei Yann Tsai; Lawrence H Schwartz; Lin Lu
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

8.  Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer.

Authors:  Chintan Parmar; Patrick Grossmann; Derek Rietveld; Michelle M Rietbergen; Philippe Lambin; Hugo J W L Aerts
Journal:  Front Oncol       Date:  2015-12-03       Impact factor: 6.244

9.  Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer.

Authors:  Xenia Fave; Lifei Zhang; Jinzhong Yang; Dennis Mackin; Peter Balter; Daniel Gomez; David Followill; Aaron Kyle Jones; Francesco Stingo; Zhongxing Liao; Radhe Mohan; Laurence Court
Journal:  Sci Rep       Date:  2017-04-03       Impact factor: 4.379

10.  Harmonizing the pixel size in retrospective computed tomography radiomics studies.

Authors:  Dennis Mackin; Xenia Fave; Lifei Zhang; Jinzhong Yang; A Kyle Jones; Chaan S Ng; Laurence Court
Journal:  PLoS One       Date:  2017-09-21       Impact factor: 3.240

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  10 in total

1.  Radiomic analysis identifies tumor subtypes associated with distinct molecular and microenvironmental factors in head and neck squamous cell carcinoma.

Authors:  Evangelia Katsoulakis; Yao Yu; Aditya P Apte; Jonathan E Leeman; Nora Katabi; Luc Morris; Joseph O Deasy; Timothy A Chan; Nancy Y Lee; Nadeem Riaz; Vaios Hatzoglou; Jung Hun Oh
Journal:  Oral Oncol       Date:  2020-06-30       Impact factor: 5.337

2.  Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Intracranial Hemorrhage.

Authors:  Andrew F Voter; Ece Meram; John W Garrett; John-Paul J Yu
Journal:  J Am Coll Radiol       Date:  2021-04-03       Impact factor: 6.240

3.  Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients.

Authors:  Rachel B Ger; Shouhao Zhou; Baher Elgohari; Hesham Elhalawani; Dennis M Mackin; Joseph G Meier; Callistus M Nguyen; Brian M Anderson; Casey Gay; Jing Ning; Clifton D Fuller; Heng Li; Rebecca M Howell; Rick R Layman; Osama Mawlawi; R Jason Stafford; Hugo Aerts; Laurence E Court
Journal:  PLoS One       Date:  2019-09-19       Impact factor: 3.240

4.  Combined CT radiomics of primary tumor and metastatic lymph nodes improves prediction of loco-regional control in head and neck cancer.

Authors:  Marta Bogowicz; Stephanie Tanadini-Lang; Matthias Guckenberger; Oliver Riesterer
Journal:  Sci Rep       Date:  2019-10-23       Impact factor: 4.379

5.  Automatic recognition and analysis of metal streak artifacts in head and neck computed tomography for radiomics modeling.

Authors:  Lise Wei; Benjamin Rosen; Martin Vallières; Thong Chotchutipan; Michelle Mierzwa; Avraham Eisbruch; Issam El Naqa
Journal:  Phys Imaging Radiat Oncol       Date:  2019-06-06

6.  Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma.

Authors:  Masaki Ogawa; Satoshi Osaga; Norio Shiraki; Daisuke Kawakita; Nobuhiro Hanai; Tsuneo Tamaki; Satoshi Tsukahara; Takatsune Kawaguchi; Misugi Urano; Yuta Shibamoto
Journal:  Sci Rep       Date:  2021-02-25       Impact factor: 4.379

7.  Reproducibility of radiomic features using network analysis and its application in Wasserstein k-means clustering.

Authors:  Jung Hun Oh; Aditya P Apte; Evangelia Katsoulakis; Nadeem Riaz; Vaios Hatzoglou; Yao Yu; Usman Mahmood; Harini Veeraraghavan; Maryam Pouryahya; Aditi Iyer; Amita Shukla-Dave; Allen Tannenbaum; Nancy Y Lee; Joseph O Deasy
Journal:  J Med Imaging (Bellingham)       Date:  2021-04-30

8.  Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images.

Authors:  Yiwen Liu; Tao Wen; Wei Sun; Zhenyu Liu; Xiaoying Song; Xuan He; Shuo Zhang; Zhenning Wu
Journal:  Sensors (Basel)       Date:  2022-07-28       Impact factor: 3.847

9.  Recurrent Convolutional Neural Networks for 3D Mandible Segmentation in Computed Tomography.

Authors:  Bingjiang Qiu; Jiapan Guo; Joep Kraeima; Haye Hendrik Glas; Weichuan Zhang; Ronald J H Borra; Max Johannes Hendrikus Witjes; Peter M A van Ooijen
Journal:  J Pers Med       Date:  2021-05-31

10.  Automated detection of dental artifacts for large-scale radiomic analysis in radiation oncology.

Authors:  Colin Arrowsmith; Reza Reiazi; Mattea L Welch; Michal Kazmierski; Tirth Patel; Aria Rezaie; Tony Tadic; Scott Bratman; Benjamin Haibe-Kains
Journal:  Phys Imaging Radiat Oncol       Date:  2021-04-21
  10 in total

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