Access the full text.
Sign up today, get DeepDyve free for 14 days.
C. Liao, Li‐yu Lee, Shiang-Fu Huang, I. Chen, C. Kang, Chien-Yu Lin, K. Fan, Hung-Ming Wang, S. Ng, T. Yen (2011)
Outcome analysis of patients with oral cavity cancer and extracapsular spread in neck lymph nodes.International journal of radiation oncology, biology, physics, 81 4
H. Sidhu, Salvatore Benigno, B. Ganeshan, Nikolaos Dikaios, E. Johnston, C. Allen, A. Kirkham, A. Groves, H. Ahmed, M. Emberton, S. Taylor, S. Halligan, S. Punwani (2016)
“Textural analysis of multiparametric MRI detects transition zone prostate cancer”European Radiology, 27
Evis Sala, E. Mema, E. Mema, Yuki Himoto, H. Veeraraghavan, James Brenton, A. Snyder, B. Weigelt, H. Vargas (2017)
Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging.Clinical radiology, 72 1
M. Soussan, Fanny Orlhac, M. Boubaya, L. Zelek, M. Ziol, V. Eder, I. Buvat (2014)
Relationship between Tumor Heterogeneity Measured on FDG-PET/CT and Pathological Prognostic Factors in Invasive Breast CancerPLoS ONE, 9
J. Fortin, D. Parker, B. Tunç, Takanori Watanabe, M. Elliott, K. Ruparel, D. Roalf, T. Satterthwaite, R. Gur, R. Gur, R. Schultz, R. Verma, R. Shinohara (2017)
Harmonization of multi-site diffusion tensor imaging dataNeuroImage, 161
P. Lambin, R. Leijenaar, T. Deist, J. Peerlings, E. Jong, J. Timmeren, S. Sanduleanu, R. Larue, A. Even, A. Jochems, Y. Wijk, H. Woodruff, J. Soest, T. Lustberg, E. Roelofs, W. Elmpt, A. Dekker, F. Mottaghy, J. Wildberger, S. Walsh (2017)
Radiomics: the bridge between medical imaging and personalized medicineNature Reviews Clinical Oncology, 14
E. Scalco, G. Rizzo (2017)
Texture analysis of medical images for radiotherapy applications.The British journal of radiology, 90 1070
W. Lodder, C. Lange, M. Velthuysen, M. Hauptmann, A. Balm, Michiel Brekel, F. Pameijer (2013)
Can extranodal spread in head and neck cancer be detected on MR imaging.Oral oncology, 49 6
G. Collewet, M. Strzelecki, F. Mariette (2004)
Influence of MRI acquisition protocols and image intensity normalization methods on texture classification.Magnetic resonance imaging, 22 1
V. Wreesmann, N. Katabi, F. Palmer, Pablo Montero, Jocelyn Migliacci, M. Gönen, D. Carlson, I. Ganly, J. Shah, R. Ghossein, S. Patel (2016)
Influence of extracapsular nodal spread extent on prognosis of oral squamous cell carcinomaHead & Neck, 38
A. Aiken, S. Poliashenko, J. Beitler, Amy Chen, K. Baugnon, A. Corey, K. Magliocca, P. Hudgins (2015)
Accuracy of Preoperative Imaging in Detecting Nodal Extracapsular Spread in Oral Cavity Squamous Cell CarcinomaAmerican Journal of Neuroradiology, 36
M. Hatt, M. Majdoub, M. Vallières, F. Tixier, C. Rest, D. Groheux, E. Hindié, A. Martineau, O. Pradier, R. Hustinx, R. Perdrisot, R. Guillevin, I. Naqa, D. Visvikis (2015)
18F-FDG PET Uptake Characterization Through Texture Analysis: Investigating the Complementary Nature of Heterogeneity and Functional Tumor Volume in a Multi–Cancer Site Patient CohortThe Journal of Nuclear Medicine, 56
Dunn Aa, Hans-Helge Müller, David Eisele, K. Kessel, Roland Moll, Jochen Werner (2006)
Meta-analysis of the prognostic significance of perinodal spread in head and neck squamous cell carcinomas (HNSCC) patients.European journal of cancer, 42 12
D. Randall, J. Lysack, Marc Hudon, K. Guggisberg, S. Nakoneshny, T. Matthews, J. Dort, S. Chandarana (2015)
Diagnostic utility of central node necrosis in predicting extracapsular spread among oral cavity squamous cell carcinomaHead & Neck, 37
N. Aggarwal, R. Agrawal (2012)
First and Second Order Statistics Features for Classification of Magnetic Resonance Brain ImagesJournal of Signal and Information Processing, 3
N Aggarwal, RK Agrawal (2012)
First and Second Order Statistics Features for Classification of Magnetic Resonance Brain ImagesJ Sign Process Syst, 2012
M. Mayerhoefer, P. Szomolanyi, D. Jirák, A. Materka, S. Trattnig (2009)
Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study.Medical physics, 36 4
R. Shaw, D. Lowe, J. Woolgar, James Brown, E. Vaughan, Christopher Evans, Huw Lewis‐Jones, R. Hanlon, G. Hall, S. Rogers (2009)
Extracapsular spread in oral squamous cell carcinomaHead & Neck, 32
Virendra Kumar, Yuhua Gu, Satrajit Basu, A. Berglund, S. Eschrich, M. Schabath, K. Forster, H. Aerts, A. Dekker, D. Fenstermacher, Dmitry Goldgof, L. Hall, P. Lambin, Y. Balagurunathan, R. Gatenby, R. Gillies (2012)
Radiomics: the process and the challenges.Magnetic resonance imaging, 30 9
Jacinto García, M. Lopez, Laura López, S. Bagué, E. Granell, M. Quer, X. León (2017)
Validation of the pathological classification of lymph node metastasis for head and neck tumors according to the 8th edition of the TNM Classification of Malignant Tumors.Oral oncology, 70
Y. Kimura, M. Sumi, N. Sakihama, F. Tanaka, Haruo Takahashi, Takashi Nakamura (2008)
MR Imaging Criteria for the Prediction of Extranodal Spread of Metastatic Cancer in the NeckAmerican Journal of Neuroradiology, 29
Omer Jalil, A. Afaq, B. Ganeshan, Uday Patel, Darren Boone, R. Endozo, Ashley Groves, Bruce Sizer, T. Arulampalam (2017)
Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long‐term survival in locally advanced rectal cancer treated by chemoradiotherapyColorectal Disease, 19
Zhifei Su, Zexi Duan, W. Pan, Chenzhou Wu, Y. Jia, B. Han, Chunyan Li (2016)
Predicting extracapsular spread of head and neck cancers using different imaging techniques: a systematic review and meta-analysis.International journal of oral and maxillofacial surgery, 45 4
Raymond Chai, T. Rath, Jonas Johnson, R. Ferris, G. Kubicek, U. Duvvuri, B. Branstetter (2013)
Accuracy of computed tomography in the prediction of extracapsular spread of lymph node metastases in squamous cell carcinoma of the head and neck.JAMA otolaryngology-- head & neck surgery, 139 11
H. Aerts, E. Velazquez, R. Leijenaar, C. Parmar, P. Grossmann, Sara Cavalho, J. Bussink, R. Monshouwer, Benjamin Haibe-Kains, D. Rietveld, F. Hoebers, M. Rietbergen, C. Leemans, A. Dekker, John Quackenbush, R. Gillies, P. Lambin (2014)
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approachNature Communications, 5
J. Carlton, A. Maxwell, Lindsey Bauer, Sara McElroy, L. Layfield, H. Ahsan, A. Agarwal (2017)
Computed tomography detection of extracapsular spread of squamous cell carcinoma of the head and neck in metastatic cervical lymph nodesThe Neuroradiology Journal, 30
C.D Llewellyn, N. Johnson, K. Warnakulasuriya (2001)
Risk factors for squamous cell carcinoma of the oral cavity in young people--a comprehensive literature review.Oral oncology, 37 5
D. Weatherspoon, A. Chattopadhyay, S. Boroumand, I. Garcia (2015)
Oral cavity and oropharyngeal cancer incidence trends and disparities in the United States: 2000-2010.Cancer epidemiology, 39 4
Y Kimura, M Sumi, N Sakihama, F Tanaka, H Takahashi, T Nakamura (2008)
MR imaging criteria for the prediction of extranodal spread of metastatic cancer in the neckAJNR Am J Neuroradiol, 29
Objective To explore the utility of MR texture analysis (MRTA) for detection of nodal extracapsular spread (ECS) in oral cavity squamous cell carcinoma (SCC). Methods 115 patients with oral cavity SCC treated with surgery and adjuvant (chemo)radiotherapy were identified retrospec- tively. First-order texture parameters (entropy, skewness and kurtosis) were extracted from tumour and nodal regions of interest (ROIs) using proprietary software (TexRAD). Nodal MR features associated with ECS (flare sign, irregular capsular contour; local infiltration; nodal necrosis) were reviewed and agreed in consensus by two experienced radiologists. Diagnostic perfor- mance characteristics of MR features of ECS were compared with primary tumour and nodal MRTA prediction using histology as the gold standard. Receiver operating characteristic (ROC) and regression analyses were also performed. Results Nodal entropy derived from contrast-enhanced T1-weighted images was significant in predicting ECS (p =0.018). MR features had varying accuracy: flare sign (70%); irregular contour (71%); local infiltration (66%); and nodal necrosis (64%). Nodal entropy combined with irregular contour was the best predictor of ECS (p = 0.004, accuracy 79%). Conclusion First-order nodal MRTA combined with imaging features may improve ECS prediction in oral cavity SCC. Key Points � Nodal MR textural analysis can aid in predicting extracapsular
European Radiology – Springer Journals
Published: Jun 5, 2018
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.