Literature DB >> 29025727

CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy.

H Kuno1,2, M M Qureshi1,3, M N Chapman1, B Li1, V C Andreu-Arasa1, K Onoue1, M T Truong1,3, O Sakai4,3,5.   

Abstract

BACKGROUND AND
PURPOSE: The accurate prediction of prognosis and failure is crucial for optimizing treatment strategies for patients with cancer. The purpose of this study was to assess the performance of pretreatment CT texture analysis for the prediction of treatment failure in primary head and neck squamous cell carcinoma treated with chemoradiotherapy.
MATERIALS AND METHODS: This retrospective study included 62 patients diagnosed with primary head and neck squamous cell carcinoma who underwent contrast-enhanced CT examinations for staging, followed by chemoradiotherapy. CT texture features of the whole primary tumor were measured using an in-house developed Matlab-based texture analysis program. Histogram, gray-level co-occurrence matrix, gray-level run-length, gray-level gradient matrix, and Laws features were used for texture feature extraction. Receiver operating characteristic analysis was used to identify the optimal threshold of any significant texture parameter. We used multivariate Cox proportional hazards models to examine the association between the CT texture parameter and local failure, adjusting for age, sex, smoking, primary tumor stage, primary tumor volume, and human papillomavirus status.
RESULTS: Twenty-two patients (35.5%) developed local failure, and the remaining 40 (64.5%) showed local control. Multivariate analysis revealed that 3 histogram features (geometric mean [hazard ratio = 4.68, P = .026], harmonic mean [hazard ratio = 8.61, P = .004], and fourth moment [hazard ratio = 4.56, P = .048]) and 4 gray-level run-length features (short-run emphasis [hazard ratio = 3.75, P = .044], gray-level nonuniformity [hazard ratio = 5.72, P = .004], run-length nonuniformity [hazard ratio = 4.15, P = .043], and short-run low gray-level emphasis [hazard ratio = 5.94, P = .035]) were significant predictors of outcome after adjusting for clinical variables.
CONCLUSIONS: Independent primary tumor CT texture analysis parameters are associated with local failure in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy.
© 2017 by American Journal of Neuroradiology.

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Mesh:

Year:  2017        PMID: 29025727      PMCID: PMC7963748          DOI: 10.3174/ajnr.A5407

Source DB:  PubMed          Journal:  AJNR Am J Neuroradiol        ISSN: 0195-6108            Impact factor:   3.825


  46 in total

1.  Use of pretreatment metabolic tumour volumes to predict the outcome of pharyngeal cancer treated by definitive radiotherapy.

Authors:  Chia-Hung Kao; Shih-Chieh Lin; Te-Chun Hsieh; Kuo-Yang Yen; Shih-Neng Yang; Yao-Ching Wang; Ji-An Liang; Chun-Hung Hua; Shang-Wen Chen
Journal:  Eur J Nucl Med Mol Imaging       Date:  2012-04-25       Impact factor: 9.236

2.  Prediction of locoregional control in head and neck squamous cell carcinoma with serial CT perfusion during radiotherapy.

Authors:  M T Truong; N Saito; A Ozonoff; J Wang; R Lee; M M Qureshi; S Jalisi; O Sakai
Journal:  AJNR Am J Neuroradiol       Date:  2011-07-14       Impact factor: 3.825

3.  Texture information in run-length matrices.

Authors:  X Tang
Journal:  IEEE Trans Image Process       Date:  1998       Impact factor: 10.856

4.  Dynamic contrast-enhanced MRI, diffusion-weighted MRI and 18F-FDG PET/CT for the prediction of survival in oropharyngeal or hypopharyngeal squamous cell carcinoma treated with chemoradiation.

Authors:  Shu-Hang Ng; Chun-Ta Liao; Chien-Yu Lin; Sheng-Chieh Chan; Yu-Chun Lin; Tzu-Chen Yen; Joseph Tung-Chieh Chang; Sheung-Fat Ko; Kang-Hsing Fan; Hung-Ming Wang; Lan-Yan Yang; Jiun-Jie Wang
Journal:  Eur Radiol       Date:  2016-02-24       Impact factor: 5.315

5.  Refining risk stratification for locoregional failure after chemoradiotherapy in human papillomavirus-associated oropharyngeal cancer.

Authors:  Jeffrey M Vainshtein; Matthew E Spector; Jonathan B McHugh; Ka Kit Wong; Heather M Walline; Serena A Byrd; Christine M Komarck; Mohannad Ibrahim; Matthew H Stenmark; Mark E Prince; Carol R Bradford; Gregory T Wolf; Scott McLean; Francis P Worden; Douglas B Chepeha; Thomas Carey; Avraham Eisbruch
Journal:  Oral Oncol       Date:  2014-02-22       Impact factor: 5.337

6.  Can pretreatment CT perfusion predict response of advanced squamous cell carcinoma of the upper aerodigestive tract treated with induction chemotherapy?

Authors:  A Zima; R Carlos; D Gandhi; I Case; T Teknos; S K Mukherji
Journal:  AJNR Am J Neuroradiol       Date:  2007-02       Impact factor: 3.825

7.  Pretreatment diffusion-weighted and dynamic contrast-enhanced MRI for prediction of local treatment response in squamous cell carcinomas of the head and neck.

Authors:  Sanjeev Chawla; Sungheon Kim; Lawrence Dougherty; Sumei Wang; Laurie A Loevner; Harry Quon; Harish Poptani
Journal:  AJR Am J Roentgenol       Date:  2013-01       Impact factor: 3.959

8.  Outcome prediction after surgery and chemoradiation of squamous cell carcinoma in the oral cavity, oropharynx, and hypopharynx: use of baseline perfusion CT microcirculatory parameters vs. tumor volume.

Authors:  Sotirios Bisdas; Shaun A Nguyen; Sharma K Anand; Gordana Glavina; Terry Day; Zoran Rumboldt
Journal:  Int J Radiat Oncol Biol Phys       Date:  2008-10-27       Impact factor: 7.038

9.  Dynamic contrast-enhanced MR imaging predicts local control in oropharyngeal or hypopharyngeal squamous cell carcinoma treated with chemoradiotherapy.

Authors:  Shu-Hang Ng; Chien-Yu Lin; Sheng-Chieh Chan; Tzu-Chen Yen; Chun-Ta Liao; Joseph Tung-Chieh Chang; Sheung-Fat Ko; Hung-Ming Wang; Shiang-Fu Huang; Yu-Chun Lin; Jiun-Jie Wang
Journal:  PLoS One       Date:  2013-08-07       Impact factor: 3.240

10.  Volumetric PET/CT parameters predict local response of head and neck squamous cell carcinoma to chemoradiotherapy.

Authors:  Atsushi Hanamoto; Mitsuaki Tatsumi; Yukinori Takenaka; Toshimitsu Hamasaki; Toshimichi Yasui; Susumu Nakahara; Yoshifumi Yamamoto; Yuji Seo; Fumiaki Isohashi; Kazuhiko Ogawa; Jun Hatazawa; Hidenori Inohara
Journal:  Cancer Med       Date:  2014-07-10       Impact factor: 4.452

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  29 in total

1.  CT-based Radiomic Signatures for Predicting Histopathologic Features in Head and Neck Squamous Cell Carcinoma.

Authors:  Pritam Mukherjee; Murilo Cintra; Chao Huang; Mu Zhou; Shankuan Zhu; A Dimitrios Colevas; Nancy Fischbein; Olivier Gevaert
Journal:  Radiol Imaging Cancer       Date:  2020-05-15

2.  CT Texture Analysis: Defining and Integrating New Biomarkers for Advanced Oncologic Imaging in Precision Medicine: A Comment on "CT Texture Analysis Potentially Predicts Local Failure in Head and Neck Squamous Cell Carcinoma Treated with Chemoradiotherapy".

Authors:  M Becker
Journal:  AJNR Am J Neuroradiol       Date:  2017-10-12       Impact factor: 3.825

3.  Discrimination of HPV status using CT texture analysis: tumour heterogeneity in oropharyngeal squamous cell carcinomas.

Authors:  Ji Young Lee; Miran Han; Kap Seon Kim; Su-Jin Shin; Jin Wook Choi; Eun Ju Ha
Journal:  Neuroradiology       Date:  2019-10-22       Impact factor: 2.804

4.  Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography.

Authors:  Hayato Tomita; Tsuneo Yamashiro; Joichi Heianna; Toshiyuki Nakasone; Yusuke Kimura; Hidefumi Mimura; Sadayuki Murayama
Journal:  Eur Radiol       Date:  2021-03-31       Impact factor: 5.315

5.  Prognostic value of pre-treatment CT texture analysis in combination with change in size of the primary tumor in response to induction chemotherapy for HPV-positive oropharyngeal squamous cell carcinoma.

Authors:  Tamari A Miller; Kayla R Robinson; Hui Li; Tanguy Y Seiwert; Daniel J Haraf; Li Lan; Maryellen L Giger; Daniel T Ginat
Journal:  Quant Imaging Med Surg       Date:  2019-03

6.  Tumor radiomic features complement clinico-radiological factors in predicting long-term local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancers.

Authors:  Jai Prakash Agarwal; Shwetabh Sinha; Jayant Sastri Goda; Kishor Joshi; Ritesh Mhatre; Sadhana Kannan; Sarbani Ghosh Laskar; Tejpal Gupta; Vedang Murthy; Ashwini Budrukkar; Naveen Mummudi; Balaji Ganeshan
Journal:  Br J Radiol       Date:  2020-02-26       Impact factor: 3.039

7.  Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models.

Authors:  Xiaoying Pan; Ting Zhang; QingPing Yang; Di Yang; Jean-Claude Rwigema; X Sharon Qi
Journal:  Br J Radiol       Date:  2020-06-19       Impact factor: 3.039

8.  Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features.

Authors:  Joseph J Foy; Mena Shenouda; Sahar Ramahi; Samuel Armato; Daniel Thomas Ginat
Journal:  J Med Imaging (Bellingham)       Date:  2020-12-30

9.  The application of radiomics in laryngeal cancer.

Authors:  Amarkumar Dhirajlal Rajgor; Shreena Patel; David McCulloch; Boguslaw Obara; Jaume Bacardit; Andrew McQueen; Eric Aboagye; Tamir Ali; James O'Hara; David Winston Hamilton
Journal:  Br J Radiol       Date:  2021-09-29       Impact factor: 3.039

Review 10.  Diagnostic Utility of Radiomics in Thyroid and Head and Neck Cancers.

Authors:  Maryam Gul; Kimberley-Jane C Bonjoc; David Gorlin; Chi Wah Wong; Amirah Salem; Vincent La; Aleksandr Filippov; Abbas Chaudhry; Muhammad H Imam; Ammar A Chaudhry
Journal:  Front Oncol       Date:  2021-07-07       Impact factor: 6.244

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