Literature DB >> 29268543

Texture analysis of CT imaging for assessment of esophageal squamous cancer aggressiveness.

Song Liu1, Huanhuan Zheng1, Xia Pan1, Ling Chen2, Minke Shi3, Yue Guan4, Yun Ge4, Jian He1, Zhengyang Zhou1.   

Abstract

BACKGROUND: To explore the role of texture analysis of computed tomography (CT) images in preoperative assessment of esophageal squamous cell carcinoma (ESCC) aggressiveness.
METHODS: Seventy-three patients with pathologically confirmed ESCC underwent unenhanced and contrast enhanced CT imaging preoperatively. Texture analysis was performed on unenhanced and contrast enhanced CT images, respectively. Six CT texture parameters were obtained. One-way analysis of variance or independent-samples t-test (normality), independent-samples Kruskal-Wallis test or Mann-Whitney U test (non-normality), binary Logistic regression analysis (multivariable), Spearman correlation test, receiver operating characteristic (ROC) curve analysis and intraclass correlation coefficient (ICC) were used for statistical analyses.
RESULTS: Kurtosis was an independent predictor for T stages (T1-2 vs. T3-4) as well as overall stages (I-II vs. III-IV) based on unenhanced CT images, while entropy was an independent predictor for T stages (T1-2 vs. T3-4), lymph node metastasis (N- vs. N+) and overall stages (I/II vs. III/IV). Skew and kurtosis based on unenhanced CT images showed significant differences among N stages (N0, N1, N2 and N3) as well as 90th percentile based on contrast enhanced CT images. In correlation with T stage of ESCC, kurtosis and entropy significantly correlated with T stage both on unenhanced and contrast enhanced CT images. Reversely, entropy and 90th percentile based on contrast enhanced CT images showed significant correlations with N stage (r: 0.526, 0.265; both P<0.05), as well as overall stage (r: 0.562, 0.315; both P<0.05). For identifying ESCC with different T stages (T1-2 vs. T3-4), lymph node metastasis (N- vs. N+) and overall stages (I/II vs. III/IV), entropy based on contrast enhanced CT images, showed good performance with area under ROC curve area under curve (AUC) of 0.637, 0.815 and 0.778, respectively.
CONCLUSIONS: Texture analysis of CT images held great potential in differentiating different T, N and overall stages of ESCC preoperatively, while failed to assess the differentiation degrees.

Entities:  

Keywords:  Computed tomography (CT); esophageal cancer; staging; texture analysis

Year:  2017        PMID: 29268543      PMCID: PMC5720997          DOI: 10.21037/jtd.2017.06.46

Source DB:  PubMed          Journal:  J Thorac Dis        ISSN: 2072-1439            Impact factor:   2.895


  27 in total

1.  Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?

Authors:  Taryn Hodgdon; Matthew D F McInnes; Nicola Schieda; Trevor A Flood; Leslie Lamb; Rebecca E Thornhill
Journal:  Radiology       Date:  2015-04-23       Impact factor: 11.105

2.  Lung nodule segmentation in chest computed tomography using a novel background estimation method.

Authors:  Pablo G Cavalcanti; Shahram Shirani; Jacob Scharcanski; Crystal Fong; Jane Meng; Jane Castelli; David Koff
Journal:  Quant Imaging Med Surg       Date:  2016-02

3.  Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules.

Authors:  Carole Dennie; Rebecca Thornhill; Vineeta Sethi-Virmani; Carolina A Souza; Hamid Bayanati; Ashish Gupta; Donna Maziak
Journal:  Quant Imaging Med Surg       Date:  2016-02

4.  Whole-liver CT texture analysis in colorectal cancer: Does the presence of liver metastases affect the texture of the remaining liver?

Authors:  Sheng-Xiang Rao; Doenja Mj Lambregts; Roald S Schnerr; Wenzel van Ommen; Thiemo Ja van Nijnatten; Milou H Martens; Luc A Heijnen; Walter H Backes; Cornelis Verhoef; Meng-Su Zeng; Geerard L Beets; Regina Gh Beets-Tan
Journal:  United European Gastroenterol J       Date:  2014-12       Impact factor: 4.623

5.  Using Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinomas on CT.

Authors:  K Buch; A Fujita; B Li; Y Kawashima; M M Qureshi; O Sakai
Journal:  AJNR Am J Neuroradiol       Date:  2015-04-02       Impact factor: 3.825

6.  Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival.

Authors:  Francesca Ng; Balaji Ganeshan; Robert Kozarski; Kenneth A Miles; Vicky Goh
Journal:  Radiology       Date:  2012-11-14       Impact factor: 11.105

Review 7.  Esophageal Cancer: Role of Imaging in Primary Staging and Response Assessment Post Neoadjuvant Therapy.

Authors:  Yvette Griffin
Journal:  Semin Ultrasound CT MR       Date:  2016-02-16       Impact factor: 1.875

8.  Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction.

Authors:  Stephen S F Yip; Thibaud P Coroller; Nina N Sanford; Elizabeth Huynh; Harvey Mamon; Hugo J W L Aerts; Ross I Berbeco
Journal:  Phys Med Biol       Date:  2016-01-07       Impact factor: 3.609

Review 9.  Staging accuracy of endoscopic ultrasound for esophageal cancer after neoadjuvant chemotherapy: a meta-analysis and systematic review.

Authors:  F Sun; T Chen; J Han; P Ye; J Hu
Journal:  Dis Esophagus       Date:  2014-08-29       Impact factor: 3.429

10.  Staging investigations for oesophageal cancer: a meta-analysis.

Authors:  E P M van Vliet; M H Heijenbrok-Kal; M G M Hunink; E J Kuipers; P D Siersema
Journal:  Br J Cancer       Date:  2008-01-22       Impact factor: 7.640

View more
  10 in total

1.  Radiomics approach for preoperative identification of stages I-II and III-IV of esophageal cancer.

Authors:  Lei Wu; Cong Wang; Xianzheng Tan; Zixuan Cheng; Ke Zhao; Lifen Yan; Yanli Liang; Zaiyi Liu; Changhong Liang
Journal:  Chin J Cancer Res       Date:  2018-08       Impact factor: 5.087

2.  CT texture analysis of tonsil cancer: Discrimination from normal palatine tonsils.

Authors:  Tae-Yoon Kim; Ji Young Lee; Young-Jun Lee; Dong Woo Park; Kyung Tae; Yun Young Choi
Journal:  PLoS One       Date:  2021-08-11       Impact factor: 3.240

3.  CT Texture Analysis-Correlations With Histopathology Parameters in Head and Neck Squamous Cell Carcinomas.

Authors:  Hans-Jonas Meyer; Gordian Hamerla; Anne Kathrin Höhn; Alexey Surov
Journal:  Front Oncol       Date:  2019-05-28       Impact factor: 6.244

4.  Analysis of KRAS Mutation Status Prediction Model for Colorectal Cancer Based on Medical Imaging.

Authors:  Zhen Ren; Jin Che; Xiao Wei Wu; Jun Xia
Journal:  Comput Math Methods Med       Date:  2021-12-22       Impact factor: 2.238

5.  Cervical Cancer Imaging Features Associated With ADRB1 as a Risk Factor for Cerebral Neurovascular Metastases.

Authors:  Xingju Zheng; Shilin Xu; JiaYing Wu
Journal:  Front Neurol       Date:  2022-07-12       Impact factor: 4.086

6.  Radiomics analysis for differentiating of cervical lymphadenopathy between cancer of unknown primary and malignant lymphoma on unenhanced computed tomography.

Authors:  Hayato Tomita; Tsuneo Yamashiro; Gyo Iida; Maho Tsubakimoto; Hidefumi Mimura; Sadayuki Murayama
Journal:  Nagoya J Med Sci       Date:  2022-05       Impact factor: 0.794

7.  A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning.

Authors:  Daisuke Kawahara; Yuji Murakami; Shigeyuki Tani; Yasushi Nagata
Journal:  Br J Radiol       Date:  2021-07-08       Impact factor: 3.629

8.  Combined CT texture analysis and nodal axial ratio for detection of nodal metastasis in esophageal cancer.

Authors:  Han Na Lee; Jung Im Kim; So Youn Shin; Dae Hyun Kim; Chanwoo Kim; Il Ki Hong
Journal:  Br J Radiol       Date:  2020-04-15       Impact factor: 3.629

9.  Radiomics Analysis of Multiparametric MRI for Prediction of Synchronous Lung Metastases in Osteosarcoma.

Authors:  Zhendong Luo; Jing Li; YuTing Liao; RengYi Liu; Xinping Shen; Weiguo Chen
Journal:  Front Oncol       Date:  2022-02-22       Impact factor: 6.244

10.  Heterogeneity of T3 stage esophageal squamous cell carcinoma in different parts based on enhanced CT radiomics.

Authors:  Xiao-Feng Li; Qiang Wang; Shao-Feng Duan; Biao Yao; Cai-Yun Liu
Journal:  Medicine (Baltimore)       Date:  2020-08-07       Impact factor: 1.817

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.