Literature DB >> 30072293

Potential Complementary Value of Noncontrast and Contrast Enhanced CT Radiomics in Colorectal Cancers.

Bogdan Badic1, Marie Charlotte Desseroit2, Mathieu Hatt2, Dimitris Visvikis2.   

Abstract

RATIONALE AND
OBJECTIVES: The aim of our study was to assess the relationships between textural features extracted from contrast enhanced (CE) and noncontrast enhanced (NCE) computed tomography (CT) images of primary colorectal cancer, in order to identify radiomics features more likely to provide potential complementary information regarding outcome.
MATERIALS AND METHODS: Sixty-one patients with primary colorectal cancer underwent both CE-CT and NCE-CT scans within the same acquisition. First-order and textural features (with three different methods for grey-level discretization) were extracted from the tumor volume in both modalities and their correlation was assessed with Spearman's rank correlation (rs). Significance was assessed at p < 0.05 with correction for multiple comparisons. Kaplan-Meier estimation and log-rank tests were used to identify features associated with long term patient survival.
RESULTS: Moderate positive correlations were observed between CE-CT and NCE-CT histogram-derived entropy (EntropyHist) and area under the curve (CHAUC) (rs = 0.49, p < 0.001 and rs= 0.45, p < 0.001, respectively). Some second and third order textural features were found highly correlated between CE-CT and NCE-CT, such as small zone-size emphasis SZSE (rs = 0.729, p < 0.001) and zone-size percentage (rs = 0.770, p < 0.001). Grey-levels discretization methods influenced these correlations. A few of the third order NCE-CT and CE-CT features were significantly associated with survival.
CONCLUSION: Some radiomics features with moderate correlations between nonenhanced and enhanced CT images were found to be associated with survival, thus suggesting that complementary prognostic value may be extracted from both modalities when available.
Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Colorectal cancer; Computed tomography; Radiomics; Tumor heterogeneity

Mesh:

Year:  2018        PMID: 30072293     DOI: 10.1016/j.acra.2018.06.004

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  14 in total

Review 1.  Radiomics: an Introductory Guide to What It May Foretell.

Authors:  Stephanie Nougaret; Hichem Tibermacine; Marion Tardieu; Evis Sala
Journal:  Curr Oncol Rep       Date:  2019-06-25       Impact factor: 5.075

2.  Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I-III colorectal cancer.

Authors:  Yanqi Huang; Lan He; Zhenhui Li; Xin Chen; Chu Han; Ke Zhao; Yuan Zhang; Jinrong Qu; Yun Mao; Changhong Liang; Zaiyi Liu
Journal:  Chin J Cancer Res       Date:  2022-02-28       Impact factor: 5.087

3.  Predicting Kirsten Rat Sarcoma Virus Gene Mutation Status in Patients With Colorectal Cancer by Radiomics Models Based on Multiphasic CT.

Authors:  Jianfeng Hu; Xiaoying Xia; Peng Wang; Yu Peng; Jieqiong Liu; Xiaobin Xie; Yuting Liao; Qi Wan; Xinchun Li
Journal:  Front Oncol       Date:  2022-06-24       Impact factor: 5.738

Review 4.  Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review.

Authors:  Natally Horvat; David D B Bates; Iva Petkovska
Journal:  Abdom Radiol (NY)       Date:  2019-11

5.  Radiogenomics-based cancer prognosis in colorectal cancer.

Authors:  Bogdan Badic; Mathieu Hatt; Stephanie Durand; Catherine Le Jossic-Corcos; Brigitte Simon; Dimitris Visvikis; Laurent Corcos
Journal:  Sci Rep       Date:  2019-07-05       Impact factor: 4.379

6.  Three-Dimensional CT Texture Analysis to Differentiate Colorectal Signet-Ring Cell Carcinoma and Adenocarcinoma.

Authors:  Yali Yue; Feixiang Hu; Tingdan Hu; Yiqun Sun; Tong Tong; Yajia Gu
Journal:  Cancer Manag Res       Date:  2019-12-13       Impact factor: 3.989

7.  A Combined-Radiomics Approach of CT Images to Predict Response to Anti-PD-1 Immunotherapy in NSCLC: A Retrospective Multicenter Study.

Authors:  Minghao Wu; Yanyan Zhang; Jianing Zhang; Yuwei Zhang; Yina Wang; Feng Chen; Yahong Luo; Shuai He; Yulin Liu; Qian Yang; Yanying Li; Hong Wei; Hong Zhang; Nian Lu; Sicong Wang; Yan Guo; Zhaoxiang Ye; Ying Liu
Journal:  Front Oncol       Date:  2022-01-10       Impact factor: 6.244

8.  Discovery of stable and prognostic CT-based radiomic features independent of contrast administration and dimensionality in oesophageal cancer.

Authors:  Concetta Piazzese; Kieran Foley; Philip Whybra; Chris Hurt; Tom Crosby; Emiliano Spezi
Journal:  PLoS One       Date:  2019-11-22       Impact factor: 3.240

9.  Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment.

Authors:  Francesco Fiz; Guido Costa; Nicolò Gennaro; Ludovico la Bella; Alexandra Boichuk; Martina Sollini; Letterio S Politi; Luca Balzarini; Guido Torzilli; Arturo Chiti; Luca Viganò
Journal:  Diagnostics (Basel)       Date:  2021-06-25

10.  Prediction of post transarterial chemoembolization MR images of hepatocellular carcinoma using spatio-temporal graph convolutional networks.

Authors:  Andrei Svecic; Rihab Mansour; An Tang; Samuel Kadoury
Journal:  PLoS One       Date:  2021-12-07       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.