Literature DB >> 29685529

CT texture analysis in colorectal liver metastases and the surrounding liver parenchyma and its potential as an imaging biomarker of disease aggressiveness, response and survival.

R C J Beckers1, S Trebeschi2, M Maas3, R S Schnerr4, J M L Sijmons5, G L Beets6, J B Houwers4, R G H Beets-Tan2, D M J Lambregts7.   

Abstract

OBJECTIVES: To study the ratio between the CT texture of colorectal liver metastases (CRLM) and the surrounding liver parenchyma and assess the potential of various texture measures and ratios as predictive/prognostic imaging markers. MATERIALS: Seventy patients with colorectal cancer and synchronous CRLM were included. All visible metastases, as well as the whole-volume of the surrounding liver, were separately delineated on the portal venous phase primary staging CT. Texture features entropy (E) and uniformity (U) were extracted and ratios between the texture features (T) of the metastases and background liver (Tmetastases/Tliver) calculated. Texture features were compared with clinical outcome parameters: [1] extent of disease (i.e. number of metastases), [2] response to chemotherapy (in 56/70 patients who underwent chemotherapy and CT for response evaluation), and [3] overall survival.
RESULTS: The Emetastases/Eliver ratio was lower in patients with limited disease (P = 0.02) and associated with overall survival, albeit not statistically significant when tested in multivariable analyses (HR 1.90; P = 0.07); Umetastases/Uliver was higher in patients with limited disease (P = 0.02). Emetastases showed a trend towards a higher value in patients that responded well to chemotherapy (P = 0.08).
CONCLUSION: The ratio between the texture of liver metastases and the surrounding liver appears to reflect relevant changes in tissue microarchitecture and may be of value to assess the extent of disease and help predict overall survival.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Colorectal neoplasms; Liver; Neoplasm metastasis; Radiographic image enhancement; Spiral computed; Texture analysis; Tomography

Mesh:

Substances:

Year:  2018        PMID: 29685529     DOI: 10.1016/j.ejrad.2018.02.031

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  18 in total

1.  Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging.

Authors:  Hubert Beaumont; Antoine Iannessi; Anne-Sophie Bertrand; Jean Michel Cucchi; Olivier Lucidarme
Journal:  Eur Radiol       Date:  2021-01-18       Impact factor: 5.315

2.  Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases.

Authors:  Vincenza Granata; Roberta Fusco; Federica De Muzio; Carmen Cutolo; Sergio Venanzio Setola; Roberta Grassi; Francesca Grassi; Alessandro Ottaiano; Guglielmo Nasti; Fabiana Tatangelo; Vincenzo Pilone; Vittorio Miele; Maria Chiara Brunese; Francesco Izzo; Antonella Petrillo
Journal:  Radiol Med       Date:  2022-03-26       Impact factor: 3.469

Review 3.  Current state of the art imaging approaches for colorectal liver metastasis.

Authors:  Bita Hazhirkarzar; Pegah Khoshpouri; Mohammadreza Shaghaghi; Mounes Aliyari Ghasabeh; Timothy M Pawlik; Ihab R Kamel
Journal:  Hepatobiliary Surg Nutr       Date:  2020-02       Impact factor: 7.293

Review 4.  Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.

Authors:  Nina J Wesdorp; Tessa Hellingman; Elise P Jansma; Jan-Hein T M van Waesberghe; Ronald Boellaard; Cornelis J A Punt; Joost Huiskens; Geert Kazemier
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-12-16       Impact factor: 9.236

5.  Magnetic Resonance Imaging-Based Radiomics Features Associated with Depth of Invasion Predicted Lymph Node Metastasis and Prognosis in Tongue Cancer.

Authors:  Fei Wang; Rukeng Tan; Kun Feng; Jing Hu; Zehang Zhuang; Cheng Wang; Jinsong Hou; Xiqiang Liu
Journal:  J Magn Reson Imaging       Date:  2021-12-10       Impact factor: 5.119

6.  Impact of inter-reader contouring variability on textural radiomics of colorectal liver metastases.

Authors:  Francesco Rizzetto; Francesca Calderoni; Cristina De Mattia; Arianna Defeudis; Valentina Giannini; Simone Mazzetti; Lorenzo Vassallo; Silvia Ghezzi; Andrea Sartore-Bianchi; Silvia Marsoni; Salvatore Siena; Daniele Regge; Alberto Torresin; Angelo Vanzulli
Journal:  Eur Radiol Exp       Date:  2020-11-10

7.  Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases.

Authors:  Elisabeth Sartoretti; Thomas Sartoretti; Michael Wyss; Carolin Reischauer; Luuk van Smoorenburg; Christoph A Binkert; Sabine Sartoretti-Schefer; Manoj Mannil
Journal:  Sci Rep       Date:  2021-03-09       Impact factor: 4.379

8.  Can the computed tomography texture analysis of colorectal liver metastases predict the response to first-line cytotoxic chemotherapy?

Authors:  Etienne Rabe; Dania Cioni; Laura Baglietto; Marco Fornili; Michela Gabelloni; Emanuele Neri
Journal:  World J Hepatol       Date:  2022-01-27

Review 9.  Radiomics in liver diseases: Current progress and future opportunities.

Authors:  Jingwei Wei; Hanyu Jiang; Dongsheng Gu; Meng Niu; Fangfang Fu; Yuqi Han; Bin Song; Jie Tian
Journal:  Liver Int       Date:  2020-07-02       Impact factor: 5.828

10.  Assessment of Response to Chemotherapy in Pancreatic Cancer with Liver Metastasis: CT Texture as a Predictive Biomarker.

Authors:  Sihang Cheng; Zhengyu Jin; Huadan Xue
Journal:  Diagnostics (Basel)       Date:  2021-12-01
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