Literature DB >> 23297334

Primary colorectal cancer: use of kinetic modeling of dynamic contrast-enhanced CT data to predict clinical outcome.

Tong San Koh1, Quan Sing Ng, Choon Hua Thng, Jin Wei Kwek, Robert Kozarski, Vicky Goh.   

Abstract

PURPOSE: To compare four different tracer kinetic models for the analysis of dynamic contrast material-enhanced computed tomographic (CT) data with respect to the prediction of 5-year overall survival in primary colorectal cancer.
MATERIALS AND METHODS: This study was approved by the ethical review board. Archival dynamic contrast-enhanced CT data from 46 patients with colorectal cancer, obtained as part of a research study, were analyzed retrospectively by using the distributed parameter, conventional compartmental, adiabatic tissue homogeneity, and generalized kinetic models. Blood flow, blood volume, mean transit time (MTT), permeability-surface area product, extraction fraction, extravascular extracellular volume (v(e)), and volume transfer constant (K(trans)) were compared by using the Friedman test, with statistical significance at 5%. Following receiver operating characteristic analysis, parameters of the different kinetic models and tumor stage were compared with respect to overall survival discrimination, with use of Kaplan Meier analysis and a univariate Cox proportional hazard model, with additional cross-validation and permutation testing.
RESULTS: Blood flow was lower with the distributed parameter model than with the conventional compartmental and adiabatic tissue homogeneity models (P < .0001), and blood flow values determined with the conventional compartmental and adiabatic tissue homogeneity models were similar. Conversely, MTT was longer with the distributed parameter model than with the conventional compartmental and adiabatic tissue homogeneity models (P < .0001). Blood volume, permeability-surface area product, and v(e) were higher with the conventional compartmental model than with the adiabatic tissue homogeneity, distributed parameter, or generalized kinetic models (P < .0001). The extraction fraction was higher with the distributed parameter model than with the adiabatic tissue homogeneity model. With respect to 5-year overall survival, only the distributed parameter model-derived v(e) was predictive of 5-year overall survival with a threshold value of 15.48 mL/100 mL after cross-validation and permutation testing.
CONCLUSION: Parameter values differ significantly between models. Of the models investigated, the distributed parameter model was the best predictor of 5-year overall survival. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120186/-/DC1. RSNA, 2013

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Year:  2013        PMID: 23297334     DOI: 10.1148/radiol.12120186

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  13 in total

Review 1.  Imaging techniques for tumour delineation and heterogeneity quantification of lung cancer: overview of current possibilities.

Authors:  Wouter van Elmpt; Catharina M L Zegers; Marco Das; Dirk De Ruysscher
Journal:  J Thorac Dis       Date:  2014-04       Impact factor: 2.895

2.  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

3.  Use of patient outcome endpoints to identify the best functional CT imaging parameters in metastatic renal cell carcinoma patients.

Authors:  Jill Rachel Mains; Frede Donskov; Erik Morre Pedersen; Hans Henrik Torp Madsen; Jesper Thygesen; Kennet Thorup; Finn Rasmussen
Journal:  Br J Radiol       Date:  2018-01-02       Impact factor: 3.039

4.  Simultaneous Denoising of Dynamic PET Images Based on Deep Image Prior.

Authors:  Cheng-Hsun Yang; Hsuan-Ming Huang
Journal:  J Digit Imaging       Date:  2022-03-03       Impact factor: 4.903

5.  Characterization of tumor heterogeneity using dynamic contrast enhanced CT and FDG-PET in non-small cell lung cancer.

Authors:  P Veit-Haibach; D De Ruysscher; W van Elmpt; M Das; Martin Hüllner; H Sharifi; K Zegers; B Reymen; P Lambin; J E Wildberger; E G C Troost
Journal:  Radiother Oncol       Date:  2013-09-14       Impact factor: 6.280

Review 6.  The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective.

Authors:  Robert H Press; Hui-Kuo G Shu; Hyunsuk Shim; James M Mountz; Brenda F Kurland; Richard L Wahl; Ella F Jones; Nola M Hylton; Elizabeth R Gerstner; Robert J Nordstrom; Lori Henderson; Karen A Kurdziel; Bhadrasain Vikram; Michael A Jacobs; Matthias Holdhoff; Edward Taylor; David A Jaffray; Lawrence H Schwartz; David A Mankoff; Paul E Kinahan; Hannah M Linden; Philippe Lambin; Thomas J Dilling; Daniel L Rubin; Lubomir Hadjiiski; John M Buatti
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-06-30       Impact factor: 7.038

Review 7.  Perfusion CT imaging of colorectal cancer.

Authors:  V Goh; R Glynne-Jones
Journal:  Br J Radiol       Date:  2014-02       Impact factor: 3.039

Review 8.  Colorectal cancer: Parametric evaluation of morphological, functional and molecular tomographic imaging.

Authors:  Pier Paolo Mainenti; Arnaldo Stanzione; Salvatore Guarino; Valeria Romeo; Lorenzo Ugga; Federica Romano; Giovanni Storto; Simone Maurea; Arturo Brunetti
Journal:  World J Gastroenterol       Date:  2019-09-21       Impact factor: 5.742

Review 9.  Incorporating prognostic imaging biomarkers into clinical practice.

Authors:  W Phillip Law; Kenneth A Miles
Journal:  Cancer Imaging       Date:  2013-09-23       Impact factor: 3.909

10.  Variability and Reproducibility of 3rd-generation dual-source dynamic volume perfusion CT Parameters in Comparison to MR-perfusion Parameters in Rectal Cancer.

Authors:  Sonja Sudarski; Thomas Henzler; Teresa Floss; Tanja Gaa; Mathias Meyer; Holger Haubenreisser; Stefan O Schoenberg; Ulrike I Attenberger
Journal:  Sci Rep       Date:  2018-05-02       Impact factor: 4.379

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