Literature DB >> 24553785

Is there any correlation between model-based perfusion parameters and model-free parameters of time-signal intensity curve on dynamic contrast enhanced MRI in breast cancer patients?

Boram Yi1, Doo Kyoung Kang, Dukyong Yoon, Yong Sik Jung, Ku Sang Kim, Hyunee Yim, Tae Hee Kim.   

Abstract

OBJECTIVE: To find out any correlation between dynamic contrast-enhanced (DCE) model-based parameters and model-free parameters, and evaluate correlations between perfusion parameters with histologic prognostic factors.
METHODS: Model-based parameters (Ktrans, Kep and Ve) of 102 invasive ductal carcinomas were obtained using DCE-MRI and post-processing software. Correlations between model-based and model-free parameters and between perfusion parameters and histologic prognostic factors were analysed.
RESULTS: Mean Kep was significantly higher in cancers showing initial rapid enhancement (P = 0.002) and a delayed washout pattern (P = 0.001). Ve was significantly lower in cancers showing a delayed washout pattern (P = 0.015). Kep significantly correlated with time to peak enhancement (TTP) (ρ = -0.33, P < 0.001) and washout slope (ρ = 0.39, P = 0.002). Ve was significantly correlated with TTP (ρ = 0.33, P = 0.002). Mean Kep was higher in tumours with high nuclear grade (P = 0.017). Mean Ve was lower in tumours with high histologic grade (P = 0.005) and in tumours with negative oestrogen receptor status (P = 0.047). TTP was shorter in tumours with negative oestrogen receptor status (P = 0.037).
CONCLUSIONS: We could acquire general information about the tumour vascular physiology, interstitial space volume and pathologic prognostic factors by analyzing time-signal intensity curve without a complicated acquisition process for the model-based parameters. KEY POINTS: • Kep mainly affected the initial and delayed curve pattern in time-signal intensity curve. • There is significant correlation between model-based and model-free parameters. • We acquired information about tumour vascular physiology, interstitial space volume and prognostic factors.

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Year:  2014        PMID: 24553785     DOI: 10.1007/s00330-014-3100-6

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  24 in total

1.  Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions?

Authors:  C K Kuhl; P Mielcareck; S Klaschik; C Leutner; E Wardelmann; J Gieseke; H H Schild
Journal:  Radiology       Date:  1999-04       Impact factor: 11.105

2.  Comparative study into the robustness of compartmental modeling and model-free analysis in DCE-MRI studies.

Authors:  Caleb Roberts; Basma Issa; Andrew Stone; Alan Jackson; John C Waterton; Geoffrey J M Parker
Journal:  J Magn Reson Imaging       Date:  2006-04       Impact factor: 4.813

3.  Meta-analysis of MR imaging in the diagnosis of breast lesions.

Authors:  Nicky H G M Peters; Inne H M Borel Rinkes; Nicolaas P A Zuithoff; Willem P T M Mali; Karel G M Moons; Petra H M Peeters
Journal:  Radiology       Date:  2007-11-16       Impact factor: 11.105

Review 4.  Modeling tracer kinetics in dynamic Gd-DTPA MR imaging.

Authors:  P S Tofts
Journal:  J Magn Reson Imaging       Date:  1997 Jan-Feb       Impact factor: 4.813

5.  3-T dynamic contrast-enhanced MRI of the breast: pharmacokinetic parameters versus conventional kinetic curve analysis.

Authors:  Riham H El Khouli; Katarzyna J Macura; Ihab R Kamel; Michael A Jacobs; David A Bluemke
Journal:  AJR Am J Roentgenol       Date:  2011-12       Impact factor: 3.959

6.  Computer-aided interpretation of dynamic magnetic resonance imaging reflects histopathology of invasive breast cancer.

Authors:  Pascal A T Baltzer; Tibor Vag; Matthias Dietzel; Sebastian Beger; Christian Freiberg; Mieczyslaw Gajda; Oumar Camara; Werner A Kaiser
Journal:  Eur Radiol       Date:  2010-03-04       Impact factor: 5.315

Review 7.  Imaging angiogenesis: applications and potential for drug development.

Authors:  Janet C Miller; Homer H Pien; Dushyant Sahani; A Gregory Sorensen; James H Thrall
Journal:  J Natl Cancer Inst       Date:  2005-02-02       Impact factor: 13.506

8.  Primary human breast adenocarcinoma: imaging and histologic correlates of intrinsic susceptibility-weighted MR imaging before and during chemotherapy.

Authors:  Sonia P Li; N Jane Taylor; Andreas Makris; Mei-Lin W Ah-See; Mark J Beresford; J James Stirling; James A d'Arcy; David J Collins; Anwar R Padhani
Journal:  Radiology       Date:  2010-09-21       Impact factor: 11.105

9.  Survival outcomes of breast cancer patients who receive neoadjuvant chemotherapy: association with dynamic contrast-enhanced MR imaging with computer-aided evaluation.

Authors:  Ann Yi; Nariya Cho; Seock-Ah Im; Jung Min Chang; Seung Ja Kim; Hyeung-Gon Moon; Wonshik Han; In-Ae Park; Dong-Young Noh; Woo Kyung Moon
Journal:  Radiology       Date:  2013-04-16       Impact factor: 11.105

Review 10.  Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols.

Authors:  P S Tofts; G Brix; D L Buckley; J L Evelhoch; E Henderson; M V Knopp; H B Larsson; T Y Lee; N A Mayr; G J Parker; R E Port; J Taylor; R M Weisskoff
Journal:  J Magn Reson Imaging       Date:  1999-09       Impact factor: 4.813

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  14 in total

1.  Discrimination of metastatic from non-metastatic mesorectal lymph nodes in rectal cancer using quantitative dynamic contrast-enhanced magnetic resonance imaging.

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Journal:  J Huazhong Univ Sci Technolog Med Sci       Date:  2016-07-28

2.  Correlation between voxel-wise enhancement parameters on DCE-MRI and pathological prognostic factors in invasive breast cancers.

Authors:  Rubina Manuela Trimboli; Marina Codari; Katia Khouri Chalouhi; Ileana Ioan; Giovanna Lo Bue; Arianna Ottini; Daniela Casolino; Luca Alessandro Carbonaro; Francesco Sardanelli
Journal:  Radiol Med       Date:  2017-09-25       Impact factor: 3.469

3.  Discrimination between benign and malignant breast lesions using volumetric quantitative dynamic contrast-enhanced MR imaging.

Authors:  Ziliang Cheng; Zhuo Wu; Guangzi Shi; Zhilong Yi; Mingwei Xie; Weike Zeng; Chao Song; Chushan Zheng; Jun Shen
Journal:  Eur Radiol       Date:  2017-09-19       Impact factor: 5.315

4.  A 10-miRNA risk score-based prediction model for pathological complete response to neoadjuvant chemotherapy in hormone receptor-positive breast cancer.

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Journal:  Sci China Life Sci       Date:  2022-05-13       Impact factor: 6.038

5.  Assessment of quantitative dynamic contrast-enhanced MRI in distinguishing different histologic grades of breast phyllode tumor.

Authors:  Zhilong Yi; Mingwei Xie; Guangzi Shi; Ziliang Cheng; Hong Zeng; Ningyi Jiang; Zhuo Wu
Journal:  Eur Radiol       Date:  2021-09-07       Impact factor: 7.034

6.  Whole-lesion histogram and texture analyses of breast lesions on inline quantitative DCE mapping with CAIPIRINHA-Dixon-TWIST-VIBE.

Authors:  Kun Sun; Hong Zhu; Weimin Chai; Ying Zhan; Dominik Nickel; Robert Grimm; Caixia Fu; Fuhua Yan
Journal:  Eur Radiol       Date:  2019-08-01       Impact factor: 5.315

7.  Diagnostic performance of initial enhancement analysis using ultra-fast dynamic contrast-enhanced MRI for breast lesions.

Authors:  Mariko Goto; Koji Sakai; Hajime Yokota; Maki Kiba; Mariko Yoshida; Hiroshi Imai; Elisabeth Weiland; Isao Yokota; Kei Yamada
Journal:  Eur Radiol       Date:  2018-08-07       Impact factor: 5.315

8.  Quantitative dynamic contrast-enhanced and diffusion-weighted MRI for differentiation between nasopharyngeal carcinoma and lymphoma at the primary site.

Authors:  Xiao-ping Yu; Jing Hou; Fei-ping Li; Wang Xiang; Qiang Lu; Yin Hu; Hui Wang
Journal:  Dentomaxillofac Radiol       Date:  2016-02-05       Impact factor: 2.419

9.  Relating Doses of Contrast Agent Administered to TIC and Semi-Quantitative Parameters on DCE-MRI: Based on a Murine Breast Tumor Model.

Authors:  Menglin Wu; Li Lu; Qi Zhang; Qi Guo; Feixiang Zhao; Tongwei Li; Xuening Zhang
Journal:  PLoS One       Date:  2016-02-22       Impact factor: 3.240

10.  Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI.

Authors:  Ji Young Lee; Kwang-Sig Lee; Bo Kyoung Seo; Kyu Ran Cho; Ok Hee Woo; Sung Eun Song; Eun-Kyung Kim; Hye Yoon Lee; Jung Sun Kim; Jaehyung Cha
Journal:  Eur Radiol       Date:  2021-07-05       Impact factor: 5.315

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