Literature DB >> 18780825

Deconvolution-based dynamic contrast-enhanced MR imaging of breast tumors: correlation of tumor blood flow with human epidermal growth factor receptor 2 status and clinicopathologic findings--preliminary results.

Smitha Makkat1, Robert Luypaert, Tadeusz Stadnik, Claire Bourgain, Steven Sourbron, Martine Dujardin, Jacques De Greve, Johan De Mey.   

Abstract

PURPOSE: To prospectively determine whether breast carcinomas possess characteristic values of tumor blood flow (TBF) that correlate with pathologic and molecular prognostic markers.
MATERIALS AND METHODS: The institutional ethics committee approved this study. After informed consent was obtained, 57 women (age range, 31-80 years) with histologically proved breast cancer underwent routine magnetic resonance (MR) mammography, which included a whole-breast dynamic contrast material-enhanced (DCE) sequence. A second contrast material bolus was injected during dynamic single-section turbo field-echo imaging of the section where the lesion was maximally enhanced. The relative signal intensity changes were deconvolved in a pixelwise fashion to yield the TBF. Formalin-fixed paraffin-embedded tumor specimens on slides were evaluated for histologic size and grade, as well as for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) protein. In patients with a HER2 protein score of 2+ or 3+, HER2 gene status was assessed. For all prognostic parameters, the Mann-Whitney U test was used to compare median TBF in the HER2-positive group with that in the HER2-negative group.
RESULTS: Significantly higher TBF was observed in tumors larger than 2 cm in diameter and in PR-negative and HER2 gene-amplified tumors (P < .05). In the HER2-positive and HER2-negative groups, ER-positive PR-positive tumors had a lower median TBF than did ER-negative PR-negative tumors, and the difference was significant in the HER2-positive group (P < .05).
CONCLUSION: Pixelwise deconvolution analysis of DCE MR data in patients with breast cancer can provide preoperative information regarding TBF. These results also support the hypothesis that there is increased TBF in HER2-positive tumors. (c) RSNA, 2008.

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Year:  2008        PMID: 18780825     DOI: 10.1148/radiol.2492071147

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


  14 in total

1.  [Importance of mammography, sonography and MRI for surveillance of neoadjuvant chemotherapy for locally advanced breast cancer].

Authors:  T Schlossbauer; M Reiser; K Hellerhoff
Journal:  Radiologe       Date:  2010-11       Impact factor: 0.635

2.  Role of DCE-MR in predicting breast cancer subtypes.

Authors:  Marco Macchini; Martina Ponziani; Andrea Prochowski Iamurri; Mirco Pistelli; Mariagrazia De Lisa; Rossana Berardi; Gian Marco Giuseppetti
Journal:  Radiol Med       Date:  2018-06-05       Impact factor: 3.469

3.  Correlation of the apparent diffusion coefficiency values on diffusion-weighted imaging with prognostic factors for breast cancer.

Authors:  S Y Choi; Y-W Chang; H J Park; H J Kim; S S Hong; D Y Seo
Journal:  Br J Radiol       Date:  2011-11-29       Impact factor: 3.039

4.  Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.

Authors:  Elizabeth S Burnside; Karen Drukker; Hui Li; Ermelinda Bonaccio; Margarita Zuley; Marie Ganott; Jose M Net; Elizabeth J Sutton; Kathleen R Brandt; Gary J Whitman; Suzanne D Conzen; Li Lan; Yuan Ji; Yitan Zhu; Carl C Jaffe; Erich P Huang; John B Freymann; Justin S Kirby; Elizabeth A Morris; Maryellen L Giger
Journal:  Cancer       Date:  2015-11-30       Impact factor: 6.860

5.  CT imaging correlates of genomic expression for oral cavity squamous cell carcinoma.

Authors:  C R Pickering; K Shah; S Ahmed; A Rao; M J Frederick; J Zhang; A K Unruh; J Wang; L E Ginsberg; A J Kumar; J N Myers; J D Hamilton
Journal:  AJNR Am J Neuroradiol       Date:  2013-06-13       Impact factor: 3.825

6.  Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study.

Authors:  Shannon C Agner; Mark A Rosen; Sarah Englander; John E Tomaszewski; Michael D Feldman; Paul Zhang; Carolyn Mies; Mitchell D Schnall; Anant Madabhushi
Journal:  Radiology       Date:  2014-03-10       Impact factor: 11.105

7.  Potential of Diffusion-Weighted Imaging in the Characterization of Malignant, Benign, and Healthy Breast Tissues and Molecular Subtypes of Breast Cancer.

Authors:  Uma Sharma; Rani G Sah; Khushbu Agarwal; Rajinder Parshad; Vurthaluru Seenu; Sandeep R Mathur; Smriti Hari; Naranamangalam R Jagannathan
Journal:  Front Oncol       Date:  2016-05-23       Impact factor: 6.244

8.  Diffusion-weighted imaging and FDG PET/CT: predicting the prognoses with apparent diffusion coefficient values and maximum standardized uptake values in patients with invasive ductal carcinoma.

Authors:  Bo Bae Choi; Sung Hun Kim; Bong Joo Kang; Ji Hye Lee; Byung Joo Song; Seung Hee Jeong; Hyeon Woo Yim
Journal:  World J Surg Oncol       Date:  2012-06-28       Impact factor: 2.754

9.  Relationships Between Human-Extracted MRI Tumor Phenotypes of Breast Cancer and Clinical Prognostic Indicators Including Receptor Status and Molecular Subtype.

Authors:  Jose M Net; Gary J Whitman; Elizabteh Morris; Kathleen R Brandt; Elizabeth S Burnside; Maryellen L Giger; Marie Ganott; Elizabeth J Sutton; Margarita L Zuley; Arvind Rao
Journal:  Curr Probl Diagn Radiol       Date:  2018-08-23

10.  Optically measured microvascular blood flow contrast of malignant breast tumors.

Authors:  Regine Choe; Mary E Putt; Peter M Carlile; Turgut Durduran; Joseph M Giammarco; David R Busch; Ki Won Jung; Brian J Czerniecki; Julia Tchou; Michael D Feldman; Carolyn Mies; Mark A Rosen; Mitchell D Schnall; Angela DeMichele; Arjun G Yodh
Journal:  PLoS One       Date:  2014-06-26       Impact factor: 3.240

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