Literature DB >> 28948442

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

Rubina Manuela Trimboli1, Marina Codari2, Katia Khouri Chalouhi3, Ileana Ioan3, Giovanna Lo Bue3, Arianna Ottini4, Daniela Casolino5, Luca Alessandro Carbonaro2, Francesco Sardanelli2,6.   

Abstract

PURPOSE: To investigate the correlation between enhancement parameters on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and pathologic prognostic factors in invasive breast cancers (BCs).
MATERIALS AND METHODS: A total of 25 invasive BCs were included: 22 invasive ductal, 2 invasive lobular and 1 invasive mucinous. The tumor volume was segmented using a semi-automatic software (Olea Sphere). The following voxel-wise enhancement parameters were extracted: (1) time to peak enhancement; (2) signal intensity at peak (SIP); (3) peak enhancement percentage (PEP); (4) post-initial enhancement percentage (PIEP). The following pathological prognostic factors were considered for potential correlation: tumor (pT) and nodal (pN) stage, grading, perivascular/perineural invasion, estrogen/progesterone receptor status, Ki-67 proliferation, and HER2 expression. Spearman and Pearson correlation coefficients were calculated according with type of variable and data distribution.
RESULTS: Tumor volume was 2.8 ± 2.0 cm3 (mean ± standard deviation [SD]). Mean SIP correlated with pT (ρ = 0.424, p = 0.035); mean PEP correlated with HER2 overexpression (ϕ = 0.471, p = 0.017) and pT (ρ = 0.449, p = 0.024). The percentage of voxels with fast PEP directly correlated with pT (ρ = 0.482, p = 0.015) and pN (ρ = 0.446, p = 0.026), while the percentage of voxels with slow PEP inversely correlated with pT (ρ = -0.421, p = 0.039) and pN (ρ = -0.481, p = 0.015). Segmentation time was 14.6 ± 1.3 min (mean ± SD).
CONCLUSION: In invasive BCs, DCE-MRI voxel-wise enhancement parameters correlated with HER2, pT, and pN.

Entities:  

Keywords:  Dynamic contrast-enhanced (DCE) MRI; Invasive breast cancers; Prognostic factors; Volume enhancement parameters

Mesh:

Substances:

Year:  2017        PMID: 28948442     DOI: 10.1007/s11547-017-0809-8

Source DB:  PubMed          Journal:  Radiol Med        ISSN: 0033-8362            Impact factor:   3.469


  22 in total

Review 1.  Basal-like and triple-negative breast cancers: a critical review with an emphasis on the implications for pathologists and oncologists.

Authors:  Sunil Badve; David J Dabbs; Stuart J Schnitt; Frederick L Baehner; Thomas Decker; Vincenzo Eusebi; Stephen B Fox; Shu Ichihara; Jocelyne Jacquemier; Sunil R Lakhani; José Palacios; Emad A Rakha; Andrea L Richardson; Fernando C Schmitt; Puay-Hoon Tan; Gary M Tse; Britta Weigelt; Ian O Ellis; Jorge S Reis-Filho
Journal:  Mod Pathol       Date:  2010-11-12       Impact factor: 7.842

2.  Accuracy of combined dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted imaging for breast cancer detection: a meta-analysis.

Authors:  Li Zhang; Min Tang; Zhiqian Min; Jun Lu; Xiaoyan Lei; Xiaoling Zhang
Journal:  Acta Radiol       Date:  2015-08-13       Impact factor: 1.990

Review 3.  Prognostic value of vascular endothelial growth factor in breast cancer.

Authors:  G Gasparini
Journal:  Oncologist       Date:  2000

Review 4.  Tumor angiogenesis: therapeutic implications.

Authors:  J Folkman
Journal:  N Engl J Med       Date:  1971-11-18       Impact factor: 91.245

Review 5.  Predictive, personalized, preventive, participatory (P4) cancer medicine.

Authors:  Leroy Hood; Stephen H Friend
Journal:  Nat Rev Clin Oncol       Date:  2011-03       Impact factor: 66.675

6.  CD133+ cells with cancer stem cell characteristics associates with vasculogenic mimicry in triple-negative breast cancer.

Authors:  T J Liu; B C Sun; X L Zhao; X M Zhao; T Sun; Q Gu; Z Yao; X Y Dong; N Zhao; N Liu
Journal:  Oncogene       Date:  2012-04-02       Impact factor: 9.867

7.  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?

Authors:  Boram Yi; Doo Kyoung Kang; Dukyong Yoon; Yong Sik Jung; Ku Sang Kim; Hyunee Yim; Tae Hee Kim
Journal:  Eur Radiol       Date:  2014-02-21       Impact factor: 5.315

8.  Enhancement parameters on dynamic contrast enhanced breast MRI: do they correlate with prognostic factors and subtypes of breast cancers?

Authors:  Ji Youn Kim; Sung Hun Kim; Yun Ju Kim; Bong Joo Kang; Yeong Yi An; A Won Lee; Byung Joo Song; Yong Soo Park; Han Bi Lee
Journal:  Magn Reson Imaging       Date:  2014-08-29       Impact factor: 2.546

9.  Prediction of low-risk breast cancer using perfusion parameters and apparent diffusion coefficient.

Authors:  Hee Jung Shin; Hak Hee Kim; Ki Chang Shin; Yoo Sub Sung; Joo Hee Cha; Jong Won Lee; Byung Ho Son; Sei Hyun Ahn
Journal:  Magn Reson Imaging       Date:  2015-10-30       Impact factor: 2.546

10.  Can contrast-enhanced MR imaging predict survival in breast cancer?

Authors:  B Boné; B K Szabó; L G Perbeck; B Veress; P Aspelin
Journal:  Acta Radiol       Date:  2003-07       Impact factor: 1.701

View more
  5 in total

1.  Preoperative loco-regional staging of invasive lobular carcinoma with contrast-enhanced digital mammography (CEDM).

Authors:  Francesco Amato; Giulia Bicchierai; Donatello Cirone; Catherine Depretto; Federica Di Naro; Ermanno Vanzi; Gianfranco Scaperrotta; Tommaso Vincenzo Bartolotta; Vittorio Miele; Jacopo Nori
Journal:  Radiol Med       Date:  2019-11-26       Impact factor: 3.469

2.  Use of MRI for Personalized Treatment of More Aggressive Tumors.

Authors:  Riham H El Khouli; Michael A Jacobs
Journal:  Radiology       Date:  2020-03-31       Impact factor: 11.105

Review 3.  Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review.

Authors:  Toshiki Kazama; Taro Takahara; Jun Hashimoto
Journal:  Life (Basel)       Date:  2022-03-28

4.  Blood Oxygenation Level Dependent Magnetic Resonance Imaging (MRI), Dynamic Contrast Enhanced MRI, and Diffusion Weighted MRI for Benign and Malignant Breast Cancer Discrimination: A Preliminary Experience.

Authors:  Roberta Fusco; Vincenza Granata; Mauro Mattace Raso; Paolo Vallone; Alessandro Pasquale De Rosa; Claudio Siani; Maurizio Di Bonito; Antonella Petrillo; Mario Sansone
Journal:  Cancers (Basel)       Date:  2021-05-17       Impact factor: 6.639

5.  Relationship between histogram metrics of pharmacokinetic parameters of DCE-MRI and histological phenotype in breast cancer.

Authors:  Guocai Yang; Jing Yang; Hui Xu; Qingxin Zhang; Yonghong Qi; Aiju Zhang
Journal:  Transl Cancer Res       Date:  2020-01       Impact factor: 1.241

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.