Literature DB >> 28107084

Can Diffusion-Weighted Imaging and Related Apparent Diffusion Coefficient be a Prognostic Value in Women With Breast Cancer?

Paola Rabasco1, Rocchina Caivano2, Vittorio Simeon3, Giuseppina Dinardo1, Antonella Lotumolo1, Matilde Gioioso1, Antonio Villonio1, Giancarlo Iannelli1, Felice D'Antuono1, Alexis Zandolino1, Luca Macarini4, Giuseppe Guglielmi4,5, Aldo Cammarota1.   

Abstract

PURPOSE: To analyze diffusion-weighted imaging (DWI) and the related apparent diffusion coefficient (ADC) in women with breast cancer, correlating these values with the presence at 3 years of distant metastases, and to demonstrate that DWI-Magnetic Resonance Imaging (MRI) and related ADC values may represent a prognostic value in the study of women with breast cancer.
MATERIALS AND METHODS: Sixty women (aged 45-73 years) affected with breast cancer with a follow-up in 3 years were enrolled. On DWI, we obtained the ADC values, and these were correlated with the clinical condition of patients at 3 years. Moreover, tumour size, lymph node status, and molecular markers, including estrogens receptor, progesterone receptor, Ki-67 index, and human growth factor receptor 2 protein, were correlated with ADC values. This study was approved by the Scientific Committee of our institution.
RESULTS: We considered patients with metastasis at 3 years (12 patients - 20%) and without metastasis (48 patients - 80%). The mean ADC value in patients with no metastases at 3 years was 1.06 ± 0.38, while for patients with metastases it was 0.74 ± 0.34 (p = .011). The receiver-operator curve analysis identified a value of 0.75 (<0.75 with risk to develop metastasis) as the best predictive cutoff for ADC values, with the highest sensitivity (81.25%) and higher specificity (66.67%). After regression analysis, ADC value, positivity to estrogen-progestin receptors, and presence of lymph nodes were the only prognostic factors found to be statistically significant.
CONCLUSIONS: DWI-MRI and related ADC values may represent a prognostic value in women with breast cancer.

Entities:  

Keywords:  Breast cancer; apparent diffusion coefficient; diffusion-weighted imaging; magnetic resonance imaging; prognostic factors

Mesh:

Year:  2017        PMID: 28107084     DOI: 10.1080/07357907.2016.1267740

Source DB:  PubMed          Journal:  Cancer Invest        ISSN: 0735-7907            Impact factor:   2.176


  8 in total

1.  Is there any relationship between adc values of diffusion-weighted imaging and the histopathological prognostic factors of invasive ductal carcinoma?

Authors:  Hale Aydin; Bahar Guner; Isil Esen Bostanci; Zarife Melda Bulut; Bilgin Kadri Aribas; Lutfi Dogan; Mehmet Ali Gulcelik
Journal:  Br J Radiol       Date:  2018-01-12       Impact factor: 3.039

Review 2.  Clinical role of breast MRI now and going forward.

Authors:  D Leithner; G J Wengert; T H Helbich; S Thakur; R E Ochoa-Albiztegui; E A Morris; K Pinker
Journal:  Clin Radiol       Date:  2017-12-09       Impact factor: 2.350

3.  Apparent diffusion coefficient in estrogen receptor-positive and lymph node-negative invasive breast cancers at 3.0T DW-MRI: A potential predictor for an oncotype Dx test recurrence score.

Authors:  Sunitha B Thakur; Manuela Durando; Soledad Milans; Gene Y Cho; Lucas Gennaro; Elizabeth J Sutton; Dilip Giri; Elizabeth A Morris
Journal:  J Magn Reson Imaging       Date:  2017-06-22       Impact factor: 4.813

4.  Sequential [18F]FDG-[18F]FMISO PET and Multiparametric MRI at 3T for Insights into Breast Cancer Heterogeneity and Correlation with Patient Outcomes: First Clinical Experience.

Authors:  Piotr Andrzejewski; Georg Wengert; Thomas H Helbich; Heinrich Magometschnigg; Dietmar Georg; Marcus Hacker; Pascal Baltzer; Paola Clauser; Panagiotis Kapetas; Petra Georg; Wolfgang Wadsak; Katja Pinker
Journal:  Contrast Media Mol Imaging       Date:  2019-01-08       Impact factor: 3.161

5.  Differentiation of Benign and Malignant Breast Lesions Using ADC Values and ADC Ratio in Breast MRI.

Authors:  Silvia Tsvetkova; Katya Doykova; Anna Vasilska; Katya Sapunarova; Daniel Doykov; Vladimir Andonov; Petar Uchikov
Journal:  Diagnostics (Basel)       Date:  2022-01-27

6.  Prediction for Distant Metastasis of Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Images under Deep Learning.

Authors:  Li Li; Hongzhe Tian; Baorong Zhang; Weijun Wang; Bo Li
Journal:  Comput Intell Neurosci       Date:  2022-06-08

Review 7.  A review on methods for diagnosis of breast cancer cells and tissues.

Authors:  Ziyu He; Zhu Chen; Miduo Tan; Sauli Elingarami; Yuan Liu; Taotao Li; Yan Deng; Nongyue He; Song Li; Juan Fu; Wen Li
Journal:  Cell Prolif       Date:  2020-06-12       Impact factor: 6.831

Review 8.  Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.

Authors:  Sandy Napel; Wei Mu; Bruna V Jardim-Perassi; Hugo J W L Aerts; Robert J Gillies
Journal:  Cancer       Date:  2018-11-01       Impact factor: 6.860

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.