Literature DB >> 22568630

Computer assisted analysis of MR-mammography reveals association between contrast enhancement and occurrence of distant metastasis.

Pascal A T Baltzer1, Ramy Zoubi, Hartmut P Burmeister, Mieczyslaw Gajda, Oumar Camara, Werner A Kaiser, Matthias Dietzel.   

Abstract

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is able to detect breast cancer with high sensitivity. Furthermore, this method provides functional information on tissue composition and vascularization. This study aims to identify the potential of DCE-MRI to predict distant metastasis in breast cancer patients using computer assisted interpretation of dynamic enhancement data. For this purpose, 59 consecutive patients with newly diagnosed invasive breast cancer received pretherapeutic DCE-MRI at 1.5 Tesla according to international recommendations. In all patients, follow up interval and occurrence of distant metastasis was documented. For DCE-MRI analysis dedicated software was used (Brevis, Siemens Healthcare, Erlangen, Germany). It allows semiautomatic identification of the most suspect curve in a lesion analyzed. Enhancement parameters assessed were "Initial Enhancement", "Washout", "Peak-Enhancement", and "Time to Peak Enhancement". Cox proportional hazards regression (CPHR) was used to analyze the effect of these parameters on the probability of metachronous distant metastasis. Median follow up period was 52.0 months. 6 patients developed distant metastases between 11 and 35 months after breast cancer diagnosis. In CPHR, Washout could be identified as significant and independent predictor for occurrence of distant metastasis (P = 0.0134). Our initial data demonstrate an association between computer measured enhancement parameters in DCE-MRI and occurrence of distant metastasis by quantification of Washout.

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Year:  2012        PMID: 22568630     DOI: 10.7785/tcrt.2012.500266

Source DB:  PubMed          Journal:  Technol Cancer Res Treat        ISSN: 1533-0338


  7 in total

1.  Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy.

Authors:  Matthias Dietzel; Clemens Kaiser; Katja Pinker; Evelyn Wenkel; Matthias Hammon; Michael Uder; Barbara Bennani Baiti; Paola Clauser; Rüdiger Schulz-Wendtland; Pascal Baltzer
Journal:  Breast Care (Basel)       Date:  2017-08-29       Impact factor: 2.860

2.  A retrospective review of MRI features associated with metastasis-free survival in women with breast cancer: focusing on skin thickening and skin enhancement.

Authors:  Valentine Mberu; Jennifer McFarlane; E Jane Macaskill; Andrew Evans
Journal:  Br J Radiol       Date:  2021-10-05       Impact factor: 3.039

3.  Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses.

Authors:  Stephan Ellmann; Evelyn Wenkel; Matthias Dietzel; Christian Bielowski; Sulaiman Vesal; Andreas Maier; Matthias Hammon; Rolf Janka; Peter A Fasching; Matthias W Beckmann; Rüdiger Schulz Wendtland; Michael Uder; Tobias Bäuerle
Journal:  PLoS One       Date:  2020-01-30       Impact factor: 3.240

4.  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 5.  The potential of predictive and prognostic breast MRI (P2-bMRI).

Authors:  Francesco Sardanelli; Pascal A T Baltzer; Matthias Dietzel; Rubina Manuela Trimboli; Moreno Zanardo; Rüdiger Schultz-Wendtland; Michael Uder; Paola Clauser
Journal:  Eur Radiol Exp       Date:  2022-08-22

6.  Pretreatment Dynamic Contrast-Enhanced MRI Improves Prediction of Early Distant Metastases in Patients With Nasopharyngeal Carcinoma.

Authors:  Shy-Chyi Chin; Chien-Yu Lin; Bing-Shen Huang; Ngan-Ming Tsang; Kang-Hsing Fan; Yi-Kang Ku; Cheng-Lung Hsu; Sheng-Chieh Chan; Shiang-Fu Huang; Cheng-He Li; Hsiao-Jung Tseng; Chun-Ta Liao; Ho-Ling Liu; Kyunghyun Sung
Journal:  Medicine (Baltimore)       Date:  2016-02       Impact factor: 1.817

7.  Preoperative dynamic breast magnetic resonance imaging kinetic features using computer-aided diagnosis: Association with survival outcome and tumor aggressiveness in patients with invasive breast cancer.

Authors:  Sang Yu Nam; Eun Sook Ko; Yaeji Lim; Boo-Kyung Han; Eun Young Ko; Ji Soo Choi; Jeong Eon Lee
Journal:  PLoS One       Date:  2018-04-12       Impact factor: 3.240

  7 in total

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