Literature DB >> 25875904

Evaluation of the treatment response to neoadjuvant chemotherapy in locally advanced breast cancer using combined magnetic resonance vascular maps and apparent diffusion coefficient.

Li-An Wu1,2, Ruey-Feng Chang3, Chiun-Sheng Huang4, Yen-Shen Lu5, Hong-Hao Chen3, Jo-Yu Chen1, Yeun-Chung Chang1.   

Abstract

PURPOSE: To evaluate the treatment response of locally advanced breast cancer (LABC) to neoadjuvant chemotherapy using magnetic resonance (MR) vascular maps and apparent diffusion coefficient (ADC) at 3T. Materials and Methods Thirty-one patients with LABC who underwent breast MR studies before, after the first course, and after completing neoadjuvant chemotherapy were enrolled. Vascular morphology was retrieved via Hessian matrix and the voxels of the vessels and volume of vessels were measured automatically. Whole tumor mean ADC values were calculated. Clinical responders were defined as >50% tumor reduction in the final MR studies. Pathologically complete responders were also recorded.
RESULTS: There were 21 clinical responders and 10 nonresponders. Compared to the nonresponders after the first course, the responders were characterized by more vascular reduction of the breast lesion and decreased bilateral vascular discrepancy (voxels and volume), and increments in the ADC value and ADC percentage of the lesions (all P < 0.05). There were three pathological complete responders who showed more apparent early vascular reduction of the lesion breast (voxels and volume) and increments in the ADC value than others (P = 0.02, 0.01 and 0.02, respectively).
CONCLUSION: The early changes of MR vascular maps and ADC are associated with the final treatment response of LABC.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  automatic vascular mapping; diffusion-weighted image; early response monitoring; locally advanced breast cancer; magnetic resonance vascular maps; neoadjuvant chemotherapy

Mesh:

Substances:

Year:  2015        PMID: 25875904     DOI: 10.1002/jmri.24915

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  5 in total

1.  Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.

Authors:  Amirhessam Tahmassebi; Georg J Wengert; Thomas H Helbich; Zsuzsanna Bago-Horvath; Sousan Alaei; Rupert Bartsch; Peter Dubsky; Pascal Baltzer; Paola Clauser; Panagiotis Kapetas; Elizabeth A Morris; Anke Meyer-Baese; Katja Pinker
Journal:  Invest Radiol       Date:  2019-02       Impact factor: 6.016

2.  Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis.

Authors:  John Virostko; Allison Hainline; Hakmook Kang; Lori R Arlinghaus; Richard G Abramson; Stephanie L Barnes; Jeffrey D Blume; Sarah Avery; Debra Patt; Boone Goodgame; Thomas E Yankeelov; Anna G Sorace
Journal:  J Med Imaging (Bellingham)       Date:  2017-11-24

3.  Magnetization Transfer MRI of Breast Cancer in the Community Setting: Reproducibility and Preliminary Results in Neoadjuvant Therapy.

Authors:  John Virostko; Anna G Sorace; Chengyue Wu; David Ekrut; Angela M Jarrett; Raghave M Upadhyaya; Sarah Avery; Debra Patt; Boone Goodgame; Thomas E Yankeelov
Journal:  Tomography       Date:  2019-03

4.  Contrast-enhanced spectral mammography in neoadjuvant chemotherapy monitoring: a comparison with breast magnetic resonance imaging.

Authors:  Valentina Iotti; Sara Ravaioli; Rita Vacondio; Chiara Coriani; Sabrina Caffarri; Roberto Sghedoni; Andrea Nitrosi; Moira Ragazzi; Elisa Gasparini; Cristina Masini; Giancarlo Bisagni; Giuseppe Falco; Guglielmo Ferrari; Luca Braglia; Alberto Del Prato; Ivana Malavolti; Vladimiro Ginocchi; Pierpaolo Pattacini
Journal:  Breast Cancer Res       Date:  2017-09-11       Impact factor: 6.466

Review 5.  Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.

Authors:  Roberto Lo Gullo; Sarah Eskreis-Winkler; Elizabeth A Morris; Katja Pinker
Journal:  Breast       Date:  2019-11-23       Impact factor: 4.380

  5 in total

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