Literature DB >> 28890184

Evaluation of the Tumor Response After Neoadjuvant Chemotherapy in Breast Cancer Patients: Correlation Between Dynamic Contrast-enhanced Magnetic Resonance Imaging and Pathologic Tumor Cellularity.

Woo Jung Choi1, Won Kyung Kim2, Hee Jung Shin1, Joo Hee Cha1, Eun Young Chae1, Hak Hee Kim3.   

Abstract

BACKGROUND: We evaluated the tumor response after neoadjuvant chemotherapy (NAC) in breast cancer patients using dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging parameters assessed using a commercially available computer-aided system. We also analyzed their correlation with pathologic tumor cellularity.
MATERIALS AND METHODS: We retrospectively reviewed the data from 130 patients with breast cancer who had undergone NAC followed by surgery from January to October 2013. Maximum diameter, volume, peak enhancement, and persistent, plateau, and washout-enhancing components were measured using a computer-aided system on DCE MR images and correlated with the Miller-Payne grading system. Patients with a Miller-Payne grade of 5 were classified into the pathologic complete response (pCR) group. Patients with grades 1, 2, 3, and 4 were included in the non-pCR group. Diagnostic performance was evaluated using receiver operating characteristic curve analysis.
RESULTS: Twenty patients were included in the pCR group and 110 patients in the non-pCR group. Of the 6 parameters, the rate of tumor volume reduction (r = 0.729, P < .001) showed the strongest correlation with the Miller-Payne grading system, followed by the maximum diameter (r = 0.706, P < .001) and washout component (r = 0.606, P < .001). The area under the receiver operating characteristic curve (Az value) was the largest for the rate of volume reduction (Az = 0.895), followed by the maximum diameter (Az = 0.891).
CONCLUSION: The tumor volume changes in breast cancers before and after NAC, measured automatically using a commercially available computer-aided system and a clinical DCE MR imaging protocol might be the most accurate tool for evaluation of the pathologic response after NAC.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Breast cancer; MRI; Neoadjuvant chemotherapy; Pathologic response; Tumor cellularity

Mesh:

Substances:

Year:  2017        PMID: 28890184     DOI: 10.1016/j.clbc.2017.08.003

Source DB:  PubMed          Journal:  Clin Breast Cancer        ISSN: 1526-8209            Impact factor:   3.225


  4 in total

Review 1.  MRI Performance in Detecting pCR After Neoadjuvant Chemotherapy by Molecular Subtype of Breast Cancer.

Authors:  Nancy Yu; Vivian W Y Leung; Sarkis Meterissian
Journal:  World J Surg       Date:  2019-09       Impact factor: 3.352

2.  Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy.

Authors:  Raffaella Massafra; Maria Colomba Comes; Samantha Bove; Vittorio Didonna; Gianluca Gatta; Francesco Giotta; Annarita Fanizzi; Daniele La Forgia; Agnese Latorre; Maria Irene Pastena; Domenico Pomarico; Lucia Rinaldi; Pasquale Tamborra; Alfredo Zito; Vito Lorusso; Angelo Virgilio Paradiso
Journal:  J Pers Med       Date:  2022-06-10

3.  Clinical implications of changes in the diversity of c-MYC copy number variation after neoadjuvant chemotherapy in breast cancer.

Authors:  Yul Ri Chung; Hyun Jeong Kim; Milim Kim; Soomin Ahn; So Yeon Park
Journal:  Sci Rep       Date:  2018-11-12       Impact factor: 4.379

4.  Correlation between MRI morphological response patterns and histopathological tumor regression after neoadjuvant endocrine therapy in locally advanced breast cancer: a randomized phase II trial.

Authors:  Joana Reis; Owen Thomas; Maryam Lahooti; Marianne Lyngra; Hossein Schandiz; Joao Boavida; Kjell-Inge Gjesdal; Torill Sauer; Jürgen Geisler; Jonn Terje Geitung
Journal:  Breast Cancer Res Treat       Date:  2021-08-06       Impact factor: 4.872

  4 in total

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