Literature DB >> 31187220

Can enhancement types on preoperative MRI reflect prognostic factors and surgical outcomes in invasive breast cancer?

Jieun Koh1, Ah Young Park1, Kyung Hee Ko1, Hae Kyoung Jung2.   

Abstract

OBJECTIVES: This study was conducted in order to evaluate whether enhancement types on preoperative MRI can reflect prognostic factors and surgical outcomes in invasive breast cancer.
METHODS: Among 484 consecutive patients who underwent preoperative breast MRI from October 2014 to July 2017 for biopsy-proven breast cancer, 313 patients with 315 invasive breast cancers who underwent subsequent surgery were finally included in this study. Two radiologists retrospectively reviewed preoperative MRI findings of these 315 lesions and categorized them to mass, nonmass, and combined type according to enhancement features. Combined type was defined as coexisted mass and nonmass enhancement. Histopathologic results focusing on prognostic factors and surgical outcomes were compared among the three types of lesion using Pearson's chi-square, linear-by-linear association, Kruskal-Wallis, one-way ANOVA test, and multinomial logistic regression.
RESULTS: Of the cancers analyzed, 198 (62.9%) were mass, 59 (18.7%) were nonmass, and 58 (18.4%) were combined type. The nonmass type showed the smallest invasive tumor size (p < 0.001) and the most common positive HER2 receptor status (p = 0.001). The combined type had the most frequent lymphovascular invasion (p = 0.011), axillary lymph node-positive status (p = 0.031), operation changes (p < 0.001), and first resection margin-positive status (p < 0.001). Initial operation of mastectomy was more frequent in the nonmass and combined types than that in the mass type (p < 0.001). But HER2 receptor status and operation changes showed no statistical significance on multivariate analysis.
CONCLUSIONS: Enhancement types on preoperative MRI reflect different prognostic factors and surgical outcomes in invasive breast cancer. KEY POINTS: • Morphologic features of contrast media uptake on contrast-enhanced MRI may be related with fundamental biological differences of invasive breast cancers. • Mass or nonmass enhancement type on preoperative MRI might reflect different prognostic factors and surgical outcomes in invasive breast cancer. • The combined mass and nonmass enhancement type might be associated with poorer prognosis and worse surgical outcomes.

Entities:  

Keywords:  Breast neoplasms; Magnetic resonance imaging; Prognosis; Surgical oncology

Mesh:

Substances:

Year:  2019        PMID: 31187220     DOI: 10.1007/s00330-019-06236-2

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  6 in total

1.  Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer.

Authors:  Tiantian Bian; Zengjie Wu; Qing Lin; Haibo Wang; Yaqiong Ge; Shaofeng Duan; Guangming Fu; Chunxiao Cui; Xiaohui Su
Journal:  Br J Radiol       Date:  2020-09-02       Impact factor: 3.039

2.  MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC.

Authors:  Qin Li; Qin Xiao; Jianwei Li; Shaofeng Duan; He Wang; Yajia Gu
Journal:  Cancer Manag Res       Date:  2020-10-27       Impact factor: 3.989

3.  Computer-Aided Diagnosis Parameters of Invasive Carcinoma of No Special Type on 3T MRI: Correlation with Pathologic Immunohistochemical Markers.

Authors:  Jinho Jeong; Chang Suk Park; Jung Whee Lee; Kijun Kim; Hyeon Sook Kim; Sun-Young Jun; Se-Jeong Oh
Journal:  Taehan Yongsang Uihakhoe Chi       Date:  2021-09-15

4.  MRI-Based Radiomics for Preoperative Prediction of Lymphovascular Invasion in Patients With Invasive Breast Cancer.

Authors:  Mayidili Nijiati; Diliaremu Aihaiti; Aisikaerjiang Huojia; Abudukeyoumujiang Abulizi; Sailidan Mutailifu; Nueramina Rouzi; Guozhao Dai; Patiman Maimaiti
Journal:  Front Oncol       Date:  2022-06-06       Impact factor: 5.738

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.  Value of Machine Learning with Multiphases CE-MRI Radiomics for Early Prediction of Pathological Complete Response to Neoadjuvant Therapy in HER2-Positive Invasive Breast Cancer.

Authors:  Qin Li; Qin Xiao; Jianwei Li; Zhe Wang; He Wang; Yajia Gu
Journal:  Cancer Manag Res       Date:  2021-06-28       Impact factor: 3.989

  6 in total

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