Literature DB >> 32982647

Imaging Predictors for Nonsentinel Lymph Node Metastases in Breast Cancer Patients.

Yizi Cong1, Suxia Wang2, Haidong Zou1, Shiguang Zhu1, Xingmiao Wang1, Jianqiao Cao1, Ji Wang1, Yanqing Liu1, Guangdong Qiao1.   

Abstract

BACKGROUND: The relationship between imaging features and nonsentinel lymph node (NSLN) metastasis is not clear.
OBJECTIVES: To determine whether imaging features could predict NSLN metastasis in sentinel lymph node (SLN)-positive breast cancer patients and to provide new clues for avoiding unnecessary axillary lymph node dissection.
METHOD: 171 patients with clinically negative axillary lymph nodes and a pathologically positive SLN were recruited between January 2007 and January 2014. According to the Breast Imaging Reporting and Data System (BI-RADS), the effects of clinicopathological factors, especially imaging features, on NSLN metastases were assessed by univariate and multivariate statistical analyses.
RESULTS: The average number of dissected SLNs was 2.11 (range, 1-6); 56 of the 171 (32.75%) patients exhibited NSLN metastases. In univariate analysis, tumor size, number of positive SLNs, ratio of positive SLNs, mammographic mass margins, ultrasonographic mass margins, and ultrasonographic vascularity were significantly correlated with NSLN involvement. Furthermore, through multivariate analysis, tumor size, number of positive SLNs, mammographic mass margins, and ultrasonographic vascularity were still independent predictors of NSLN involvement. Additionally, in SLN-positive patients, number of positive SLNs and ultrasonographic vascularity could also predict the tumor burden in NSLN.
CONCLUSIONS: In addition to tumor size and the number of positive SLNs, mammographic mass margins and ultrasonographic vascularity were also independent predictors of NSLN metastases in SLN-positive patients of breast cancer. The number of positive SLNs and ultrasonographic vascularity could also predict the tumor burden in NSLN.
Copyright © 2019 by S. Karger AG, Basel.

Entities:  

Keywords:  Breast neoplasms; Mammography; Predictive value of tests; Sentinel lymph node biopsy; Ultrasonography

Year:  2019        PMID: 32982647      PMCID: PMC7490649          DOI: 10.1159/000501955

Source DB:  PubMed          Journal:  Breast Care (Basel)        ISSN: 1661-3791            Impact factor:   2.860


  26 in total

1.  Comparison of three mathematical models for predicting the risk of additional axillary nodal metastases after positive sentinel lymph node biopsy in early breast cancer.

Authors:  Y Moghaddam; M Falzon; L Fulford; N R Williams; M R Keshtgar
Journal:  Br J Surg       Date:  2010-11       Impact factor: 6.939

2.  Prediction of non-sentinel lymph node involvement in breast cancer patients with a positive sentinel lymph node.

Authors:  Anneleen Reynders; Olivier Brouckaert; Ann Smeets; Annouschka Laenen; Emi Yoshihara; Frederik Persyn; Giuseppe Floris; Karin Leunen; Frederic Amant; Julie Soens; Chantal Van Ongeval; Philippe Moerman; Ignace Vergote; Marie-Rose Christiaens; Gracienne Staelens; Koen Van Eygen; Alain Vanneste; Peter Van Dam; Cecile Colpaert; Patrick Neven
Journal:  Breast       Date:  2014-04-24       Impact factor: 4.380

3.  Is mammographic spiculation an independent, good prognostic factor in screening-detected invasive breast cancer?

Authors:  Andrew J Evans; Sarah E Pinder; Jonathan J James; Ian O Ellis; Eleanor Cornford
Journal:  AJR Am J Roentgenol       Date:  2006-11       Impact factor: 3.959

4.  Axillary dissection vs no axillary dissection in women with invasive breast cancer and sentinel node metastasis: a randomized clinical trial.

Authors:  Armando E Giuliano; Kelly K Hunt; Karla V Ballman; Peter D Beitsch; Pat W Whitworth; Peter W Blumencranz; A Marilyn Leitch; Sukamal Saha; Linda M McCall; Monica Morrow
Journal:  JAMA       Date:  2011-02-09       Impact factor: 56.272

5.  Correlation between mammograghic findings and clinical/ pathologic features in women with small invasive breast carcinomas.

Authors:  Jun-Nan Li; Jing Xu; Ju Wang; Chun Qing; Yu-Mei Zhao; Pei-Fang Liu
Journal:  Asian Pac J Cancer Prev       Date:  2014

6.  Mammographic tumor features can predict long-term outcomes reliably in women with 1-14-mm invasive breast carcinoma.

Authors:  Laszlo Tabar; Hsiu-Hsi Tony Chen; M F Amy Yen; Tibor Tot; Tao-Hsin Tung; Li-Sheng Chen; Yueh-Hsia Chiu; Stephen W Duffy; Robert A Smith
Journal:  Cancer       Date:  2004-10-15       Impact factor: 6.860

7.  Cancer Cell Interaction with Adipose Tissue: Correlation with the Finding of Spiculation at Mammography.

Authors:  Hiroki Moriuchi; Junzo Yamaguchi; Hiroko Hayashi; Hiroshi Ohtani; Isao Shimokawa; Hajime Abiru; Hideki Okada; Susumu Eguchi
Journal:  Radiology       Date:  2015-10-09       Impact factor: 11.105

8.  Strategies for subtypes--dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011.

Authors:  A Goldhirsch; W C Wood; A S Coates; R D Gelber; B Thürlimann; H-J Senn
Journal:  Ann Oncol       Date:  2011-06-27       Impact factor: 32.976

9.  Subtype is a predictive factor of nonsentinel lymph node involvement in sentinel node-positive breast cancer patients.

Authors:  Kaptan Gülben; Uğur Berberoğlu; Ogün Aydoğan; Volkan Kınaş
Journal:  J Breast Cancer       Date:  2014-12-26       Impact factor: 3.588

10.  Molecular subtype classification is a determinant of non-sentinel lymph node metastasis in breast cancer patients with positive sentinel lymph nodes.

Authors:  Wenbin Zhou; Zhongyuan He; Jialei Xue; Minghai Wang; Xiaoming Zha; Lijun Ling; Lin Chen; Shui Wang; Xiaoan Liu
Journal:  PLoS One       Date:  2012-04-26       Impact factor: 3.240

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  1 in total

1.  Contrast-Enhanced Spectral Mammography-Based Prediction of Non-Sentinel Lymph Node Metastasis and Axillary Tumor Burden in Patients With Breast Cancer.

Authors:  Xiaoqian Wu; Yu Guo; Yu Sa; Yipeng Song; Xinghua Li; Yongbin Lv; Dong Xing; Yan Sun; Yizi Cong; Hui Yu; Wei Jiang
Journal:  Front Oncol       Date:  2022-05-06       Impact factor: 5.738

  1 in total

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