Literature DB >> 19826125

Prospective multicenter comparison of models to predict four or more involved axillary lymph nodes in patients with breast cancer with one to three metastatic sentinel lymph nodes.

Gabrielle Werkoff1, Eric Lambaudie, Eric Fondrinier, Jean Levêque, Fréderic Marchal, Michele Uzan, Emmanuel Barranger, François Guillemin, Emile Darai, Serge Uzan, Gilles Houvenaeghel, Roman Rouzier, Charles Coutant.   

Abstract

PURPOSE: Three models have been developed to predict four or more involved axillary lymph nodes (ALNs) in patients with breast cancer with one to three involved sentinel lymph nodes (SLNs). Two scores were developed by Chagpar et al (Louisville scores excluding or including method of detection), and a nomogram was developed by Katz et al. The purpose of our investigation was to compare these models in a prospective, multicenter study. PATIENTS AND METHODS: Our study involved a cohort of 536 patients having one to three involved SLNs who underwent ALN dissection. We evaluated the area under the receiver operating characteristic curve (AUC), calibration (for the Katz nomogram only), false-negative (FN) rate, and clinical utility of the three models. Results were compared with the optimal logistic regression (OLR) model that was developed from the validation cohort.
RESULTS: Among the 536 patients, 57 patients (10.6%) had > or = four involved ALNs. The AUC for the Katz nomogram was 0.84 (95% CI, 0.81 to 0.86). The Louisville score excluding method of detection was 0.75 (95% CI, 0.72 to 0.78). The Louisville score including method of detection was 0.77 (95% CI, 0.74 to 0.79). The FN rates were 2.5% (eight of 321 patients), 1.8% (two of 109 patients), and 0% (zero of 27 patients) for the Katz nomogram and the Louisville scores excluding and including method of detection, respectively. The Katz nomogram was well calibrated. Optimism-corrected bootstrap estimate AUC of the OLR model was 0.86. Using this result as a reasonable target for an external model, the performance of the Katz nomogram was remarkable.
CONCLUSION: We validated the three models for their use in clinical practice. The Katz nomogram outperformed the two other models.

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Year:  2009        PMID: 19826125     DOI: 10.1200/JCO.2009.21.9139

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  6 in total

1.  Preoperative MRI improves prediction of extensive occult axillary lymph node metastases in breast cancer patients with a positive sentinel lymph node biopsy.

Authors:  Christopher Loiselle; Peter R Eby; Janice N Kim; Kristine E Calhoun; Kimberly H Allison; Vijayakrishna K Gadi; Sue Peacock; Barry E Storer; David A Mankoff; Savannah C Partridge; Constance D Lehman
Journal:  Acad Radiol       Date:  2014-01       Impact factor: 3.173

2.  Clinical nomogram to predict bone-only metastasis in patients with early breast carcinoma.

Authors:  Yann Delpech; Sami I Bashour; Ruben Lousquy; Roman Rouzier; Kenneth Hess; Charles Coutant; Emmanuel Barranger; Francisco J Esteva; Noato T Ueno; Lajos Pusztai; Nuhad K Ibrahim
Journal:  Br J Cancer       Date:  2015-09-22       Impact factor: 7.640

3.  Multicenter prospective evaluation of the reliability of the combined use of two models to predict non-sentinel lymph node status in breast cancer patients with metastatic sentinel lymph nodes: the MSKCC nomogram and the Tenon score. Results of the NOTEGS study.

Authors:  Roman Rouzier; Catherine Uzan; Alexandra Rousseau; Eugénie Guillot; Sonia Zilberman; Charles Meyer; Pablo Estevez; Pierre-Francois Dupre; David Kere; Virginie Doridot; Gauthier D'halluin; Xavier Fritel; Nicolas Pouget; Clémentine Jankowski; Chafika Mazouni; Tabassome Simon; Charles Coutant
Journal:  Br J Cancer       Date:  2017-03-21       Impact factor: 7.640

4.  Intraoperative Nomograms, Based on One-Step Nucleic Acid Amplification, for Prediction of Non-sentinel Node Metastasis and Four or More Axillary Node Metastases in Breast Cancer Patients with Sentinel Node Metastasis.

Authors:  Kenzo Shimazu; Nobuaki Sato; Akiko Ogiya; Yoshiaki Sota; Daisuke Yotsumoto; Takashi Ishikawa; Seigo Nakamura; Takayuki Kinoshita; Hitoshi Tsuda; Yasuyo Ohi; Futoshi Akiyama; Shinzaburo Noguchi
Journal:  Ann Surg Oncol       Date:  2018-07-05       Impact factor: 5.344

5.  Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer.

Authors:  Xiang Cui; Hao Zhu; Jisheng Huang
Journal:  Front Oncol       Date:  2020-12-04       Impact factor: 6.244

6.  Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer.

Authors:  Xu Guo; Zhenyu Liu; Caixia Sun; Lei Zhang; Ying Wang; Ziyao Li; Jiaxin Shi; Tong Wu; Hao Cui; Jing Zhang; Jie Tian; Jiawei Tian
Journal:  EBioMedicine       Date:  2020-09-24       Impact factor: 8.143

  6 in total

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