scholarly journals Clinical relevance of TNM staging system according to breast cancer subtypes

2011 ◽  
Vol 22 (7) ◽  
pp. 1554-1560 ◽  
Author(s):  
Y.H. Park ◽  
S.J. Lee ◽  
E.Y. Cho ◽  
Y.La. Choi ◽  
J.E. Lee ◽  
...  
2019 ◽  
Vol 30 (12) ◽  
pp. 2011 ◽  
Author(s):  
Y.H. Park ◽  
S.J. Lee ◽  
E.Y. Cho ◽  
Y. La. Choi ◽  
J.E. Lee ◽  
...  

2010 ◽  
Vol 28 (15_suppl) ◽  
pp. 632-632
Author(s):  
Y. Park ◽  
S. Lee ◽  
J. Kong ◽  
E. Cho ◽  
Y. Choi ◽  
...  

Author(s):  
Gabriel N. Hortobagyi ◽  
Stephen B. Edge ◽  
Armando Giuliano

Expanded understanding of biologic factors that modulate the clinical course of malignant disease have led to the gradual integration of biomarkers into staging classifications. The American Joint Committee on Cancer (AJCC) TNM staging system is universally used and has largely displaced other staging classifications for most, although not all, cancers. Many of the chapters of the eighth edition of the AJCC TNM staging system integrated biomarkers with anatomic definitions. The Breast Chapter added estrogen receptor (ER) and progesterone receptor (PR) expression, HER2 expression, and/or amplification and histologic grade to the anatomic assessment of tumor size, regional lymph node involvement, and distant metastases (known as TNM). While preserving an anatomic staging system for continuity and for regions where modern biomarkers are not always available, the eighth edition emphasizes the increased prognostic precision of the clinical prognostic stage groups and the pathologic prognostic stage groups. The clinical prognostic stage groups are applicable to all patients with primary breast cancer before any treatment has been implemented, but require a clinical and imaging evaluation as well as a biopsy with grade and available ER, PR, and HER2 results; the pathologic prognostic stage groups are applicable to all patients treated with complete surgical excision as first treatment and also require a complete pathology report, grade, and ER, PR, and HER2. Applying the pathologic prognostic stage groups to a large database of patients staged by basic TNM groupings changed the stage grouping of almost 40% of patients. Grouping by pathologic prognostic stage groups led to a better prognostic distribution of the group and more precise individual prognostication.


2017 ◽  
Vol 18 (4) ◽  
pp. e228-e232 ◽  
Author(s):  
Tamer M Fouad ◽  
Angelica M Gutierrez Barrera ◽  
James M Reuben ◽  
Anthony Lucci ◽  
Wendy A Woodward ◽  
...  

2006 ◽  
Vol 14 (1) ◽  
pp. 143-147 ◽  
Author(s):  
Pedro F Escobar ◽  
Rebecca J Patrick ◽  
Lisa A Rybicki ◽  
David E Weng ◽  
Joseph P Crowe

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Jianguo Lai ◽  
Bo Chen ◽  
Guochun Zhang ◽  
Xuerui Li ◽  
Hsiaopei Mok ◽  
...  

Abstract Background Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients. Methods We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA). Results A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001). Conclusions A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies.


Author(s):  
Jigar A Patel ◽  
Matthew T Hueman ◽  
Dechang Chen ◽  
Donald E Henson

2015 ◽  
Vol 21 (2) ◽  
pp. 147-154 ◽  
Author(s):  
Amila Orucevic ◽  
Jason Chen ◽  
James M. McLoughlin ◽  
Robert E. Heidel ◽  
Timothy Panella ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document