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Updated: Feb 7, 2026

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
Published on: March 20, 2020
Refined risk stratification in residual triple-negative breast cancer after neoadjuvant therapy using residual cancer
Tae Hoon Lee1, Hyunwoo Lee2, Jeong Yun Jang3
1Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
Purpose:
This study aimed to identify prognostic factors and to stratify recurrence risk using a prognostic model incorporating the identified factors in patients with residual triple-negative breast cancer (TNBC) following neoadjuvant systemic therapy (NST).
Methods:
A retrospective analysis was conducted using data from a prospectively collected single-institution database. Eligible patients had residual TNBC after NST and curative surgery between 2007 and 2020 and completed planned postoperative radiotherapy. Prognostic factors for disease-free survival (DFS) were identified using multivariable Cox proportional hazards regression. Risk groups were stratified according to the number of these factors.
Results:
A total of 347 patients were included. With a median follow-up of 61.6 months, the 5-year DFS and overall survival rates were 62.5% and 73.9%, respectively. Lymphovascular invasion (LVI) positivity and residual cancer burden (RCB) class 3 were significant risk factors for worse DFS. The 5-year DFS rates were 82.9% (0 factor), 55.7% (1 factor), and 20.0% (2 factors) (p < 0.001). The new three-tiered stratification using LVI positivity and RCB class 3 demonstrated a higher concordance index compared to RCB class (bootstrap-estimated difference 0.038, 95% CI 0.006-0.070, p = 0.026). Adjuvant capecitabine was associated with improved 5-year DFS in patients with 1 risk factor (71.9% vs. 44.2%, p = 0.027), but not in those with 0 (83.1% vs. 81.7%, p = 0.548) or 2 factors (29.2% vs. 14.6%, p = 0.066).
Conclusion:
Patients with residual TNBC can be stratified into risk groups based on LVI and RCB class. The effect of adjuvant treatment varied across these groups. This model may support more tailored adjuvant treatment decisions after NST.
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