Pathologic Complete Response and Its Impact on Breast Cancer Recurrence and Patient’s Survival after Neoadjuvant Therapy: A Comprehensive Meta-Analysis

Liu, Hui; Lv, Liqiong; Gao, Hui; Cheng, Ming

doi:10.1155/2021/7545091

Cited by 15 publications

(15 citation statements)

References 43 publications

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“…In addition to race, molecular subtypes, access to care and other factors could contribute to different outcomes. OS was also signi cantly associated with pCR, consistent with multiple clinical trials demonstrating improved breast cancer outcomes in patients who achieve pCR, with prognostic value greatest for aggressive tumor subtypes [44- 49,59].…”

Section: Overall Survivalsupporting

confidence: 74%

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in the Montefiore Health System in the Bronx

Dell’Aquila,

Vadlamani,

Maldjian

et al. 2023

Preprint

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Background. Predicting response to treatment and survival in breast cancer patients remains a crucial challenge. This study employed four machine-learning models to predict pathological complete response (pCR) and overall survival (OS) up to 7.5 years. Methods. Demographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy from 01/01/2017 to 12/31/2021 in the Montefiore Health System in the Bronx. Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with five-fold cross validation. Results. pCR was not associated with age, race, ethnicity, differentiation, income, and insurance status (p > 0.05). ER-/HER2 + showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2- (p < 0.05), tumor staging (p = 0.011), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.03) were associated with pCR. Machine-learning models ranked ER+/HER2-, ER-/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74–0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine-learning models ranked tumor stage, pCR, nodal stage, and triple negative subtype as top predictors of OS (AUC = 0.83–0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05). Conclusion. Tumor subtypes and imaging characteristics were top predictors of pCR. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine-learning models accurately predicted pCR and OS using clinicopathological and radiological data.

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Section: Overall Survivalsupporting

confidence: 74%

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in the Montefiore Health System in the Bronx

Dell’Aquila,

Vadlamani,

Maldjian

et al. 2023

Preprint

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“…In previous studies, other risk factors except for molecular subtypes of tumors have been reported. Young age, clinical T stage, and clinical N stage were referred to as risk factors for recurrence including LRR and distant metastases after achieving pCR [25][26][27][28][29][30][31]. According to Ishitobi M. et al, patients who were younger than 40 years at the time of diagnosis had significantly worse IBTR-free survival than those who were 40 years or older (5-year IBTR-free survival, 87.7 vs 96.9%; p = .002) [25].…”

Section: Discussionmentioning

confidence: 99%

Risk Factors of Breast Cancer Recurrence in Pathologic Complete Response Achieved by Patients Following Neoadjuvant Chemotherapy

Choi¹,

Woen²,

Jang³

et al. 2023

Preprint

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Background: Pathologic complete response (pCR) of breast cancer after neoadjuvant chemotherapy (NAC) is highly related to molecular subtypes. Patients who achieved tumor pCR after NAC have a better prognosis. However, despite of better prognosis, pCR patients have a potential for recurrence. There is little evidence of risk factors of recurrence in patients with pCR. We aim to analyze factors associated with tumor recurrence in patients who achieved pCR. Methods: This study retrospectively reviewed the data of patients diagnosed with breast cancer who achieved pCR after receiving NAC between January 2009 and December 2018 in Samsung Medical Center. pCR was defined as no residual invasive cancer in the breast and axillary nodes even if there is residual ductal carcinoma in situ (ypT0 or ypTis with ypN0). Breast cancers are classified into 4 subtypes based on hormone receptors (HR) and human epithelial growth factor receptor 2 (HER2) status. Patients who had bilateral breast cancer, inflammatory breast cancer, distant metastasis, unknown subtype, and histologically unique case were excluded from the study. Results: In total 483 patients were included in this study except for patients who corresponded to the exclusion criteria. The median follow-up duration was 59.0 months (range, 0.5-153.3 months). Breast cancer recurred in 4.1% of patients (20 of 483). There was a significant difference in clinical T (p = .004) and clinical N (p = .034) stage in the Kaplan-Meier curve for disease-free survival. Molecular subtypes (p = .573), Ki67 (p = 1.000), and breast surgery type (p = .574) were not associated with tumor recurrence in patients who achieved pCR after NAC. In the clinical T stage and clinical N stage, there was a significant difference between recurrence and no-recurrence groups (clinical T stage; p = .045, clinical N stage; p = .002). Univariable Cox regression revealed statistical significance in the clinical T stage (p = .049) and clinical N stage (p = .010), while multivariable Cox regression demonstrated non-significance in the clinical T stage (p = .320) and clinical N stage (p = .073). Conclusion: Results in this study showed that clinical T, clinical N stage, and molecular subtypes were not statistically significant predictors of recurrence in patients who achieved pCR after NAC. It is hypothesized that a multicenter-based study would lead to the identification of factors as predictors of recurrence after achieving pCR.

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“… 1 Total pathological complete response (total pCR), defined as complete eradication of invasive cancer in the breast and axillary lymph nodes (ypT0/is ypN0) after neoadjuvant therapy, was strongly associated with higher overall survival (OS) and event-free survival (EFS), particularly in HER-2 positive patients. 2 , 3 Total pCR (ypT0/is ypN0) was related to a better prognostic outcome of OS and EFS, 4 , 5 compared to the eradication of invasive cancer in the breast alone (ypT0/is).…”

Section: Introductionmentioning

confidence: 99%

Axillary Downstaging and the Impact of Clinical Axillary Status on Efficacy of Neoadjuvant Therapy for HER2-Positive Breast Cancer: A Network Meta-Analysis

Luo

Jiang

Liu

et al. 2023

Technol Cancer Res Treat

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Background: Lymph node downstaging and the achievement of total-pCR (ypT0/is ypN0) after neoadjuvant therapy are of great importance in HER-2 positive breast cancer. We aim to provide an overall review of neoadjuvant regimens for lymph node downstaging and to indirectly compare the total-pCR by various neoadjuvant regimens with network meta-analysis in HER2-positive patients according to their clinical lymph node status. Methods: Five English databases were searched comprehensively and systematically for relevant RCTs and case-control studies. The data extracted from the included studies were analyzed with the use of Review Manager 5.3 or STATA 15.0 software. Results: A total of 1508 published manuscripts were identified, and 17 studies including 4747 patients were finally included in our analysis. The network meta-analysis of total-pCR showed that dual-target therapy is significantly better than single-target therapy in clinically node-positive patients, and carboplatin performed significantly better than anthracycline in single-target condition. Lapatinib performed poorly in clinically node-positive patients. However, lapatinib in combination with trastuzumab was ranked at the top in the clinically node-negative group, and pertuzumab showed dissatisfied performance in contrast to the primacy of pertuzumab in clinically node-positive groups. Conclusion: In summary, different lymph node statuses led to the diverse first choice of neoadjuvant regimen. We highly recommended TCbHP as the first choice for the neoadjuvant treatment in clinically node-positive HER-2 positive breast cancer. Since lapatinib with trastuzumab ranked top in the clinically node-negative group, we looked forward to discovering the potential value of TKI in clinically node-negative patients, which needs further analysis in the future.

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Pathologic Complete Response and Its Impact on Breast Cancer Recurrence and Patient’s Survival after Neoadjuvant Therapy: A Comprehensive Meta-Analysis

Cited by 15 publications

References 43 publications

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in the Montefiore Health System in the Bronx

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in the Montefiore Health System in the Bronx

Risk Factors of Breast Cancer Recurrence in Pathologic Complete Response Achieved by Patients Following Neoadjuvant Chemotherapy

Axillary Downstaging and the Impact of Clinical Axillary Status on Efficacy of Neoadjuvant Therapy for HER2-Positive Breast Cancer: A Network Meta-Analysis

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