The prognostic value of lymph node cross-sectional cancer area in node-positive breast cancer: a comparison with N stage and lymph node ratio

Nontherapeutic Risk Factors of Different Grouped Stage IIIC Breast Cancer Patients’ Mortality: A Study of the US Surveillance, Epidemiology, and End Results Database

Objectives

Stage IIIC breast cancer, as a local advanced breast cancer, has a poor prognosis compared with that of early breast cancer. We further investigated the risk factors of mortality in stage IIIC primary breast cancer patients and their predictive value.

Methods

We extracted data from the US Surveillance, Epidemiology, and End Results (SEER) database of female patients with stage IIIC primary breast cancer (n = 1673) from January 2011 to December 2015.

Results

Hormone receptor negativity (P ≤ 0.001 and P ≤ 0.001, respectively), aggressive molecular typing (P ≤ 0.001 and P ≤ 0.001, respectively), high T stage (P ≤ 0.001 and P ≤ 0.001, respectively), a high number of positive lymph nodes (≥14) (P=0.005 and P=0.001, respectively), and lymph node ratio (≥0.8148) (P ≤ 0.001 and P ≤ 0.001, respectively) were associated with poor disease-specific survival. The indicators of disease-specific survival included estrogen receptor status, progesterone receptor status, molecular typing, T stage, number of positive lymph nodes, and lymph node ratio (P ≤ 0.001,P ≤ 0.001,P ≤ 0.001,P ≤ 0.001, P=0.002, and P ≤ 0.001, respectively).

Conclusion

Hormone receptor negativity, aggressive molecular typing, high T stage, high number of positive lymph nodes, and lymph node ratio are poor prognostic factors patients with stage IIIC primary breast cancer. The efficient indicators of disease-specific survival include estrogen receptor status, progesterone receptor status, molecular typing, T stage, number of positive lymph nodes, and lymph node ratio.

Prognostic Diagnosis for Breast Cancer Patients Using Probabilistic Bayesian Classification

The diagnosis and treatment of patients in the healthcare industry are greatly aided by data analytics. Massive amounts of data should be handled using machine learning approaches to provide tools for prediction and categorization to support practitioner decision-making. Based on the kind of tumor, disorders like breast cancer can be categorized. The difficulties associated with evaluating vast amounts of data should be overcome by discovering an efficient method for categorization. Based on the Bayesian method, we analyzed the influence of clinic pathological indicators on the prognosis and survival rate of breast cancer patients and compared the local resection value directly using the lymph node ratio (LNR) and the overall value using the LNR differences in effect between estimates. Logistic regression was used to estimate the overall LNR of patients. After that, a probabilistic Bayesian classifier-based dynamic regression model for prognosis analysis is built to capture the dynamic effect of multiple clinic pathological markers on patient prognosis. The dynamic regression model employing the total estimated value of LNR had the best fitting impact on the data, according to the simulation findings. In comparison to other models, this model has the greatest overall survival forecast accuracy. These prognostic techniques shed light on the nodal survival and status particular to the patient. Additionally, the framework is flexible and may be used with various cancer types and datasets.

Breast Cancer Patients With Positive Apical or Infraclavicular/Ipsilateral Supraclavicular Lymph Nodes Should Be Excluded in the Application of the Lymph Node Ratio System

Aim: Increasing studies have demonstrated lymph node ratio (LNR) to be an accurate prognostic indicator in breast cancer and an alternative to pN staging; however, the AJCC-TNM staging system classified apical or infraclavicular/ipsilateral supraclavicular lymph node-positive (APN(+)) patients with a worse prognosis as the pN3 stage. Until now, different reports on LNR in breast cancer have ignored this possibility. Consequently, it is necessary to discuss the role of APN(+) patients in the LNR system to obtain a precise LNR that predicts the prognosis accurately.

Materials and Methods: We collected data on 10,120 breast cancer patients, including 3,936 lymph node-positive patients (3,283 APN(−) and 653 APN(+) patients), who visited our hospital from 2007 to 2012. Then we applied X-tile analysis to calculate cut-off values and conduct survival analysis and multivariate analysis to evaluate patients’ prognosis.

Results: We confirmed that some APN(+) patients were mis-subgrouped according to previously reported LNR, indicating that APN(+) patients should be excluded in the application of LNR to predict prognosis. Then we applied X-tile analysis to calculate two cut-off values (0.15 and 0.34) for LNR-APN(−) patients and conducted survival analysis and found that LNR-APN(−) staging was superior to pN staging in predicting the prognosis of APN(−) breast cancer patients.

Conclusion: From this study, we conclude that excluding APN(+) patients is the most necessary condition for effective implementation of the LNR system. LNR-APN(−) staging could be a more comprehensive approach in predicting prognosis and guiding clinicians to provide accurate and appropriate treatment.

Quality assurance review: Intra-operative evaluation of sentinel lymph nodes in breast cancer

BACKGROUND

Intraoperative consultation (IOC) of axillary sentinel lymph node (SLN) biopsy continues to play a role in selected breast cancer patients. The reported sensitivity rates for intraoperative SLN evaluation in breast cancer range from 47% to 80%. We study a center where the majority of SLN IOC is performed by imprint cytology, and a protocol was established to limit microscopic examination to three slides for a reporting TAT goal of 30 min.

METHODS

Approval to conduct this study was obtained from the REB. A retrospective review was performed on all consecutive SLN cases sent for IOC. Reported IOC assessments of all cases were compared with the final pathology.

RESULTS

Of 164 patients, there were 22 (13%) false negative IOC events, including 15 missed macro-metastasis and 7 missed micro-metastasis. The overall sensitivity for touch imprint in detecting SLNs macro-metastasis was 70.9%. Reporting total turnaround time was on average 3 min longer, whereas sensitivity and specificity were not significantly different in the two protocol periods.

CONCLUSION

Implementation of an IOC policy for a maximum of three slides for imprint cytology did not result in a significant impact on the sensitivity, specificity, or total turnaround time for SLN in breast cancer patients. False negative IOC events were mainly due to sampling error. Quality review was made difficult by limited documentation related to the gross handling of the specimens at IOC. System factors identified include insufficient space for the IOC report on the pathology requisition, and the lack of clearly communicated expectations for documentation.

The prognostic value of lymph node ratio in survival of non-metastatic breast carcinoma patients

PURPOSE

This study was conducted to assess the correlation between lymph node ratio (LNR) and prognosis of non-metastatic invasive breast carcinoma.

METHOD

This retrospective study examined 455 patients who were diagnosed with non-metastatic, unilateral invasive breast carcinoma and underwent either breast conservative surgery (BCS) or modified radical mastectomy (MRM) with axillary lymph node dissection (ALND) with at least one lymph node identified in the ALND specimen. Receiver operating characteristics (ROC) curve analysis was used to find out predictive cut-off values of the LNR and negative lymph nodes (NLN).

RESULTS

The median follow-up duration was 38 months. The median DFS and OS were 68 months and 72 months, respectively. 25.1% of patients had reported recurrence. The optimal cut-off value of LNR was 0.40. LNR was found to correlate significantly with pathological T (p < 0.001), pathological N (p < 0.001), and NLN (p < 0.001). Univariate analysis of the patients showed that the age group ≤ 35 years, menstrual status, pathological T, nodal status, lymphovascular invasion (LVI), perineural invasion (PNI), tumor grade, estrogen receptor (ER), progesterone receptor (PR), molecular subtypes, LNR, and NLN can affect disease-free survival (DFS) (p < 0.05) and OS (p < 0.05). Multivariate analysis showed that the pathological T (p < 0.001), menstrual status (p = 0.030), and LNR (p < 0.001) were the independent prognostic factors for DFS. Pathological T (p < 0.001) and LNR (p < 0.001) were the independent prognostic factors affecting OS.

CONCLUSION

LNR is the independent prognostic factor of DFS and OS for non-metastatic breast carcinoma.

The value of positive lymph nodes ratio combined with negative lymph node count in prediction of breast cancer survival

BACKGROUND

Positive lymph node ratio (LNR), defined as ratio of positive lymph nodes to all lymph nodes removed, is a powerful prognostic factor in invasive breast cancer. Here we focused on the impact of negative lymph node (NLN) count on the prediction of value of LNR in breast cancer survival.

METHODS

Of 929 invasive breast cancer patients were enrolled in our retrospective study. We use Kaplan-Meier to calculate the 5-year overall survival (OS) according to different clinicopathologic parameters. The prediction value of NLN count and LNR in OS was examined.

RESULTS

The optimal cutoff of NLN count was designated as 9. Five-year OS was 77.0% and 95.0% in patients with NLN of 0-9 and ≥10, respectively (P<0.001). Among 204 patients who had 0-9 NLN, 25 patients with LNR 0-20.0% had 5-year OS of 95.7%, 104 patients with LNR 20.1-65.0% had 5-year OS of 83.4%, and 75 patients with LNR 65.1-100.0% had 5-year OS of 61.7% (P<0.001); Among 725 patients who had NLN ≥10, 650 patients with LNR 0-20.0% had 5-year OS of 96.1%, 68 patients with LNR 20.1-65.0% had 5-year OS of 86.8%, and 7 patients with LNR 65.1-100% had 5-year OS of 71.4% (P<0.001).

CONCLUSIONS

High NLN count is associated with improved survival in invasive breast cancer patients. Combining NLN count with LNR could be considered as an alternative to LNR alone in prediction of postoperative breast cancer survival.

Prognostic value of the lymph node ratio for lymph-node-positive breast cancer- is it just a denominator problem?

Purpose

To examine the prognostic value of lymph node ratio (LNR) for patients with node-positive breast cancer with varying numbers of minimum nodes removed (>5, > 10 and > 15 total node count).

Methods

This study examined the original histopathological reports of 332 node-positive patients treated in the state of New South Wales (NSW), Australia between 1 April 1995 and 30 September 1995. The LNR was defined as the number of positive lymph nodes (LNs) over the total number of LNs removed. The LNR cutoffs were defined as low-risk, 0.01–0.20; intermediate-risk, 0.21– 0.65; and high-risk, LNR >0.65.

Results

The median follow-up was 10.3 years. In multivariate analysis, LNR was an independent predictor of 10-year breast cancer specific survival when > 5 nodes were removed. However, LNR was not an independent predictor when > 15 nodes were removed. In a multivariate analysis the relative risk of death (RR) decreased from 2.20 to 1.05 for intermediate-risk LNR and from 3.07 to 2.64 for high-risk while P values increased from 0.027 to 0.957 for intermediate-risk LNR and 0.018 to 0.322 for high-risk with the number of nodes removed increasing from > 5 to > 15.

Conclusions

Although LNR is important for patients with low node denominators, for patients with macroscopic nodal metastases in several nodes following an axillary dissection who have more than 15 nodes dissected, the oncologist can be satisfied that prognosis, selection of adjuvant chemotherapy and radiotherapy fields can be based on the numerator of the positive nodes.

Electronic supplementary material

The online version of this article (doi:10.1186/s40064-015-0865-2) contains supplementary material, which is available to authorized users.

Clinical significance of lymph node ratio in locally advanced breast cancer molecular subtypes

BACKGROUND

The ratio of metastatic to dissected lymph nodes (lymph node ratio; LNR) is a sensitive and superior prognostic factor for lymph node evaluation, but its relationship to cancer subtypes is unclear.

PATIENTS AND METHODS

Data from 469 patients with axillary lymph node metastasis out of 640 early breast cancer cases were retrospectively analyzed. They were classified into 4 molecular subtypes; luminal A, luminal B HER2(+), HER2 overexpression, basal-like. LNRs were compared between groups and with other prognostic factors.

RESULTS

The distribution of LNRs was 35.2% in luminal A, 43.2% in luminal B HER2(+), 46.9% in HER2 over-expression, and 39.1% in basal-like. A significant difference was found between luminal A and HER2 over-expression subtypes (p = 0.023). LNR was significantly correlated with tumor size and lymphovascular invasion, but not with other prognostic factors including menopausal status, laterality, grade, and perineural invasion. An LNR of 29.8% was defined as the cut-off value, and significant differences in survival rates were identified accordingly between basal-like and both luminal A (p = 0.003) and luminal B HER2(+) (p = 0.04).

CONCLUSION

The LNR differs between some molecular subtypes of breast cancer, and it correlates with certain prognostic factors and survival. These data support using the LNR to assess breast cancer patients.