Validation of nomograms to predict the risk of non-sentinels lymph node metastases in North African Tunisian breast cancer patients with sentinel node involvement

Prediction of negative axillary node clearance by sentinel node-positive to total node ratio: a retrospective cohort study

Introduction

Increasing evidence suggests that de-escalation of axillary surgery is safe, without significantly impacting patient outcome. Obtaining positive lymph nodes at a sentinel lymph node biopsy (SNB) can guide decisions toward the requirement of axillary nodal clearance (ANC). However, methods to predict how many further nodes will be positive are not available. This study investigates the feasibility of predicting the likelihood of a negative ANC based on the ratio between positive nodes and the total number of lymph nodes excised at SNB.

Methods

Retrospective data from January 2017 to March 2022 was collected from electronic medical records. Patients with oestrogen receptor (ER) positive and HER2 negative receptor disease were included in the study. ER-negative and HER2-positive disease was excluded, alongside patients who had chemotherapy before ANC.

Results

Of 102 patients, 58.8% (n=60) had no macrometastasis at ANC. On average, 2.76 lymph nodes were removed at SNB. A higher SNB ratio of positive to total nodes [OR 11.09 (CI 95% 2.33-52.72), P=0.002] had a significant association with positive nodes during ANC. SNB ratio less than or equal to 0.33 (1/3) had a specificity of 79.2% in identifying cases that later had a negative completion ANC, with a 95.8% specificity of no further upgrade of nodal staging.

Conclusion

A low SNB ratio of less than 0.33 (1/3) has a high specificity in excluding the upgradation of nodal staging on completion of ANC, with a false-negative rate of less than 5%. This may be used to identify patients with a low risk of axillary metastasis, who can avoid ANC.

Factors Influencing Non-sentinel Node Involvement in Sentinel Node Positive Patients and Validation of MSKCC Nomogram in Indian Breast Cancer Population

Current guidelines recommend completion axillary lymphnode dissection (ALND) when sentinel lymphnode (SLN) contains metastatic tumor deposit. In consequent ALND sentinel node is the only node involved by tumor in 40-70 % of cases. Recent studies demonstrate the oncologic safety of omitting completion ALND in low risk patients. Several nomograms (MSKCC, Stanford, MD Anderson score, Tenon score) had been developed in predicting the likelihood of additional nodes metastatic involvement. We evaluated accuracy of MSKCC nomogram and other clinicopathologic variables associated with additional lymph node metastasis in our patients. A total of 334 patients with primary breast cancer patients underwent SLN biopsy during the period Jan 2007 to June 2014. Clinicopathologic variables were prospectively collected. Completion ALND was done in 64 patients who had tumor deposit in SLN. The discriminatory accuracy of nomogram was analyzed using Area under Receiver operating characteristic curve (ROC). SLN was the only node involved with tumor in 69 % (44/64) of our patients. Additional lymph node metastasis was seen in 31 % (20/64). On univariate analysis, extracapsular infiltration in sentinel node and multiple sentinel nodes positivity were significantly associated (p < 0.05) with additional lymph node metastasis in the axilla. Area under ROC curve for nomogram was 0.58 suggesting poor performance of the nomogram in predicting NSLN involvement. Sentinel nodes are the only nodes to be involved by tumor in 70 % of the patients. Our findings indicate that multiple sentinel node positivity and extra-capsular invasion in sentinel node significantly predicted the likelihood of additional nodal metastasis. MSKCC nomogram did not reliably predict the involvement of additional nodal metastasis in our study population.

Comparison of five different popular scoring systems to predict nonsentinel lymph node status in patients with metastatic sentinel lymph nodes: a tertiary care center experience

Sentinel lymph node biopsy (SLNB) is the current standard of care for breast cancers with no clinically palpable axillary lymph nodes. Almost 50 % of sentinel lymph node positive patients have negative non-sentinel nodes and undergo non-therapeutic axillary dissection. Five different scoring systems, reported in the literature, were compared for their predictive ability of non-SLN involvement in patients with SLN positive breast cancer. 242 patients who underwent breast surgery and SLNB were included in the study. Of these, 70 who were confirmed to have SLN metastasis and received complementary ALND and constituted the final study population. The nomograms (MSKCC, M.D. Anderson Cancer Center, Tenon model, Stanford and Turkish) were statistically compared for their prediction of non-SLN metastasis (95 % confidence interval). We have determined only two clinicopathologic (multifocality and size of the primary tumor) situations which have a statistically significant association between SLN metastasis with using a multivariate logistic regression analysis. Multifocality (P = 0.001) and size of the primary tumor (P = 0.001) were associated with a higher probability of-SLN metastasis. No predictive model was constructed that showed good area under the curve (AUC) discrimination in the validation series. Currently published predictive models lack accuracy when applied to a different population. Multi-institutional heterogenic population studies are important to determine the exact combination of scoring systems and/or nomograms.

Axillary nodal metastases in Italian early breast cancer patients with positive sentinel lymph node: can axillary node dissection be avoided by using predictive nomograms?

AIMS AND BACKGROUND

Clinical guidelines recommend axillary lymph node dissection (ALND) in cases of metastatic sentinel lymph node (SNL) in patients with clinically node-negative early breast cancer. However, a relevant number of ALND could be avoided in a subset of patients in whom the risk of non-SNL metastases is low. In order to define this population, several authors have proposed mathematical models, which have been validated in many studies. These studies reached different conclusions regarding which model demonstrated the best statistical discrimination power, mainly due to differences in clinical and pathologic variables used, and particularly differences in the number of dissected SLNs.

METHODS

We retrospectively reviewed clinically node-negative patients who underwent ALND in our surgical ward after the diagnosis of breast cancer metastases on SLN biopsy from January 2000 to December 2012. The predictive accuracy of the widely used nomograms to predict the risk of additional nodal disease in our patients with SLN breast cancer metastases was measured by receiver operating characteristic curve. We then attempted to develop a new nomogram by analyzing the dataset.

RESULTS

A total of 105 patients were included in this study, with ratio of metastatic lymph node/removed lymph node of about 0.89; we found axillary nodal metastases on ALND in only 31 patients (29.5%). Applied to our dataset, Mayo nomogram showed the best area under the receiving operator characteristic curve (0.74) followed by our model (0.71). Instead, the Memorial Sloan-Kettering model showed poor discrimination, as did Tenon (0.56).

CONCLUSIONS

Based on our data, we cannot recommend the clinical use of validated predictive nomograms in order to avoid ALND. We suggest setting up a multicenter Italian study to build a model specific to our setting and based on larger series.

A new and simple predictive formula for non-sentinel lymph node metastasis in breast cancer patients with positive sentinel lymph nodes, and validation of 3 different nomograms in Turkish breast cancer patients

BACKGROUND

Nomogram accuracies for predicting non-sentinel lymph node (SLN) involvement vary between different patient populations. Our aim is to put these nomograms to test on our patient population and determine our individual predictive parameters affecting SLN and non-SLN involvement.

PATIENTS AND METHODS

Data from 932 patients was analyzed. Nomogram values were calculated for each patient utilizing MSKCC, Tenon, and MHDF models. Moreover, using our own patient- and tumor-depended parameters, we established a unique predictivity formula for SLN and non-SLN involvement.

RESULTS

The calculated area under the curve (AUC) values for MSKCC, Tenon, and MHDF models were 0.727 (95% confidence interval (CI) 0.64-0.8), 0.665 (95% CI 0.59-0.73), and 0.696 (95% CI 0.59-0.79), respectively. Cerb-2 positivity (p = 0.004) and size of the metastasis in the lymph node (p = 0.006) were found to correlate with non-SLN involvement in our study group. The AUC value of the predictivity formula established using these parameters was 0.722 (95% CI 0.63-0.81).

CONCLUSION

The most accurate nomogram for our patient group was the MSKCC nomogram. Our unique predictivity formula proved to be as equally effective and competent as the MSKCC nomogram. However, similar to other nomograms, our predictivity formula requires future validation studies.

Prognostic factors in elderly patients with breast cancer

Background

Breast cancer (BC) remains principally a disease of old ages; with 35-50% of cases occurring in women older than 65 years. Even mortality for cancer increases with aging: 19.7% between 65 and 74 years; 22.6% between 75 and 84 years; and 15.1% in 85 years or more.

The study was aimed to investigate specific predictive factors for elderly patients so to select the best way to treat and follow these patients.

Methods

A search was performed on Medline, Embase, Scopus using the following Key words: Breast cancer, Breast neoplasms, Aged, Elder, Elderly, Eldest, Older, Survival analysis, Prognosis, Prognostic factors, Tumor markers, Biomarkers, Comorbidity, Geriatric assessment, Axilla, Axillary surgery. 3029 studies have been retrieved. Paper in which overall or disease free survival were not end points, or age class was not well defined, or the sample was too small, were excluded. At last 42 papers fulfilled the criteria.

Results and discussion

Lack of screening and delay in diagnosis may be responsible for the minor improvement in survival observed in elderly respect to younger breast cancer patients. Predictive factors are the same and must be assessed with the same attention reserved to younger women.

Conclusions

Most of elderly patient are fit to undergo standard treatment and can get the same benefits of younger women. Nevertheless it is possible that some older women with early breast cancer can be spared too aggressive treatments. Geriatric assessment and co-morbidities can affect the prognosis modifying surveillance, life expectancy and compliance to therapies. They can thus be useful to select the better treatment, either surgical or radio or hormone - or chemo-therapy.

Which nomogram is best for predicting non-sentinel lymph node metastasis in breast cancer patients? A meta-analysis

To present a systematic [corrected] review and meta-analysis to evaluate the nomograms developed to predict non-sentinel lymph node (NSLN) metastasis in breast cancer patients. We focused on the six nomograms (Cambridge, MSKCC, Mayo, MDA, Tenon, and Stanford) that are the most widely validated. The AUCs were converted to odds ratios for the meta-analysis. In total, the Cambridge, Mayo, MDA, MSKCC, Stanford, and Tenon models were validated in 2,156, 2,431, 843, 8,143, 3,700, and 3,648 patients, respectively. The pooled AUCs for the Cambridge, MDA, MSKCC, Mayo, Tenon, and Stanford models were 0.721, 0.706, 0.715, 0.728, 0.720, and 0.688, respectively. Subgroup analysis revealed that in populations with a higher micrometastasis rate in the SLNs, the Tenon and Stanford models had a significantly higher predictive accuracy. A meta-regression analysis revealed that the SLN micrometastasis rate, but not the NSLN-positivity rate, was associated with improved predictive accuracy in the Tenon and Stanford models. The performance of the MSKCC and Cambridge models was not influenced by these two factors. All of these prediction models perform better than random chance. The Stanford model seems to be relatively inferior to the other models. The accuracy of the Tenon and Stanford models is influenced by the tumor burden in the SLNs.

A simple nomogram to evaluate the risk of nonsentinel node metastases in breast cancer patients with minimal sentinel node involvement

BACKGROUND

Tumor-positive sentinel node biopsy (SNB) suggests a risk of nonsentinel node metastases in breast cancer. This risk is lower after micrometastasis or isolated tumor cells (ITC) in the sentinel node (SN), and recent studies suggest that completion axillary lymph node dissection (ALND) might not improve outcome in these patients. We aim to validate existing predictive models and to develop a new model for micrometastatic and ITC patients.

METHODS

A series of 484 patients with micrometastases or ITC in SN followed by ALND was used to evaluate factors affecting nonsentinel node involvement. Logistic regression analysis was performed to construct a predictive model, which was validated by a separate series of 51 patients.

RESULTS

Only 7.2% of patients had additional metastases on completion ALND. Tumor diameter and multifocality associated with nonsentinel status on multivariate analysis. A predictive model was constructed showing good [area under the curve (AUC) 0.791] discrimination in the validation series. Previously published models performed poorly in our patient population.

CONCLUSIONS

Nonsentinel node metastases are rare with micrometastasis or ITC in SN. Most published predictive models for nonsentinel node involvement perform poorly in the present patient population. We developed a new predictive model which seems to perform well in discriminating patients with more than 10% risk of additional metastases. However, the presented nomogram needs to be validated with an independent patient series to evaluate its accuracy, especially for high-risk patients.