Comparison of two models for predicting non-sentinel lymph node metastases in sentinel lymph node-positive breast cancer patients

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.

Validation of the Memorial Sloan Kettering Cancer Center nomogram for predicting non-sentinel lymph node metastasis in sentinel lymph node-positive breast-cancer patients

BACKGROUND

The main purpose of the study reported here was to validate the clinical value of the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram that predicts non-sentinel lymph node (SLN) metastasis in SLN-positive patients with breast cancer.

METHODS

Data on 1,576 patients who received sentinel lymph node biopsy (SLNB) at the Shandong Cancer Hospital from December 2001 to March 2014 were collected in this study, and data on 509 patients with positive SLN were analyzed to evaluate the risk factors for non-SLN metastasis. The MSKCC nomogram was used to estimate the probability of non-SLN metastasis and was compared with actual probability after grouping into deciles. A receiver-operating characteristic (ROC) curve was drawn and predictive accuracy was assessed by calculating the area under the ROC curve.

RESULTS

Tumor size, histological grade, lymphovascular invasion, multifocality, number of positive SLNs, and number of negative SLNs were correlated with non-SLN metastasis (P<0.05) by univariate analysis. However, multivariate analysis showed that tumor size (P=0.039), histological grade (P=0.043), lymphovascular invasion (P=0.001), number of positive SLNs (P=0.001), and number of negative SLNs (P=0.000) were identified as independent predictors for non-SLN metastasis. The trend of actual probability in various decile groups was comparable to the predicted probability. The area under the ROC curve was 0.722. Patients with predictive values lower than 10% (97/492, 19.7%) had a frequency of non-SLN metastasis of 17.5% (17/97).

CONCLUSION

The MSKCC nomogram can provide an accurate prediction of the probability of non-SLN metastasis, and offers a reference basis about axillary lymph node dissection. Axillary lymph node dissection could be avoided in patients with predictive values lower than 10%.

In breast cancer patients sentinel lymph node metastasis characteristics predict further axillary involvement

The aim of the study was to correlate various primary tumor characteristics with lymph node status, to examine sentinel lymph node (SLN) metastasis size and non-SLN axillary involvement, to look for a cut-off size/number value possibly predicting additional axillary involvement with more accuracy and to examine the relationship of SLN metastasis size to overall survival. Of 301 patients who underwent SLN biopsy, 75 had positive SLNs. The size of the metastases was measured. For different size categories, association with the prevalence of non-SLN metastases was assessed. Associations between metastasis size and tumor characteristics and overall survival (OS) were studied. The prevalence of axillary lymph node (ALN) involvement was not significantly different between cases with micrometastasis or macrometastasis in SLNs (p = 0.124). However, for metastases larger than 6, 7, and 8 mm, the prevalence of ALN involvement was significantly higher (p = 0.046, 0.022, and 0.025). OS was significantly lower in SLN-positive than in SLN-negative cases (p = 0.0375). Primary tumor size larger than 20 mm was associated with a significantly higher incidence of SLN metastasis (p < 0.001), and primary tumor size over 26 mm was associated with additional positive non-SLN (p < 0.001). Higher mitotic index (≥ 7) in primary tumors was significantly (p < 0.001) associated with ALN involvement in SLN-positive cases, whereas higher Ki67 labeling index was not significantly correlated with SLN or ALN involvement. Lymphovascular invasion (LVI) in primary tumors was significantly correlated with SLN positivity (p < 0.001) but not with further ALN involvement or OS. Tumor size and LVI are predictive for SLN metastasis. Mitotic index, primary tumor size, and larger volume SLN involvement are determinants of further ALN involvement. SLN metastasis size over 6 mm is a strong predictor of further axillary involvement. OS is shorter in the presence of positive SLN.

Axillary ultrasonography in breast cancer patients helps in identifying patients preoperatively with limited disease of the axilla

BACKGROUND

The sentinel lymph node biopsy (SLNB) procedure is the method of choice for the identification and monitoring of regional lymph node metastases in patients with breast cancer. In the case of a positive sentinel lymph node (SLN), additional lymph node dissection is still warranted for regional control, although 40-65 % have no additional axillary disease. Recent studies show that after breast-conserving surgery, SLNB, and adjuvant systemic therapy, there is no significant difference between recurrence-free period and overall survival if there are ≤2 positive axillary nodes. The purpose of this study was preoperative identification of patients with limited axillary disease (≤2 macrometastases) by using ultrasonography.

METHODS

Data from 1,103 consecutive primary breast cancer patients with tumors smaller than 50 mm, no palpable adenopathy, and a maximum of 2 SLNs with macrometastases were collected. The variable of interest was US of the axilla.

RESULTS

Of the 1,103 patients included, 1,060 remained after exclusion criteria. Of these, 102 (9.6 %) had more than 2 positive axillary nodes on ALND. Selected by unsuspected US, the chance of having >2 positive lymph nodes (LNs) is substantially lower (4.2 %). This is significant on univariate and multivariate analysis. After excluding the patients with extracapsular extension of the SLN, the chance of having >2 positive LNs is only 2.6 %. For pT1-2, this is 2.2 %.

CONCLUSIONS

The risk of more than 2 positive axillary nodes is relatively small in patients with cT1-2 breast cancer. US of the axilla helps in further identifying patients with a minimal risk of additional axillary disease, putting ALND up for discussion.

Radio-guided sentinel lymph node identification by lymphoscintigraphy fused with an anatomical vector profile: clinical applications

OBJECTIVE

To develop a method to fuse lymphoscintigraphic images with an adaptable anatomical vector profile and to evaluate its role in the clinical practice.

METHODS

We used Adobe Illustrator CS6 to create different vector profiles, we fused those profiles, using Adobe Photoshop CS6, with lymphoscintigraphic images of the patient. We processed 197 lymphoscintigraphies performed in patients with cutaneous melanomas, breast cancer or delayed lymph drainage.

RESULTS

Our models can be adapted to every patient attitude or position and contain different levels of anatomical details ranging from external body profiles to the internal anatomical structures like bones, muscles, vessels, and lymph nodes. If needed, more new anatomical details can be added and embedded in the profile without redrawing them, saving a lot of time. Details can also be easily hidden, allowing the physician to view only relevant information and structures. Fusion times are about 85 s. The diagnostic confidence of the observers increased significantly. The validation process showed a slight shift (mean 4.9 mm).

CONCLUSIONS

We have created a new, practical, inexpensive digital technique based on commercial software for fusing lymphoscintigraphic images with built-in anatomical reference profiles. It is easily reproducible and does not alter the original scintigraphic image. Our method allows a more meaningful interpretation of lymphoscintigraphies, an easier recognition of the anatomical site and better lymph node dissection planning.

Applicability of two different validated models to predict axillary non-sentinel lymph node status by sentinel node biopsy in a single Italian center

BACKGROUND

The necessity of complete axillary lymph node dissection (CALND) after sentinel lymph node biopsy (SLNB) for women with sentinel lymph node metastases is a matter of debate because non-sentinel lymph nodes after CALND contain no further metastases in about 50 % of cases. Our study aims to determine the applicability in our setting of two different validated nomograms to predict axillary lymph node status after SLNB.

METHODS

We collected data about all women who underwent SLNB in our Department of Surgery from 2007 to 2010, focusing on tumor, patient, and breast characteristics. Data was analyzed by R (version 2.15.2); p < 0.05 was considered significant.

RESULTS

Among 511 women who underwent SLNB, 126 received CALND due to sentinel lymph node metastasis, and 73.0 % of these had no further metastatic non-sentinel lymph node. The area under the receiver operating characteristic (ROC) curves for the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram and the Tenon score were 78.5 % (95 % CI 70.1-86.8 %) and 77.0 % (95 % CI 67.9-86.0 %) (p = 0.678), respectively.

CONCLUSIONS

Both the MSKCC nomogram and the Tenon score were predictive for the axillary non-sentinel lymph node status by SLNB. The MSKCC nomogram was the more accurate of the two and the Tenon score was the easier one to apply.

Risk factors for sentinel lymph node metastasis and validation study of the MSKCC nomogram in breast cancer patients

OBJECTIVE

To evaluate the risk factors for sentinel lymph node metastasis and validate the value of the Memorial Sloan-Kettering Cancer Center nomogram for the prediction of sentinel lymph node metastasis in breast cancer patients.

METHODS

A sentinel lymph node biopsy database containing 1227 consecutive breast cancer patients (416 patients with at least one positive sentinel lymph node) was retrospectively analyzed. The predictive value of the Memorial Sloan-Kettering Cancer Center nomogram was calculated by the trend line and the area under the receiver-operator characteristic curve. Meanwhile, predictors for sentinel lymph node metastasis were also evaluated.

RESULTS

Tumor size, histological grade, lymphovascular invasion, mulifocality, estrogen receptor and progesterone receptor status were significant independent predictors for sentinel lymph node metastasis (all P<0.01). The Memorial Sloan-Kettering Cancer Center nomogram presented an area under the receiver-operator characteristic curve value of 0.730. Patients with predictive value<16% had a frequency of sentinel lymph node metastasis of 0.9%. Those with values larger than 70% had a frequency of 96.2%.

CONCLUSIONS

The risk factors for sentinel lymph node metastasis in our study were consistent with those in the Memorial Sloan-Kettering Cancer Center nomogram. The Memorial Sloan-Kettering Cancer Center nomogram is a useful tool that could accurately predict the probability of sentinel lymph node metastasis in our breast cancer patients. Axillary surgical staging might be avoided in patients with a predictive value of <16% and axillary lymph node dissection might be done directly in those with a predictive value >70%, while other patients should still accept sentinel lymph node biopsy.

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.

Prediction of non-sentinel lymph node status in breast cancer patients with sentinel lymph node metastases: evaluation of the tenon score

INTRODUCTION

Current guidelines recommend completion axillary lymph node dissection (cALND) in case of a sentinel lymph node (SLN) metastasis larger than 0.2 mm. However, in 50%-65% of these patients, the non-SLNs contain no further metastases and cALND provides no benefit. Several nomograms and scoring systems have been suggested to predict the risk of metastases in non-SLNs. We have evaluated the Tenon score.

PATIENTS AND METHODS

In a retrospective review of the Swedish Sentinel Node Multicentre Cohort Study, risk factors for additional metastases were analysed in 869 SLN-positive patients who underwent cALND, using uni- and multivariate logistic regression models. A receiver operating characteristic (ROC) curve was drawn on the basis of the sensitivity and specificity of the Tenon score, and the area under the curve (AUC) was calculated.

RESULTS

Non-SLN metastases were identified in 270/869 (31.1%) patients. Tumour size and grade, SLN status and ratio between number of positive SLNs and total number of SLNs were significantly associated with non-SLN status in multivariate analyses. The area under the curve for the Tenon score was 0.65 (95% CI 0.61-0.69). In 102 patients with a primary tumour <2 cm, Elston grade 1-2 and SLN metastases ≤2 mm, the risk of non SLN metastasis was less than 10%.

CONCLUSION

The Tenon score performed inadequately in our material and we could, based on tumour and SLN characteristics, only define a very small group of patients in which negative non-sentinel nodes could be predicted.