A head to head comparison of nine tools predicting non-sentinel lymph node status in sentinel node positive breast cancer women

St Gallen 2019 guidelines understage the axilla in lobular breast cancer: a population-based study

BACKGROUND

The St Gallen 2019 guidelines for primary therapy of early breast cancer recommend omission of completion axillary lymph node dissection (cALND), regardless of histological type, in patients with one or two sentinel lymph node (SLN) metastases. Concurrently, adjuvant chemotherapy is endorsed for luminal A-like disease with four or more axillary lymph node (ALN) metastases. The aim of this study was to estimate the proportion of patients with invasive lobular cancer (ILC) versus invasive ductal cancer of no special type (NST) with one or two SLN metastases for whom cALND would have led to a recommendation for adjuvant chemotherapy.

METHODS

Patients with ILC and NST who had surgery between 2014 and 2017 were identified in the National Breast Cancer Register of Sweden. After exclusion of patients with incongruent or missing data, those who fulfilled the St Gallen 2019 criteria for cALND omission were included in the population-based study cohort.

RESULTS

Some 1886 patients in total were included in the study, 329 with ILC and 1507 with NST. Patients with ILC had a higher metastatic nodal burden and were more likely to have a luminal A-like subtype than those with NST. The prevalence of at least four ALN metastases was higher in ILC (31.0 per cent) than NST (14.9 per cent), corresponding to an adjusted odds ratio of 2.26 (95 per cent c.i. 1.59 to 3.21). Luminal A-like breast cancers with four or more ALN metastases were over-represented in ILC compared with NST, 52 of 281 (18.5 per cent) versus 43 of 1299 (3.3 per cent) (P < 0.001).

CONCLUSION

Patients with ILC more often have luminal A-like breast cancer with at least four nodal metastases. Omission of cALND in patients with luminal A-like invasive lobular cancer and one or two SLN metastases warrants future attention as there is a risk of nodal understaging and undertreatment in one-fifth of patients.

Are Breast Cancer Nomograms Still Valid to Predict the Need for Axillary Dissection?

BACKGROUND

Nomograms can help in estimating the nodal status among clinically node-negative patients. Yet their validity in external cohorts over time is unknown. If the nodal stage can be estimated preoperatively, the need for axillary dissection can be decided.

OBJECTIVES

The aim of this study was to validate three existing nomograms predicting 4 or more axillary lymph node metastases.

METHOD

The risk for ≥4 lymph node metastases was calculated for n = 529 eligible breast cancer patients using the nomograms of Chagpar et al. [Ann Surg Oncol. 2007;14:670-7], Katz et al. [J Clin Oncol. 2008;26(13):2093-8], and Meretoja et al. [Breast Cancer Res Treat. 2013;138(3):817-27]. Discrimination and calibration were calculated for each nomogram to determine their validity.

RESULTS

In this cohort, the AUC values for the Chagpar, Katz, and Meretoja models were 0.79 (95% CI 0.74-0.83), 0.87 (95% CI 0.83-0.91), and 0.82 (95% CI 0.76-0.86), respectively, showing good discrimination between patients with and without high nodal burdens.

CONCLUSION

This study presents support for the use of older breast cancer nomograms and confirms their current validity in an external population.

Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer

Background

Completion axillary lymph node dissection is overtreatment for patients with sentinel lymph node (SLN) metastasis in whom the metastatic risk of residual non-SLN (NSLN) is low. However, the National Comprehensive Cancer Network panel posits that none of the previous studies has successfully identified such subset patients. Here, we develop a multicentre deep learning radiomics of ultrasonography model (DLRU) to predict the risk of SLN and NSLN metastasis.

Methods

In total, 937 eligible breast cancer patients with ultrasound images were enrolled from two hospitals as the training set (n = 542) and independent test set (n = 395) respectively. Using the images, we developed and validated a prediction model combined with deep learning radiomics and axillary ultrasound to sequentially identify the metastatic risk of SLN and NSLN, thereby, classifying patients to relevant axillary management groups.

Findings

In the test set, the DLRU yields the best performance in identifying patients with metastatic disease in SLNs (sensitivity=98.4%, 95% CI 96.6–100) and NSLNs (sensitivity=98.4%, 95% CI 95.6–99.9). The DLRU also accurately stratifies patients without metastasis in SLN or NSLN into the corresponding low-risk (LR)-SLN and high-risk (HR)-SLN&LR-NSLN category with the negative predictive value of 97% (95% CI 94.2–100) and 91.7% (95% CI 88.8–97.9), respectively. Moreover, compared with the current clinical management, DLRU appropriately assigned 51% (39.6%/77.4%) of overtreated patients in the entire study cohort into the LR group, perhaps avoiding overtreatment.

Interpretation

The performance of the DLRU indicates that it may offer a simple preoperative tool to promote personalized axillary management of breast cancer.

Funding

The National Nature Science Foundation of China; The National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; The Scientific research project of Heilongjiang Health Committee; The Postgraduate Research &Practice Innovation Program of Harbin Medical University.

Nomogram-based estimate of axillary nodal involvement in ACOSOG Z0011 (Alliance): validation and association with radiation protocol variations

PURPOSE

A substantial proportion of patients enrolled on ACOSOG Z0011 received protocol-deviant radiation treatment. It is currently unknown whether these deviations involved the use of more extensive fields in patients at higher nomogram-predicted risk.

METHODS

We used the M.D. Anderson (MDA) and Memorial Sloan-Kettering (MSK) nomograms to estimate risk of additional positive axillary nodes using surgical pathology information. In the control arm, we compared axillary dissection (AD) findings to nomogram-predicted estimates for validation. We used logistic regression to evaluate whether nomogram-estimated higher risk of nodal involvement was associated with high tangent (HT) or supraclavicular (SCV) radiation fields for patients with known radiation field design.

RESULTS

552/856 (64.5%) had complete details for the MDA nomogram. Mean MDA risk estimate in both treatment arms was 23.8%. Estimated risk for patients on the AD arm with positive nodes was 25.9%. Higher risk estimate was associated with additional positive nodes in the AD arm (OR 1.04, 95% CI 1.02-1.06, p < 0.0001). We observed significant association with higher MDA nomogram-estimated risk and SCV radiation (OR 1.07, 95% CI 1.04-1.10, p < 0.0001) but not HT (OR 0.99, 95% CI 0.96-1.02, p = 0.52) The MSK nomogram had similar associations.

CONCLUSION

MDA and MSK nomogram risk estimates were associated with lymph node risk in ACOSOG Z0011. Radiation oncologists' use of differing radiation fields were associated with treating higher risk patients. ClinicalTrials.gov id: NCT00003854.

Predicting non-sentinel lymph node metastasis in Australian breast cancer patients: are the nomograms still useful in the post-Z0011 era?

BACKGROUND

Axillary lymph node dissection (ALND) can be avoided in breast cancer patients with low-volume disease in the sentinel lymph nodes (SLNs) according to Z0011 trial. We believe that nomograms developed for predicting non-sentinel lymph node (NSLN) metastases can guide the axillary treatment in patients who do not fully match the criteria of Z0011 study. We identified risk factors and evaluated the performance of three nomograms to predict NSLN status in patients with positive SLNs.

METHODS

Data from 526 breast cancer patients with positive SLNs who underwent ALND at two Australian hospitals from 2002 to 2015 were studied. Univariate and multivariate associations for NSLN metastasis were analysed. Predictive models evaluated were MD Anderson Cancer Centre (MDA), Helsinki University Hospital and Memorial Sloan Kettering Cancer Centre.

RESULTS

Thirty-nine per cent of patients demonstrated NSLN metastasis. The multivariate analysis identified extranodal extension (OR 3.2, 95% CI 2.07-4.80), tumour size >2 cm (OR 2.5, 95% CI 1.66-3.89), macrometastasis (OR 1.9, 95% CI 1.09-3.47), positive SLN ratio >0.5 (OR 1.7, 95% CI 1.16-2.60) and lymphovascular invasion (OR 1.6, 95% CI 1.09-2.44) as independent predictors for NSLN metastasis. MDA nomogram showed the best discrimination (area under the curve of 0.74) and a 9% false negative rate for predicted probability of NSLN metastasis ≤10%.

CONCLUSION

Our results suggest that presence of extranodal extension and tumour size >2 cm may influence the need of further axillary treatment. Conversely, ALND can be safety spared in low risk patients identified by MDA nomogram.

Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients

Purpose: Lymph node metastasis is a multifactorial event. Several scholars have developed nomograph models to predict the sentinel lymph nodes (SLN) metastasis before operation. According to the clinical and pathological characteristics of breast cancer patients, we use the new method to establish a more comprehensive model and add some new factors which have never been analyzed in the world and explored the prospect of its clinical application.

Materials and methods: The clinicopathological data of 633 patients with breast cancer who underwent SLN examination from January 2011 to December 2014 were retrospectively analyzed. Because of the imbalance in data, we used smote algorithm to oversample the data to increase the balanced amount of data. Our study for the first time included the shape of the tumor and breast gland content. The location of the tumor was analyzed by the vector combining quadrant method, at the same time we use the method of simply using quadrant or vector for comparing. We also compared the predictive ability of building models through logistic regression and Bagged-Tree algorithm. The Bagged-Tree algorithm was used to categorize samples. The SMOTE-Bagged Tree algorithm and 5-fold cross-validation was used to established the prediction model. The clinical application value of the model in early breast cancer patients was evaluated by confusion matrix and the area under receiver operating characteristic (ROC) curve (AUC).

Results: Our predictive model included 12 variables as follows: age, body mass index (BMI), quadrant, clock direction, the distance of tumor from the nipple, morphology of tumor molybdenum target, glandular content, tumor size, ER, PR, HER2, and Ki-67.Finally, our model obtained the AUC value of 0.801 and the accuracy of 70.3%.We used logistic regression to established the model, in the modeling and validation groups, the area under the curve (AUC) were 0.660 and 0.580.We used the vector combining quadrant method to analyze the original location of the tumor, which is more precise than simply using vector or quadrant (AUC 0.801 vs. 0.791 vs. 0.701, Accuracy 70.3 vs. 70.3 vs. 63.6%).

Conclusions: Our model is more reliable and stable to assist doctors predict the SLN metastasis in breast cancer patients before operation.

Risk factors and a predictive nomogram for non-sentinel lymph node metastases in Chinese breast cancer patients with one or two sentinel lymph node macrometastases and mastectomy

Background

Two ongoing prospective randomized trials are evaluating whether omitting axillary lymph node dissection (alnd) in patients with breast cancer (bca) and sentinel lymph node (sln) macrometastases undergoing mastectomy is safe. Determining predictive risk factors for non-sln metastases and developing a model to predict the probability of those patients having non-sln metastases is also important.

Methods

This retrospective study enrolled 396 patients with bca and 1-2 slns with macrometastases who underwent alnd and mastectomy between January 2012 and December 2016. Factors influencing the non-sln metastases were determined, and a predictive nomogram was formulated. Performance of the nomogram was evaluated by its area under the curve (auc).

Results

We developed a predictive nomogram with an auc of 0.81 (cross-validation 95% confidence interval: 0.75 to 0.86) that included 4 factors (tumour size, histologic grade, and number of negative slns and axillary lymph nodes on imaging).

Conclusions

Our predictive nomogram assesses the risk of non-sln metastases in patients with bca and 1-2 sln macrometastases undergoing mastectomy.

Impact of Size of the Tumour, Persistence of Estrogen Receptors, Progesterone Receptors, HER2Neu Receptors and Ki67 Values on Positivity of Axillary Lymph Nodes in Patients with Early Breast Cancer with Clinically Negative Axillary Examination

AIM:

The study aimed to identify factors that influence the positivity of axillary lymph nodes in patients with early breast cancer and clinically negative axillary lymph nodes, who were subjected for modified radical mastectomy and axillary lymphadenectomy.

MATERIAL AND METHODS:

This study included 81 surgically treated, early breast cancer patients during the period from 08-2015 to 05-2017. All the cases have been analysed by standard histological analysis including macroscopic and microscopic examination by routine H&E staining. For determination of molecular receptors, immunostaining by PT LINK immunoperoxidase has been done for HER2neu, ER, PR, p53 and Ki67.

RESULTS:

Patients age ranged between 31-73 years, an average of 56.86 years. The mean size of a primary tumour in the surgically treated patient was 20.33 ± 6.0 mm. Axillary dissection revealed from 5 to 32 lymph nodes, with an average of 14. Metastases have been found in 1 to 7 lymph nodes, with an average 0.7. Only 26 (32.1%) of the patients showed metastases in the axillary lymph nodes. The univariant regression analysis showed that the size of a tumour and presence of HER2neu receptors on cancer cells influence the positivity of the axillary lymph nodes. The presence of the estrogen receptors, progesterone receptors have no influence on the positivity for metastatic deposits of lymph nodes. Multivariant model and logistic regression analysis as significant independent factors or predictors of positivity of the axillary lymph nodes are influenced by the tumour size only.

CONCLUSION:

Our study showed that the metastatic involvement of the axillary lymph nodes is mainly influenced by the size of a tumour and presence of HER2neu receptors in the univariant analysis. This point to the important influence of positivity of the axillary lymph nodes but, in multi-variant regressive analysis the lymph node status correlates with the tumour size only.

Secondary node analysis as an indicator for axillary lymphadenectomy in breast cancer patients

INTRODUCTION

Currently, there is no agreement regarding if it would be necessary to perform an axillary lymph node dissection (ALND) in patients who have macrometastases in the sentinel lymph node (SLN). We studied the utility of the secondary node analysis (SN), defined as the following node after the SLN in an anatomical and lymphatic pathway, as a sign of malignant axillary involvement.

METHODS

An observational, retrospective and multicentre study was designed to assess the utility of the SN as a sign of axillary involvement. Among 2273 patients with breast cancer, a valid sample of 283 was obtained representing those who had the SN studied. Main endpoints of our study were: the SLN, the SN and the ALND histological pattern. Sensitivity, specificity and precision of the test were also calculated.

RESULTS

SN test, in cases with positive SLN, has a sensitivity of 61.1%, a specificity of 78.7%, a positive predictive value of 45.8% and a negative predictive value of 87.3% with a precision of 74.7%.

CONCLUSION

The study of the SN together with the technique of the SLN allows a more precise staging of the axillary involvement, in patients with breast cancer, than just the SLN technique.

A nomogram to predict lymph node metastasis in patients with early gastric cancer

Background

Lymph node status is crucial to determining treatment for early gastric cancer (EGC). We aim to establish a nomogram to predict the possibility of lymph node metastasis (LNM) in EGC patients.

Methods

Medical records of 952 EGC patients with curative resection, from 2002 to 2014, were retrospectively retrieved. Univariate and multivariate analysis were performed to examine risk factors associated with LNM. A nomogram for predicting LNM was established and internally validated.

Results

Five variables significantly associated with LNM were included in our model, these are sex (Odd ratio [OR] = 1.961, 95% confidence index [CI], 1.334 to 2.883; P = 0.001), depth of tumor (OR = 2.875, 95% CI, 1.872 to 4.414; P = 0.000), tumor size (OR = 1.986, 95% CI, 1.265 to 3.118; P = 0.003), histology type (OR = 2.926, 95% CI, 1.854 to 4.617; P = 0.000) and lymphovascular invasion (OR = 4.967, 95% CI, 2.996 to 8.235; P = 0.000). The discrimination of the prediction model was 0.786.

Conclusions

A nomogram for predicting lymph node metastasis in patients with early gastric cancer was successfully established, which was superior to the absolute endoscopic submucosal dissection (ESD) indication in terms of the clinical performance.

Prognostic Factors on the Positivity for Metastases of the Axillary Lymph Nodes from Primary Breast Cancer

AIM

The aim of the study was to identify the impact of T stage, the presence of estrogen, progesterone, HER2neu receptors and the values of the Ki67 on the positivity for metastases of the axillary lymph nodes, from primary breast cancer.

MATERIAL AND METHODS

290 surgically treated patients for breast cancer were included in the study. All cases have been analyzed by standard histological analysis including microscopic analysis on standard H&E staining. For determining the molecular receptors - HER2neu, ER, PR, p53 and Ki67, immunostaining by PT LINK immunoperoxidase has been done.

RESULTS

Patients age was ranged between 18-90 years, average of 57.6+11.9. The mean size of the primary tumor in the surgically treated patient was 30.27 + 18.3 mm. On dissection from the axillary pits 8 to 39 lymph nodes were taken out, an average of 13.81+5.56. Metastases have been found in 1 to 23 lymph nodes, an average 3.14+4.71. In 59% of the patients there have been found metastases in the axillary lymph nodes. The univariate regression analysis showed that the location, size of tumor, differentiation of the tumor, stage, the value of the Ki67 and presence of lymphovascular invasion influence on the positivity of the axillary lymph nodes. The presence of the estrogen receptors, progesterone receptors and HER2neu receptors showed that they do not have influence on the positivity for metastatic deposits in axillary lymph nodes. The multivariate model and the logistic regression analysis as independent significant factors or predictors of positivity of the axillary lymph nodes are influenced by the tumor size and the positive lymphovascular invasion.

CONCLUSION

Our study showed that the involving of the axillary lymph nodes is mainly influenced by the size of the tumor and the presence of lymphovascular invasion in the tumor. Ki67 determined proliferative index in the univariate analysis points the important influence of positivity in the axillary lymph nodes, but not in the multivariate regressive analysis.

Elaboration of a nomogram to predict nonsentinel node status in breast cancer patients with positive sentinel node, intraoperatively assessed with one step nucleic amplification: Retrospective and validation phase

BACKGROUND

Tumor-positive sentinel lymph node (SLN) biopsy results in a risk of non sentinel node metastases in micro- and macro-metastases ranging from 20 to 50%, respectively. Therefore, most patients underwent unnecessary axillary lymph node dissections. We have previously developed a mathematical model for predicting patient-specific risk of non sentinel node (NSN) metastases based on 2460 patients. The study reports the results of the validation phase where a total of 1945 patients were enrolled, aimed at identifying a tool that gives the possibility to the surgeon to choose intraoperatively whether to perform or not axillary lymph node dissection (ALND).

METHODS

The following parameters were recorded: Clinical: hospital, age, medical record number; Bio pathological: Tumor (T) size stratified in quartiles, grading (G), histologic type, lymphatic/vascular invasion (LVI), ER-PR status, Ki 67, molecular classification (Luminal A, Luminal B, HER-2 Like, Triple negative); Sentinel and non-sentinel node related: Number of NSNs removed, number of positive NSNs, cytokeratin 19 (CK19) mRNA copy number of positive sentinel nodes stratified in quartiles. A total of 1945 patients were included in the database. All patient data were provided by the authors of this paper.

RESULTS

The discrimination of the model quantified with the area under the receiver operating characteristics (ROC) curve (AUC), was 0.65 and 0.71 in the validation and retrospective phase, respectively. The calibration determines the distance between predicted outcome and actual outcome. The mean difference between predicted/observed was 2.3 and 6.3% in the retrospective and in the validation phase, respectively. The two values are quite similar and as a result we can conclude that the nomogram effectiveness was validated. Moreover, the ROC curve identified in the risk category of 31% of positive NSNs, the best compromise between false negative and positive rates i.e. when ALND is unnecessary (<31%) or recommended (>31%).

CONCLUSIONS

The results of the study confirm that OSNA nomogram may help surgeons make an intraoperative decision on whether to perform ALND or not in case of positive sentinel nodes, and the patient to accept this decision based on a reliable estimation on the true percentage of NSN involvement. The use of this nomogram achieves two main gools: 1) the choice of the right treatment during the operation, 2) to avoid for the patient a second surgery procedure.