Predictive Factors for Nonsentinel Lymph Node Metastasis in Patients With Positive Sentinel Lymph Nodes After Neoadjuvant Chemotherapy: Nomogram for Predicting Nonsentinel Lymph Node Metastasis

Can Axillary Ultrasound Identify Node Positive Patients Who can Avoid an Axillary Dissection After Neoadjuvant Chemotherapy?

INTRODUCTION

Axillary lymph node dissection (ALND) is recommended for patients with invasive breast cancer with axillary metastasis treated with neoadjuvant chemotherapy (NAC) who do not have a nodal pathologic complete response (n-pCR). We hypothesized that patients with a single, ultrasound-suspicious, nonpalpable lymph node (LN) at diagnosis, who do not achieve an n-pCR, will have ypN1 disease on surgical pathology.

METHODS

This retrospective study identified breast cancer patients in our institution from 2012 to 2020 with axillary metastasis treated with NAC who did not achieve an n-pCR and had an ALND. Patient's tumor characteristics, axillary ultrasound, and lymph node disease burden at the time of surgery were reviewed.

RESULTS

Fifty five patients met the criteria and 36% had one suspicious LN on ultrasound, 25% had 2, and 38% had >3. After chemotherapy, 64% had ypN1 disease, 29% had ypN2 disease, and 7% had ypN3 disease. Of the 20 patients with one abnormal LN on initial ultrasound, 17 (85%, 95% CI 61-96%) had ypN1 disease. Eleven patients with one abnormal LN on initial ultrasound also had no suspicious LNs on prechemotherapy physical exam; among these patients, 100% had ypN1 disease.

CONCLUSIONS

For breast cancer patients who do not achieve an n-pCR after NAC, pretreatment normal clinical axillary exam and prechemotherapy ultrasound showing only one abnormal LN is associated with ypN1 disease. It may be reasonable to consider omitting completion ALND in this subset of patients while awaiting the results of the Alliance A011202 trial.

Non-sentinel node metastasis prediction during surgery in breast cancer patients with one to three positive sentinel node(s) following neoadjuvant chemotherapy

Our aim was to develop a tool to accurately predict the possibility of non-sentinel lymph node metastasis (NSLNM) during surgery so that a surgeon might decide the extent of further axillary lymph node dissection intraoperatively for patients with 1-3 positive sentinel lymph node(s) (SLN) after neoadjuvant chemotherapy. After retrospective analysis of Asan Medical Center (AMC) database, we included 558 patients' records who were treated between 2005 and 2019. 13 factors were assessed for their utility to predict NSLNM with chi-square and logistic regression with a bootstrapped, backward elimination method. Based on the result of the univariate analysis for statistical significance, number of positive SLN(s), number of frozen nodes, Progesterone Receptor (PR) positivity, clinical N stage were selected for the multivariate analysis and were utilized to generate a nomogram for prediction of residual nodal disease. The resulting nomogram was tested for validation by using a patient group of more recent, different time window at AMC. We designed a nomogram to be predictive of the NSLNM which consisted of 4 components: number of SLN(s), number of frozen nodes, PR positivity, and clinical N stage before neoadjuvant chemotherapy. The Area under the receiver operating characteristics curve (AUC) value of this formula was 0.709 (95% CI, 0.658-0.761) for development set and 0.715 (95% CI, 0.634-0.796) for validation set, respectively. This newly created AMC nomogram may provide a useful information to a surgeon for intraoperative guidance to decide the extent of further axillary surgery.

Surgical Management of the Axilla for Breast Cancer

This review discusses the contemporary surgical management of the axilla in patients with breast cancer. Surgical paradigms are highlighted by clinical nodal status at presentation and treatment approach, including upfront surgery and neoadjuvant systemic therapy settings. This review focuses on the increasing opportunities for de-escalating the extent of axillary surgery in the era of sentinel lymph node biopsy, while also reviewing the remaining indications for axillary clearance with axillary lymph node dissection.

Early prediction of pathological complete response to neoadjuvant chemotherapy combining DCE-MRI and apparent diffusion coefficient values in breast Cancer

INTRODUCTION

Improving the early prediction of neoadjuvant chemotherapy (NAC) efficacy in breast cancer can lead to an improved prediction of the final prognosis of patients, which would be useful for promoting individualized treatment. This study aimed to explore the value of the combination of dynamic contrast-enhanced (DCE)-MRI parameters and apparent diffusion coefficient (ADC) values in the early prediction of pathological complete response (pCR) to NAC for breast cancer.

METHODS

A total of 119 (range, 28-69 years) patients with biopsy-proven breast cancer who received two cycles of NAC before breast surgery were retrospectively enrolled from our hospital database. Patients were divided into pCR and non pCR groups according to their pathological responses; a total of 24 patients achieved pCR, while 95 did not. The quantitative (K^trans; K_ep; V_e; IAUC) and semiquantitative parameters (W-in; W-out; TTP) of DCE-MRI that were significantly different between groups were combined with ADC values to explore their value in the early prediction of pCR to NAC for breast cancer. The independent T test was performed to compare the differences in DCE-MRI parameters and ADC values between the two groups. Receiver operating characteristic (ROC) curves were plotted, and the area under the ROC curve (AUC), sensitivity and specificity were calculated to evaluate the performance of the prediction.

RESULTS

The K^trans, K_ep, IAUC, ADC, W-in and TTP values were significantly different between the pCR and non pCR groups after NAC. The AUC (0.845) and specificity (95.79%) of the combined K^trans, K_ep, IAUC and ADC values were both higher than those of the individual parameters. The combination of W-in, TTP and ADC values had the highest AUC value (0.886) in predicting pCR, with a sensitivity and specificity of 87.5% and 82.11%, respectively.

CONCLUSIONS

The results suggested that the combination of ADC values and quantitative and semiquantitative DCE-MRI parameters, especially the combination of W-in, TTP, and ADC values, may improve the early prediction of pCR in breast cancer.

A Model Incorporating Axillary Tail Position on Mammography for Preoperative Prediction of Non-sentinel Lymph Node Metastasis in Patients with Initial cN+ Breast Cancer after Neoadjuvant Chemotherapy

RATIONALE AND OBJECTIVES

This study aimed to develop a model incorporating axillary tail position on mammography (AT) for the prediction of non-sentinel Lymph Node (NSLN) metastasis in patients with initial clinical node positivity (cN+).

METHODS AND MATERIALS

The study reviewed a total of 257 patients with cN+ breast cancer who underwent both sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND) following neoadjuvant chemotherapy (NAC). A logistic regression model was developed based on these factors and the results of post-NAC AT and axillary ultrasound (AUS).

RESULTS

Four clinical factors with p<0.1 in the univariate analysis, including ycT0(odds ratio [OR]: 4.84, 95% confidence interval [CI]: 2.13-11.91, p<0.001), clinical stage before NAC (OR: 2.68, 95%CI: 1.15-6.58, p=0.025), estrogen receptor (ER) expression (OR: 3.29, 95%CI: 1.39-8.39, p=0.009), and HER2 status (OR: 0.21, 95%CI: 0.08-0.50, p=0.001), were independent predictors of NSLN metastases. The clinical model based on the above four factors resulted in the area under the curve (AUC) of 0.82(95%CI: 0.76-0.88) in the training set and 0.83(95% CI: 0.74-0.92) in the validation set. The results of post-NAC AUS and AT were added to the clinical model to construct a clinical imaging model for the prediction of NSLN metastasis with AUC of 0.87(95%CI: 0.81-0.93) in the training set and 0.89(95%CI: 0.82-0.96) in the validation set.

CONCLUSIONS

The study incorporated the results of post-NAC AT and AUS with other clinal factors to develop a model to predict NSLN metastasis in patients with initial cN+ before surgery. This model performed excellently, allowing physicians to select patients for whom unnecessary ALND could be avoided after NAC.

Factors Influencing Non-sentinel Lymph Node Involvement in Patients with Positive Sentinel Lymph Node(s) After Neoadjuvant Chemotherapy for Breast Cancer

BACKGROUND

When a positive sentinel lymph node (SLN) is identified after neoadjuvant chemotherapy (NAC), completion axillary lymph node dissection (cALND) is generally recommended. We sought to evaluate the rate of non-SLN positivity and factors influencing this in patients with a positive SLN following NAC.

METHODS

We identified all patients at our hospital between 2006 and 2021 with a positive SLN (> 0.2 mm) following NAC who underwent cALND. Rates of positive non-SLN (NSLN) on cALND were compared by nodal status. Chi-square tests and multivariable logistic regression were used to assess factors predictive of positive NSLN and overall nodal burden.

RESULTS

Overall, 229 cases (177 cN+, 52 cN0 prior to NAC) with positive SLN(s) after NAC underwent cALND. Additional NSLN involvement was found in 129/229 (56.3%) patients, including 24/52 (46.2%) cN0 and 105/177 (59.3%) cN+ patients (p = 0.09). There was a trend for patients with SLN micrometastases to be less likely to have positive NSLN(s) than those with SLN macrometastases (38.5% vs. 58.6%; p = 0.05). Subgroup analyses showed no clinicopathologic factors significantly associated with additional axillary involvement for initially cN0 patients. Factors found to significantly influence NSLN positivity in the initially cN+ subgroup were HER2 status, multicentricity/multifocality, number of positive SLNs, and size of SLN metastasis. SLN metastasis size > 5 mm and three or more positive SLNs exerted the greatest influence on NSLN positivity.

CONCLUSION

Rates of nodal positivity on cALND in the setting of positive SLN after NAC are high, supporting the current standard of routine cALND. In cN+ disease, NSLN positivity varies by tumor biology, multicentricity/multifocality, number of positive SLNs, and SLN metastasis size.

AGO Recommendations for the Surgical Therapy of the Axilla After Neoadjuvant Chemotherapy: 2021 Update

For many decades, the standard procedure to treat breast cancer included complete dissection of the axillary lymph nodes. The aim was to determine histological node status, which was then used as the basis for adjuvant therapy, and to ensure locoregional tumour control. In addition to the debate on how to optimise the therapeutic strategies of systemic treatment and radiotherapy, the current discussion focuses on improving surgical procedures to treat breast cancer. As neoadjuvant chemotherapy is becoming increasingly important, the surgical procedures used to treat breast cancer, whether they are breast surgery or axillary dissection, are changing. Based on the currently available data, carrying out SLNE prior to neoadjuvant chemotherapy is not recommended. In contrast, surgical axillary management after neoadjuvant chemotherapy is considered the procedure of choice for axillary staging and can range from SLNE to TAD and ALND. To reduce the rate of false negatives during surgical staging of the axilla in pN+ _CNB stage before NACT and ycN0 after NACT, targeted axillary dissection (TAD), the removal of > 2 SLNs (SLNE, no untargeted axillary sampling), immunohistochemistry to detect isolated tumour cells and micro-metastases, and marking positive lymph nodes before NACT should be the standard approach. This most recent update on surgical axillary management describes the significance of isolated tumour cells and micro-metastasis after neoadjuvant chemotherapy and the clinical consequences of low volume residual disease diagnosed using SLNE and TAD and provides an overview of this yearʼs AGO recommendations for surgical management of the axilla during primary surgery and in relation to neoadjuvant chemotherapy.

Predictive modelling of level IIb lymph node metastasis in oral squamous cell carcinoma

The aim of the present study was to examine the conditions, characteristics, and risk factors of level IIb lymph node metastases in oral squamous cell carcinoma and to formulate surgical criteria for level IIb lymph node dissection. We analyzed clinical and pathological records for 541 oral squamous carcinoma patients in relation to level IIb metastasis. Univariate and multivariate analyses were performed to detect risk factors for level IIb lymph node metastasis; a predictive model was built based on multivariate analysis and tested in a validation group. Univariate and multivariate analyses using the training group indicated that level IIa metastasis and Lymphovascular permeation (LVP) were two independent risk factors for level IIb lymph node metastasis. This model was built and tested in a validation group, the area under the curve being 0.697 (P < .0.001). The model's sensitivity was 66.7% and specificity was 77.4%. Nomogram incorporating validated variables was developed for level IIb metastasis prediction. Expected survival probabilites were analysed to specify significance of model's variable on patients' overall survival and recurrence. Level IIb dissection should be performed in patients with level IIa metastasis and LVP. However, thorough consideration of the oncologic safety of omitting level IIb dissection is compulsory.

Predictors of positive axillary non-sentinel lymph nodes in breast cancer patients with positive sentinel lymph node biopsy after neoadjuvant systemic therapy

AIM

To assess the rate of positive non-sentinel lymph nodes (non-SLNs) after neoadjuvant systemic therapy (NAST) in breast cancer (BC) following positive sentinel lymph node biopsy (SLNB).

MATERIALS AND METHODS

From institutional database, 265 consecutive patients receiving NAST for cT1-3, any N, M0 BC between 2001 and 2018 were identified. Patients presented clinically negative axilla before surgery and were candidate for SLNB. Following metastatic SLNB, completion axillary lymph node dissection (AxLND) was performed. Non-SLNs rate was investigated using multivariate (MV) logistic regression models. The distribution of non-SLNs across the axilla was observed.

RESULTS

Positive non-SLNs were found in 62.3% of cases and showed no correlation with SLN metastasis size. At MV, statistically significant variables associated with non-SLNs were older age (p = 0.025), clinically positive lymph nodes (p = 0.002), SLN extracapsular extension (ECE, p = 0.001), and higher ratio of positive SLNs/total SLNs (p = 0.016). ECE and higher nodal ratio were independent predictors of III axillary level positivity. By categorizing patients in intermediate- and high-risk groups using the study variables, positive non-SLNs were found in the range of 23-56% across the three axillary levels, rates which did not support radiotherapy volume de-escalation. The III axillary level lower involvement (6.3%) was better identified with the RAPCHEM trial criteria based on the ypN status after AxLND.

CONCLUSIONS

Involved non-SLNs rate following positive SLNB after NAST is nearly double the rate observed after primary surgery, supporting some intervention on the axilla. If AxLND is limited to I and II level, the involvement of the III level up to 31% of the cases seems to require some additional treatment, while the omission in selected cases needs further investigation.

A nomogram to predict non-sentinel lymph node metastasis in patients with initial cN+ breast cancer that downstages to cN0 after neoadjuvant chemotherapy

BACKGROUND AND OBJECTIVES

This study mainly explored the factors that influence non-sentinel lymph node (NSLN) metastasis in patients with breast cancer (BC) whose axillary lymph nodal status changed from clinically node positive (cN+) to clinically node negative (cN0) after neoadjuvant chemotherapy (NAC).

METHODS

We retrospectively analyzed the clinicopathological factors affecting NSLN metastasis in a total of 179 patients with cN+ BC downstaged to cN0 (120 in the training set and 59 in the validation set) who underwent both sentinel lymph node (SLN) biopsy and axillary lymph node dissection following NAC.

RESULTS

Among 179 patients enrolled, the overall NSLN metastatic rate was 24.0% (95% confidence interval [CI]: 17.7%-30.3%). In multivariate logistic regression analysis, the number of positive SLNs achieving a pathological complete remission of the breast and clinical node staging was independent predictors of NSLN metastasis. A nomogram was established based on these factors and displayed a good discriminatory capability, with an area under the curve of 0.919 (95% CI: 0.865-0.973) for the training set and 0.900 (95% CI: 0.812-0.988) for the validation set and its clinical utility was confirmed by the decision curve analysis.

CONCLUSIONS

The nomogram established showed the ability to predict NSLN metastases in patients with initial cN+ BC that downstaged to cN0 after NAC.

A New Model Incorporating Axillary Ultrasound After Neoadjuvant Chemotherapy to Predict Non-Sentinel Lymph Node Metastasis in Invasive Breast Cancer

Purpose

Few models with good discriminative power have been introduced to predict the risk of non-sentinel lymph node (non-SLN) metastasis in breast cancer after neoadjuvant chemotherapy (NAC). We aimed to develop a new and simple model for predicting the probability of non-SLN metastasis in breast cancer and facilitate the selection of patients who could avoid unnecessary axillary lymph node dissection following NAC.

Patients and Methods

A total of 298 patients diagnosed with invasive breast cancer, who underwent SLN biopsy after completing NAC and subsequently breast surgery, were included and classified into the training set (n=228) and testing set (n=70). Univariate and multivariate analyses were used to select factors that could be determined prior to breast surgery and significantly correlated with non-SLN metastasis in the training set. A logistic regression model was developed based on these factors and validated in the testing set.

Results

Nodal status before NAC, post-NAC axillary ultrasound status, SLN number, and SLN metastasis number were independent predictors of non-SLN metastases in breast cancer after NAC. A predictive model based on these factors yielded an area under the curve of 0.838 (95% confidence interval: 0.774-0.902, p< 0.001) in the training set. When applied to the testing set, this model yielded an area under the curve of 0.808 (95% confidence interval: 0.609-1.000, p= 0.003).

Conclusion

A new and simple model, which incorporated factors that could be determined prior to breast surgery, was developed to predict non-SLN metastasis in invasive breast cancer following NAC. Although this model performed excellently in internal validation, it requires external validation before it can be widely utilized in the clinical setting.

Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics

BACKGROUND

Lymphovascular invasion (LVI) status facilitates the selection of optimal therapeutic strategy for breast cancer patients, but in clinical practice LVI status is determined in pathological specimens after resection.

PURPOSE

To explore the use of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI)-based radiomics for preoperative prediction of LVI in invasive breast cancer.

STUDY TYPE

Prospective.

POPULATION

Ninety training cohort patients (22 LVI-positive and 68 LVI-negative) and 59 validation cohort patients (22 LVI-positive and 37 LVI-negative) were enrolled.

FIELD STRENGTH/SEQUENCE

1.5 T and 3.0 T, T₁ -weighted DCE-MRI.

ASSESSMENT

Axillary lymph node (ALN) status for each patient was evaluated based on MR images (defined as MRI ALN status), and DCE semiquantitative parameters of lesions were calculated. Radiomic features were extracted from the first postcontrast DCE-MRI. A radiomics signature was constructed in the training cohort with 10-fold cross-validation. The independent risk factors for LVI were identified and prediction models for LVI were developed. Their prediction performances and clinical usefulness were evaluated in the validation cohort.

STATISTICAL TESTS

Mann-Whitney U-test, chi-square test, kappa statistics, least absolute shrinkage and selection operator (LASSO) regression, logistic regression, receiver operating characteristic (ROC) analysis, DeLong test, and decision curve analysis (DCA).

RESULTS

Two radiomic features were selected to construct the radiomics signature. MRI ALN status (odds ratio, 10.452; P < 0.001) and the radiomics signature (odds ratio, 2.895; P = 0.031) were identified as independent risk factors for LVI. The value of the area under the curve (AUC) for a model combining both (0.763) was higher than that for MRI ALN status alone (0.665; P = 0.029) and similar to that for the radiomics signature (0.752; P = 0.857). DCA showed that the combined model added more net benefit than either feature alone.

DATA CONCLUSION

The DCE-MRI-based radiomics signature in combination with MRI ALN status was effective in predicting the LVI status of patients with invasive breast cancer before surgery.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:847-857.