New models and online calculator for predicting non-sentinel lymph node status in sentinel lymph node positive breast cancer patients. BMC Cancer. 2008;8:66. [PMID: 18315887 PMCID: PMC2311316 DOI: 10.1186/1471-2407-8-66]

Correlation between sentinel lymph node biopsy and non-sentinel lymph node metastasis in patients with cN0 breast carcinoma: comparison of invasive ductal carcinoma and invasive lobular carcinoma

BACKGROUND

Some studies have suggested that axillary lymph node dissection (ALND) can be avoided in women with cN0 breast cancer with 1-2 positive sentinel nodes (SLNs). However, these studies included only a few patients with invasive lobular carcinoma (ILC), so the validity of omitting ALDN in these patients remains controversial. This study compared the frequency of non-sentinel lymph nodes (non-SLNs) metastases in ILC and invasive ductal carcinoma (IDC).

MATERIALS METHODS

Data relating to a total of 2583 patients with infiltrating breast carcinoma operated at our institution between 2012 and 2023 were retrospectively analyzed: 2242 (86.8%) with IDC and 341 (13.2%) with ILC. We compared the incidence of metastasis to SLNs and non-SLNs between the ILC and IDC cohorts and examined factors that influenced non-SLNs metastasis.

RESULTS

SLN biopsies were performed in 315 patients with ILC and 2018 patients with IDC. Metastases to the SLNs were found in 78/315 (24.8%) patients with ILC and in 460 (22.8%) patients with IDC (p = 0.31). The incidence of metastases to non-SLNs was significantly higher (p = 0.02) in ILC (52/78-66.7%) compared to IDC (207/460 - 45%). Multivariate analysis showed that ILC was the most influential predictive factor in predicting the presence of metastasis to non-SLNs.

CONCLUSIONS

ILC cases have more non-SLNs metastases than IDC cases in SLN-positive patients. The ILC is essential for predicting non-SLN positivity in macro-metastases in the SLN. The option of omitting ALND in patients with ILC with 1-2 positive SLNs still requires further investigation.

A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer

BACKGROUND

It has been reported that patients with early breast cancer with 1-2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection.

PURPOSE

To develop the potential of machine learning based on multiparametric magnetic resonance imaging (MRI) and clinical factors for predicting the risk of NSLN metastasis in breast cancer.

MATERIAL AND METHODS

This retrospective study included 144 patients with 1-2 positive sentinel lymph node breast cancer. Multiparametric MRI morphologic findings and the detailed demographical characteristics of the primary tumor and axillary lymph node were extracted. The logistic regression, support vector classification, extreme gradient boosting, and random forest algorithm models were established to predict the risk of NSLN metastasis. The prediction efficiency of a machine-learning-based model was evaluated. Finally, the relative importance of each input variable was analyzed for the best model.

RESULTS

Of the 144 patients, 80 (55.6%) developed NSLN metastasis. A total of 24 imaging features and 14 clinicopathological features were analyzed. The extreme gradient boosting algorithm had the strongest prediction efficiency with an area under curve of 0.881 and 0.781 in the training set and test set, respectively. Five main factors for the metastasis of NSLN were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age.

CONCLUSION

The machine-learning model incorporating multiparametric MRI features and clinical factors can predict NSLN metastasis with high accuracy for breast cancer and provide predictive information for clinical protocol.

Top 100 cited classical articles in sentinel lymph nodes biopsy for breast cancer

Background

The sentinel lymph node biopsy (SLNB) takes on a critical significance in breast cancer surgery since it is the gold standard for assessing axillary lymph node (ALN) metastasis and determining whether to perform axillary lymph node dissection (ALND). A bibliometric analysis is beneficial to visualize characteristics and hotspots in the field of sentinel lymph nodes (SLNs), and it is conducive to summarizing the important themes in the field to provide more insights into SLNs and facilitate the management of SLNs.

Materials and methods

Search terms relating to SLNs were aggregated and searched in the Web of Science core collection database to identify the top 100 most cited articles. Bibliometric tools were employed to identify and analyze publications for annual article volume, authors, countries, institutions, keywords, as well as hotspot topics.

Results

The period was from 1998 to 2018. The total number of citations ranged from 160 to 1925. LANCET ONCOLOGY and JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION were the top two journals in which the above articles were published. Giuliano, AE was the author with the highest number of articles in this field with 15. EUROPEAN INST ONCOL is the institution with the highest number of publications, with 35 articles. Hotspots include the following 4 topics, false-negative SLNs after neoadjuvant chemotherapy; prediction of metastatic SLNs; quality of life and postoperative complications; and lymphography of SLNs.

Conclusion

This study applies bibliometric tools to analyze the most influential literature, the top 100 cited articles in the field of SLNB, to provide researchers and physicians with research priorities and hotspots.

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT

Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.

Network pharmacology and bioinformatics were used to construct a prognostic model and immunoassay of core target genes in the combination of quercetin and kaempferol in the treatment of colorectal cancer

Purpose: CRC is a malignant tumor seriously threatening human health. Quercetin and kaempferol are representative components of traditional Chinese medicine (TCM). Previous studies have shown that both quercetin and kaempferol have antitumor pharmacological effects, nevertheless, the underlying mechanism of action remains unclear. To explore the synergy and mechanism of quercetin and kaempferol in colorectal cancer. Methods: In this study, network pharmacology, and bioinformatics are used to obtain the intersection of drug targets and disease genes. Training gene sets were acquired from the TCGA database, acquired prognostic-related genes by univariate Cox, multivariate Cox, and Lasso-Cox regression models, and validated in the GEO dataset. We also made predictions of the immune function of the samples and used molecular docking to map a model for binding two components to prognostic genes. Results: Through Lasso-Cox regression analysis, we obtained three models of drug target genes. This model predicts the combined role of quercetin and kaempferol in the treatment and prognosis of CRC. Prognostic genes are correlated with immune checkpoints and immune infiltration and play an adjuvant role in the immunotherapy of CRC. Conclusion: Core genes are regulated by quercetin and kaempferol to improve the patient's immune system and thus assist in the treatment of CRC.

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.

Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients: A retrospective analysis

Purpose

The aim of this study was to develop a nomogram for predicting positive non-sentinel lymph nodes (non-SLNs) in positive SLN breast cancer patients and validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram for non-SLN metastasis in Chinese patients.

Methods

The pathological features of 2,561 breast cancer patients were retrospectively reviewed, and the patients were divided into training and validation cohorts. Positive non-SLN predictors were identified using univariate and multivariate analyses and used to construct the nomogram. In patients with positive SLNs, the MSKCC nomogram was used to calculate the probability of non-SLN metastasis. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of this model and the MSKCC nomogram.

Results

According to multivariate logistic regression analysis, the number of positive and negative SLNs, tumor stage, lymphovascular invasion, perineural invasion, and extracapsular extension were independent predictive factors for non-SLN metastasis and were selected to establish the nomogram for predicting positive non-SLNs. This nomogram performed favorably in predicting positive non-SLNs, with AUCs of 0.765 and 0.741 for the training and validation cohorts, respectively. The MSKCC nomogram predicted non-SLN metastasis with an AUC of 0.755.

Conclusion

A nomogram was developed and validated to assist clinicians in evaluating the likelihood of positive non-SLN. For Chinese patients with a known ER status before surgery, the MSKCC nomogram can be used to predict non-SLN metastases.

Optimized Radiomics Nomogram Based on Automated Breast Ultrasound System: A Potential Tool for Preoperative Prediction of Metastatic Lymph Node Burden in Breast Cancer

Background

Axillary lymph node dissection (ALND) can be safely avoided in women with T1 or T2 primary invasive breast cancer (BC) and one to two metastatic sentinel lymph nodes (SLNs). However, cancellation of ALND based solely on SLN biopsy (SLNB) may lead to adverse outcomes. Therefore, preoperative assessment of LN tumor burden becomes a new focus for ALN status.

Objective

This study aimed to develop and validate a nomogram incorporating the radiomics score (rad-score) based on automated breast ultrasound system (ABUS) and other clinicopathological features for evaluating the ALN status in patients with early-stage BC preoperatively.

Methods

Totally 354 and 163 patients constituted the training and validation cohorts. They were divided into ALN low burden (<3 metastatic LNs) and high burden (≥3 metastatic LNs) based on the histopathological diagnosis. The radiomics features of the segmented breast tumor in ABUS images were extracted and selected to generate the rad-score of each patient. These rad-scores, along with the ALN burden predictors identified from the clinicopathologic characteristics, were included in the multivariate analysis to establish a nomogram. It was further evaluated in the training and validation cohorts.

Results

High ALN burdens accounted for 11.2% and 10.8% in the training and validation cohorts. The rad-score for each patient was developed based on 7 radiomics features extracted from the ABUS images. The radiomics nomogram was built with the rad-score, tumor size, US-reported LN status, and ABUS retraction phenomenon. It achieved better predictive efficacy than the nomogram without the rad-score and exhibited favorable discrimination, calibration and clinical utility in both cohorts.

Conclusion

We developed an ABUS-based radiomics nomogram for the preoperative prediction of ALN burden in BC patients. It would be utilized for the identification of patients with low ALN burden if further validated, which contributed to appropriate axillary treatment and might avoid unnecessary ALND.

Anatomical site and size of sentinel lymph node metastasis predicted additional axillary tumour burden and breast cancer survival

AIMS

Sentinel lymph node (SLN) biopsy is the current standard assessment for tumour burden in axillary lymph node (ALN). However, not all SLN+ patients have ALN metastasis. The prognostic implication of SLN features is not clear. We aimed to evaluate predictive factors for ALN metastasis and the clinical value of SLN features.

METHODS AND RESULTS

A total of 228 SLN+ and 228 SLN- (with matched year and grade) cases were included. Clinicopathological features in SLN, ALN and primary tumours, treatment data and survival data were analysed according to ALN status and outcome. Except for larger tumour size and the presence of LVI (both P < 0.001), no significant differences were found in SLN- and SLN+ cases. Only 31.8% of SLN+ cases with ALN dissection had ALN metastasis. The presence of macrometastases (MaM), extranodal extension (ENE), deeper level of tumour invasion in SLN and more SLN+ nodes were associated with ALN metastasis (P ≤ 0.025). Moreover, isolated tumour cells (ITC) and level of tumour invasion in SLN were independent adverse prognostic features for disease-free survival and breast cancer-specific survival, respectively. Interestingly, cases with ITC located in the subcapsular region have better survival than those in cortex (OS: χ²  = 4.046, P = 0.044).

CONCLUSIONS

Our study identified features in SLN, i.e. the level of tumour invasion at SLN and tumour size in SLN as useful predictors for both ALN metastasis and breast cancer outcome. The presence of ITC, particularly those with a deeper invasion in SLN, portended a worse prognosis. Proper attention should be taken for their management.

A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients

Background

Sentinel lymph node biopsy (SLNB) is the standard treatment for breast cancer patients with clinically negative axilla. However, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node (SLN) positive patients. Clinical data reveals about 40-75% of patients without non-sentinel lymph node (NSLN) metastasis after ALND. Unnecessary ALND increases the risk of complications and detracts from quality of life. In this study, we expect to develop a nomogram based on genotypic and clinicopathologic factors to predict the risk of NSLN metastasis in SLN-positive Chinese women breast cancer patients.

Methods

This retrospective study collected data from 1,879 women breast cancer patients enrolled from multiple centers. Genotypic features contain 96 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, therapy and prognosis. SNP genotyping was identified by the quantitative PCR detection platform. The genetic features were divided into two clusters by the mutational stability. The normalized polygenic risk score (PRS) was used to evaluate the combined effect of each SNP cluster. Recursive feature elimination (RFE) based on linear discriminant analysis (LDA) was adopted to select the most useful predictive features, and RFE based on support vector machine (SVM) was used to reduce the number of SNPs. Multivariable logistic regression models (i.e., nomogram) were built for predicting NSLN metastasis. The predictive abilities of three types of model (based on only clinicopathologic information, the integrated clinicopathologic and all SNPs information, and integrated clinicopathologic and significant SNPs information) were compared. Internal and external validations were performed and the area under ROC curves (AUCs) as well as a series of evaluation indicators were assessed.

Results

229 patients underwent SLNB followed by ALND and without any neo-adjuvant therapy, 79 among them (34%) had a positive axillary NSLN metastasis. The LDA-RFE identified the characteristics including lymphovascular invasion, number of positive SLNs, number of negative SLNs and two SNP clusters as significant predictors of NSLN metastasis. Furthermore, the SVM-RFE selected 29 significant SNPs in the prediction of NSLN metastasis. In internal validation, the median AUCs of the clinical and all SNPs combining model, the clinical and 29 significant SNPs combining model, and the clinical model were 0.837, 0.795 and 0.708 respectively. Meanwhile, in external validation, the AUCs of the three models were 0.817, 0.815 and 0.745 respectively.

Conclusion

We present a new nomogram by combining genotypic and clinicopathologic factors to achieve higher sensitivity and specificity comparing with traditional clinicopathologic factors to predict NSLN metastasis in Chinese women breast cancer. It is recommended that more validations are required in prospective studies among different patient populations.

Predictive nomogram based on serum tumor markers and clinicopathological features for stratifying lymph node metastasis in breast cancer

BACKGROUND

This study was aimed to establish the nomogram to predict patients' axillary node status by using patients' clinicopathological and tumor characteristic factors.

METHODS

A total of 705 patients with breast cancer were enrolled in this study. All patients were randomly divided into a training group and a validation group. Univariate and multivariate ordered logistic regression were used to determine the predictive ability of each variable. A nomogram was performed based on the factors selected from logistic regression results. Receiver operating characteristic curve (ROC) analysis, calibration plots and decision curve analysis (DCA) were used to evaluate the discriminative ability and accuracy of the models.

RESULTS

Logistic regression analysis demonstrated that CEA, CA125, CA153, tumor size, vascular-invasion, calcification, and tumor grade were independent prognostic factors for positive ALNs. Integrating all the predictive factors, a nomogram was successfully developed and validated. The C-indexes of the nomogram for prediction of no ALN metastasis, positive ALN, and four and more ALN metastasis were 0.826, 0.706, and 0.855 in training group and 0.836, 0.731, and 0.897 in validation group. Furthermore, calibration plots and DCA demonstrated a satisfactory performance of our nomogram.

CONCLUSION

We successfully construct and validate the nomogram to predict patients' axillary node status by using patients' clinicopathological and tumor characteristic factors.

A Combined Nomogram Model to Preoperatively Predict Positive Sentinel Lymph Biopsy for Breast Cancer In Iranian Population

Background

Axillary dissection in breast cancer provides useful information on the degree of axillary nodule involvement, which serves as a reliable indicator for the prognosis and staging of breast cancer in patients. The aim of this study was to develop and validate the nomogram model by combining prognostic factors and clinical features to predict the node status of preoperative breast guard positive node cancer.

Materials and Methods

Subjects consisted of patients referring to hospitals with the diagnosis of breast cancer. Patients were allowed to substitute molecular subtypes with data on breast cancer diagnosis and prognosis as well as sentinel node status. The bootstrap review was used for internal validation. The predicted performance was evaluated based on the area under the receiver operating characteristic curve. According to the logistic regression analysis, the nomograms reported material strength between predictors and final status reliability.

Results

1172 patients participated in the study, of whom only 539 patients had axillary lymph node involvement. The subtype, family history, calcification, and necrosis were not significantly related to axillary lymph node involvement. Tumor size, histological type, and lymphovascular invasion in multivariate logistic regression were significantly and directly correlated with axillary lymph node involvement.

Conclusion

Nomograms, depending on the population, help make decisions to prevent axillary surgery. It seems that the prediction model presented in this study, based on the results of the neuromography, can help surgeons make a more informed decision on underarm surgery. Moreover, in some cases, their surgical program will be informed by accurate medical care and preclusion of major surgeries such as ALND.

Factors Predicting Positive Sentinel Lymph Node Biopsy in Clinically Node-Negative Breast Cancer

Purpose

Sentinel lymph node (SLN) biopsy (SLNB) is the standard tool to stage the axilla of breast cancer patients. This study aimed to identify the predictors of positive SLNB in patients with clinically node-negative breast cancer.

Patients and Methods

A retrospective, single-institution cohort of patients with early-stage breast cancer without clinically identifiable axillary lymphadenopathy was chosen from January 2010 to December 2018. Logistic regression was used to identify possible predictors of positive SLNB.

Results

Four hundred and seventy patients were identified; their mean age was 50±11 years. Most patients had the following characteristics: invasive ductal carcinoma (n=382, 81.3%), unilateral tumor (n=461, 98.1%), unifocal disease (n=351, 74.7%), intermediate grade (n=276, 59.0%), and estrogen and progesterone receptor positivity with human epidermal growth factor receptor 2 negativity (n=305, 64.9%). The mean size of the breast mass was 2.3±1.5 cm. SLNB was positive in 128 (27.2%) cases. The mean number of SLNs was 2±1.2. Axillary lymph node dissection was performed in 109 patients. The mean number of lymph nodes removed was 15±6. In 66 (60.6%) of the 109 patients with metastatic axillary nodes, only the SLNs were found to be positive. The number of SLNs, tumor size, tumor grade, receptor status, prominent axillary lymph nodes, and lymphovascular invasion predicted positive SLNB (P = 0.01, 0.03, 0.03, and 0.04 and <0.001 and <0.001, respectively).

Conclusion

Our results suggest that a number of histopathological and radiological characteristics of breast cancer can predict SLNB positivity in clinically node-negative breast cancer patients.

Factors predicting upstaging from clinical N0 to pN2a/N3a in breast cancer patients

BACKGROUND

With sentinel node metastasis in breast cancer (BC) patients, axillary lymph node (ALN) dissection is often omitted from cases with breast-conserving surgery. Omission of lymph node dissection reduces the invasiveness of surgery to the patient, but it also obscures the number of metastases to non-sentinel nodes. The possibility of finding ≥ 4 lymph nodes (pN2a/pN3a) preoperatively is important given the ramifications for postoperative treatment.

AIM

To search for clinicopathological factors that predicts upstaging from N0 to pN2a/pN3a.

METHODS

Patients who were sentinel lymph node (SLN)-positive and underwent ALN dissection between September 2007 and August 2018 were selected by retrospective chart review. All patients had BC diagnosed preoperatively as N0 with axillary evaluation by fluorodeoxyglucose (FDG) positron emission tomography/computed tomography and ultrasound (US) examination. When suspicious FDG accumulation was found in ALN, the presence of metastasis was reevaluated by second US. We examined predictors of upstaging from N0 to pN2a/pN3a.

RESULTS

Among 135 patients, we identified 1-3 ALNs (pN1) in 113 patients and ³4 ALNs (pN2a/pN3a) in 22 patients. Multivariate analysis identified the total number of SLN metastasis, the maximal diameter of metastasis in the SLN (SLNDmax), and FDG accumulation of ALN as predictors of upstaging to pN2a/pN3a.

CONCLUSION

We identified factors involved in upstaging from N0 to pN2a/pN3a. The SLNDmax and number of SLN metastasis are predictors of ≥ 4 ALNs (pN2a/pN3a) and predictors of metastasis to non-sentinel nodes, which have been reported in the past. Attention should be given to axillary accumulations of FDG, even when faint.

A novel nomogram for decision-making assistance on exemption of axillary lymph node dissection in T1–2 breast cancer with only one sentinel lymph node metastasis

Background

T1–2 breast cancer patients with only one sentinel lymph node (SLN) metastasis have an extremely low non-SLN (NSLN) metastatic rate and are favorable for axillary lymph node dissection (ALND) exemption. This study aimed to construct a nomogram-based preoperative prediction model of NSLN metastasis for such patients, thereby assisting in preoperatively selecting proper surgical procedures.

Methods

A total of 729 T1–2 breast cancer patients with only one SLN metastasis undergoing sentinel lymph node biopsy and ALND were retrospectively selected from Harbin Medical University Cancer Hospital between January 2013 and December 2020, followed by random assignment into training (n=467) and validation cohorts (n=262). A nomogram-based prediction model for NSLN metastasis risk was constructed by incorporating the independent predictors of NSLN metastasis identified from multivariate logistic regression analysis in the training cohort. The performance of the nomogram was evaluated by the calibration curve and the receiver operating characteristic (ROC) curve. Finally, decision curve analysis (DCA) was used to determine the clinical utility of the nomogram.

Results

Overall, 160 (21.9%) patients had NSLN metastases. Multivariate analysis in the training cohort revealed that the number of negative SLNs (OR: 0.98), location of primary tumor (OR: 2.34), tumor size (OR: 3.15), and lymph-vascular invasion (OR: 1.61) were independent predictors of NSLN metastasis. The incorporation of four independent predictors into a nomogram-based preoperative estimation of NSLN metastasis demonstrated a satisfactory discriminative capacity, with a C-index and area under the ROC curve of 0.740 and 0.689 in the training and validation cohorts, respectively. The calibration curve showed good agreement between actual and predicted NSLN metastasis risks. Finally, DCA revealed the clinical utility of the nomogram.

Conclusion

The nomogram showed a satisfactory discriminative capacity of NSLN metastasis risk in T1–2 breast cancer patients with only one SLN metastasis, and it could be used to preoperatively estimate NSLN metastasis risk, thereby facilitating in precise clinical decision-making on the selective exemption of ALND in such patients.

Risk factors of non-sentinel lymph node metastasis in 443 breast cancer patients with sentinel lymph node-positive

Axillary lymph node dissection is the standard surgical procedure for breast cancer patients with sentinel lymph node (SLN) positive. In clinical practice, axillary lymph node dissection may be an unnecessary treatment for some breast cancer patients with non-sentinel lymph node (NSLN) negative. The aim of this study was to analyze the risk factors of NSLN metastasis in breast cancer patients with SLN positive. Four hundred fifty-six clinical early stage breast cancer patients with SLN positive were collected and analyzed in the oncological surgery department of Fujian Provincial Hospital during 2013 to 2018. All these patients underwent surgical treatment. The average age and tumor size of 443 patients with SLN positive breast cancer were (49.8 ± 10.8) years and (2.42 ± 0.94) cm. Univariate analysis showed that the size of primary tumor, the number of positive SLN, the number of negative SLN, the ratio of positive SLNs, and the type of metastases in SLN were the influencing factors of NSLN metastasis. Multivariate regression analysis showed that primary tumor size T > 2 cm (P < .001, OR = 2.609), the positive number of SLNs ≥3 (P = .002, OR = 5.435), the ratio of positive SLNs ≥ 50% (P = .017, OR = 1.770), and SLN macrometastases (P < 0.001, OR = 16.099) were independent risk factors for NSLN metastasis. Combined with the 4 independent risk factors, the area under the curve to predict NSLN metastasis was 0.747 > 0.7. For clinical early breast cancer with positive SLN, primary tumor size T > 2 cm,the positive number of SLNs ≥ 3, the ratio of positive SLNs ≥ 50%, and SLN macrometastases could predict NSLN metastasis well, and guide surgery to avoid overtreatment.

Can axillary lymphadenectomy be avoided in breast cancer with positive sentinel lymph node biopsy? Predictors of non-sentinel lymph node metastasis

PURPOSE

Completion axillary lymph node dissection (cALND) can currently be avoided in those patients with a low tumor load (LTL) and/or a low-risk profile that tested with positive sentinel lymph node biopsy (SLNB). Our objective is to identify prognostic factors that significantly influence axillary lymph node involvement to identify patients who could benefit from surgery without axillary lymphadenectomy.

METHODS

This is an observational retrospective study of consecutive patients diagnosed and operated of breast cancer between 2000 and 2014 at University Hospital La Ribera (UHR).

RESULTS

The size of the sample was 1641 patients, from which 1174 underwent SLNB. In the multivariate analysis, we objectify a raise of risk of positive sentinel lymph node (SLN) up to 5.2% for every millimeter of increase. The risk of positive SLNB when showing lymphovascular invasion seems to be 2.80 times greater but becomes lower when SLN involvement appears in luminal A, luminal B and triple-negative types, regarding HER2. In case of triple negatives, the difference is statistically significant. 16.7% present affected additional lymph nodes. The proportion of patients with affected additional lymph nodes increase dramatically above OSNA values of 12,000 copies/μl of CK19 mRNA and it depends on tumor size and lymphovascular infiltration.

CONCLUSIONS

Tumors smaller than 5 cm whose OSNA SLNB analysis is less than 12,000 copies/μl of CK19 mRNA have a low chance to develop additional affected lymph nodes, thus cALND can be avoided.

Application of the Machine-Learning Model to Improve Prediction of Non-Sentinel Lymph Node Metastasis Status Among Breast Cancer Patients

Background

Performing axillary lymph node dissection (ALND) is the current standard option after a positive sentinel lymph node (SLN). However, whether 1–2 metastatic SLNs require ALND is debatable. The probability of metastasis in non-sentinel lymph nodes (NSLNs) can be calculated using nomograms. In this study, we developed an individualized model using machine-learning (ML) methods to select potential variables, which influence NSLN metastasis.

Materials and Methods

Cohorts of patients with early breast cancer who underwent SLN biopsy and ALND between 2012 and 2021 were created (training cohort, N 157 and validation cohort, N 58) for the development of the nomogram. Three ML methods were trained in the training set to create a strong predictive model. Finally, the multiple iterations of the least absolute shrinkage and selection operator regression method were used to determine the variables associated with NSLN status.

Results

Four independent variables (positive SLN number, absence of lymph node hilum, lymphovascular invasion (LVI), and total number of SLNs harvested) were combined to generate the nomogram. The area under the receiver operating characteristic curve (AUC) value of 0.759 was obtained in the entire set. The AUC values for the training set and the test set were 0.782 and 0.705, respectively. The Hosmer-Lemeshow test of the model fit accuracy was identified with p = 0.759.

Conclusion

This study developed a nomogram that incorporates ultrasound (US)-related variables using the ML method and serves to clinically predict the non-metastatic status of NSLN and help in the selection of the appropriate treatment option.

MRI-Based Radiomics Nomogram: Prediction of Axillary Non-Sentinel Lymph Node Metastasis in Patients With Sentinel Lymph Node-Positive Breast Cancer

Background

Overtreatment of axillary lymph node dissection (ALND) may occur in patients with axillary positive sentinel lymph node (SLN) but negative non-SLN (NSLN). Developing a magnetic resonance imaging (MRI)-based radiomics nomogram to predict axillary NSLN metastasis in patients with SLN-positive breast cancer could effectively decrease the probability of overtreatment and optimize a personalized axillary surgical strategy.

Methods

This retrospective study included 285 patients with positive SLN breast cancer. Fifty five of them had metastatic NSLNs and 230 had non-metastatic NSLNs. MRI-based radiomic features of primary tumors were extracted and MRI morphologic findings of the primary tumor and axillary lymph nodes were assessed. Four models, namely, a radiomics signature, an MRI-clinical nomogram, and two MRI-clinical-radiomics nomograms were established based on MRI morphologic findings, clinicopathologic characteristics, and MRI-based radiomic features to predict the NSLN status. The optimal predictors in each model were selected using the 5-fold cross-validation (CV) method. Their predictive performances were determined by the receiver operating characteristic (ROC) curves analysis. The area under the curves (AUCs) of different models was compared by the Delong test. Their discrimination capability, calibration curve, and clinical usefulness were also assessed.

Results

The 5-fold CV analysis showed that the AUCs ranged from 0.770 to 0.847 for the radiomics signature, from 0.720 to 0.824 for the MRI-clinical nomogram, from 0.843 to 0.932 for the MRI-clinical-radiomics nomogram. The optimal predictive factors in the radiomics signature, MRI-clinical nomogram, and MRI-clinical-radiomics nomogram were one texture feature of diffusion-weighted imaging (DWI), two clinicopathologic features together with one MRI morphologic finding, and the DWI-based texture feature together with the two clinicopathologic features plus the one MRI morphologic finding, respectively. The MRI-clinical-radiomics nomogram with CA 15-3 included achieved the highest AUC compared with the radiomics signature (0.868 vs. 0.806, P <0.001) and MRI-clinical nomogram (0.868 vs. 0.761; P <0.001). In addition, the MRI-clinical-radiomics nomogram without CA 15-3 showed a higher performance than that of the radiomics signature (AUC, 0.852 vs. 0.806, P = 0.016) and the MRI-clinical nomogram (AUC, 0.852 vs. 0.761, P = 0.007). The MRI-clinical-radiomics nomograms showed good discrimination and good calibration. Decision curve analysis demonstrated that the MRI-clinical-radiomics nomograms were clinically useful.

Conclusion

The MRI-clinical-radiomics nomograms developed in our study showed high predictive performance, which can be used to predict the axillary NSLN status in SLN-positive breast cancer patients before surgery.

Sentinel Lymph Node Positive Rate Predicts Non-Sentinel Lymph Node Metastasis in Breast Cancer

BACKGROUND

To investigate retrospectively an association between the number of metastatic sentinel lymph nodes (SLNs) per total number of SLNs per patient (i.e., the SLN positive rate, or SLN-PR) and non-SLN metastasis in breast cancer.

METHODS

A large population (n = 2250) underwent SLN dissection from January 1, 2014 to January 1, 2020; 627 (27.87%) had at least one positive SLN (SLN⁺). Among these, 283 underwent axillary lymph node (ALN) dissection, and formed the test group. Four external validation groups comprised 43 patients treated in 2019. SLN mappings were examined using methylene blue and indocyanine green. Lymph node ultrasound, SLN-PR, and pathological characteristics were compared between patients with and without non-SLN metastasis. An SLN-PR cutoff value was calculated using receiver operating characteristic (ROC) curves. Associations between clinicopathological variables and SLN-PR with non-SLN metastasis were analyzed by multivariate logistic regression model.

RESULTS

The median age was 47 years (IQR: 42-56 y). The median number of resected SLNs was 4. Patients with positive non-SLNs (126/283, 44.52%) had a median of 2 positive node. SLN-PR > 0.333 was a risk factor for non-SLN positivity (area under the ROC curve, 0.726); and carried significantly higher risk of non-SLN metastasis (P < 0.001). This was validated in the external group.

CONCLUSIONS

SLN-PR > 0.333 was associated with greater risk of non-SLN metastasis. This provides a reference to non-SLN metastasis in patients with SLN metastasis, an indication for ALN dissection and choice of adjuvant treatment.

Macrometastasis at selective lymph node biopsy: A practical going-for-the-one clinical scoring system to personalize decision making

BACKGROUND

Axillary sentinel lymph node biopsy (SLNB) is standard treatment for patients with clinically and pathological negative lymph nodes. However, the role of completion axillary lymph node dissection (cALND) following positive sentinel lymph node biopsy (SLNB) is debated.

AIM

To identify a subgroup of women with high axillary tumor burden undergoing SLNB in whom cALND can be safely omitted in order to reduce the risk of long-term complications and create a Preoperative Clinical Risk Index (PCRI) that helps us in our clinical practice to optimize the selection of these patients.

METHODS

Patients with positive SLNB who underwent a cALND were included in this study. Univariate and multivariate analysis of prognostic and predictive factors were used to create a PCRI for safely omitting cALND.

RESULTS

From May 2007 to April 2014, we performed 1140 SLN biopsies, of which 125 were positive for tumor and justified to practice a posterior cALND. Pathologic findings at SLNB were micrometastases (mic) in 29 cases (23.4%) and macrometastasis (MAC) in 95 cases (76.6%). On univariate analysis of the 95 patients with MAC, statistically significant factors included: age, grade, phenotype, histology, lymphovascular invasion, lymph-node tumor size, and number of positive SLN. On multivariate analysis, only lymph-node tumor size (≤ 20 mm) and number of positive SLN (> 1) retained significance. A numerical tool was created giving each of the parameters a value to predict preoperatively which patients would not benefit from cALND. Patients with a PCRI ≤ 15 has low probability (< 10%) of having additional lymph node involvement, a PRCI between 15-17.6 has a probability of 43%, and the probability increases to 69% in patients with a PCRI > 17.6.

CONCLUSION

The PCRI seems to be a useful tool to prospectively estimate the risk of nodal involvement after positive SLN and to identify those patients who could omit cALND. Further prospective studies are necessary to validate PCRI clinical generalization.

Omitting completion axillary lymph node dissection after detection of sentinel node micrometastases in breast cancer: first results from the prospective SENOMIC trial

BACKGROUND

Completion axillary lymph node dissection has been abandoned widely among patients with breast cancer and sentinel lymph node micrometastases, based on evidence from prospective RCTs. Inclusion in these trials has been subject to selection bias, with patients undergoing mastectomy being under-represented. The aim of the SENOMIC (omission of axillary lymph node dissection in SENtinel NOde MICrometases) trial was to confirm the safety of omission of axillary lymph node dissection in patients with breast cancer and sentinel lymph node micrometastases, and including patients undergoing mastectomy.

METHODS

The prospective SENOMIC multicentre cohort trial enrolled patients with breast cancer and sentinel lymph node micrometastases who had breast-conserving surgery or mastectomy at one of 23 Swedish hospitals between October 2013 and March 2017. No completion axillary lymph node dissection was performed. The primary endpoint was event-free survival, with a trial accrual target of 452 patients. Survival proportions were based on Kaplan-Meier survival estimates.

RESULTS

The trial included 566 patients. Median follow-up was 38 (range 7-67) months. The 3-year event-free survival rate was 96.2 per cent, based on 26 reported breast cancer recurrences, including five isolated axillary recurrences. The unadjusted 3-year event-free survival rate was higher than anticipated, but differed between patients who had mastectomy and those who underwent breast-conserving surgery (93.8 versus 97.8 per cent respectively; P = 0.011). Patients who underwent mastectomy had significantly worse tumour characteristics. On univariable Cox proportional hazards regression analysis, patients who had mastectomy without adjuvant radiotherapy had a significantly higher risk of recurrence than those who underwent breast-conserving surgery (hazard ratio 2.91, 95 per cent c.i. 1.25 to 6.75).

CONCLUSION

After 3 years, event-free survival was excellent in patients with breast cancer and sentinel node micrometastases despite omission of axillary lymph node dissection. Long-term follow-up and continued enrolment of patients having mastectomy, especially those not receiving adjuvant radiotherapy, are of utmost importance.

Application of OSNA Nomogram in Patients With Macrometastatic Sentinel Lymph Node: A Retrospective Assessment of Accuracy

Introduction:

Almost 50% to 70% of patients who undergo axillary lymph node dissection (ALND) because of a single metastatic sentinel lymph node (SLN) have no further metastatic nodes at the axillary histology. On these grounds, the one-step nucleic acid amplification (OSNA) nomogram was designed and validated. As a mathematical model, calculated through tumor size (expressed in millimeters) and CK19 mRNA copy number, it is thought to predict nonsentinel lymph node (NSLN) status. The aim of the study is to verify the diagnostic accuracy of the OSNA nomogram in a group of patients with macrometastatic SLN, with a retrospective analysis.

Methods:

The OSNA nomogram was retrospectively applied to a group of 66 patients with macrometastatic SLN who underwent ALND. The result of the final histology of the axillary cavity was compared to the nomogram prediction. We calculated the prevalence of NSLN metastasis in patients who underwent ALND, sensitivity and specificity, negative and positive predictive value of the nomogram.

Results:

In patients with macrometastasis in SLN, the prevalence of patients with metastatic NSLN was 45%. The sensitivity of the nomogram was excellent (90%). The specificity was low (36%). Positive predictive value amounted to 54%, while negative predictive value was good (81%).

Conclusions:

These results suggest that the OSNA nomogram is a valid instrument that can help choose the best surgical strategy for the treatment of axillary cavity. The mathematical model is useful to avoid surgery in a selected group of patients because it accurately predicts NSLN status.

Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients with 1-2 positive sentinel lymph node macro-metastases: a retrospective analysis of two independent cohorts

BACKGROUND

It is reported that appropriately 50% of early breast cancer patients with 1-2 positive sentinel lymph node (SLN) micro-metastases could not benefit from axillary lymph node dissection (ALND) or breast-conserving surgery with whole breast irradiation. However, whether patients with 1-2 positive SLN macro-metastases could benefit from ALND remains unknown. The aim of our study was to develop and validate nomograms for assessing axillary non-SLN metastases in patients with 1-2 positive SLN macro-metastases, using their pathological features alone or in combination with STMs.

METHODS

We retrospectively reviewed pathological features and STMs of 1150 early breast cancer patients from two independent cohorts. Best subset regression was used for feature selection and signature building. The risk score of axillary non-SLN metastases was calculated for each patient as a linear combination of selected predictors that were weighted by their respective coefficients.

RESULTS

The pathology-based nomogram possessed a strong discrimination ability for axillary non-SLN metastases, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.727 (95% CI: 0.682-0.771) in the primary cohort and 0.722 (95% CI: 0.653-0.792) in the validation cohort. The addition of CA 15-3 and CEA can significantly improve the performance of pathology-based nomogram in the primary cohort (AUC: 0.773 (0.732-0.815) vs. 0.727 (0.682-0.771), P < 0.001) and validation cohort (AUC: (0.777 (0.713-0.840) vs. 0.722 (0.653-0.792), P < 0.001). Decision curve analysis demonstrated that the nomograms were clinically useful.

CONCLUSION

The nomograms based on pathological features can be used to identify axillary non-SLN metastases in breast cancer patients with 1-2 positive SLN. In addition, the combination of STMs and pathological features can identify patients with patients with axillary non-SLN metastases more accurately than pathological characteristics alone.

Predicting Nonsentinel Lymph Node Metastasis in Breast Cancer: A Multicenter Retrospective Study

BACKGROUND

Sentinel lymph node (SLN) biopsy has been the standard modality for breast cancer patients with clinically node negative disease. In patients who undergo axillary lymph node dissection (ALND) due to SLN metastasis, the harvested nodes (non-SLNs) often contain no metastasis. Here, we evaluated the predictive factors associated with non-SLN metastasis in breast cancer patients.

MATERIALS AND METHODS

This was a retrospective study of patients with operable cT1-3, cN0 invasive breast cancer who underwent SLN biopsy followed by ALND due to SLN metastasis. The clinicopathologic factors and predictive factors of non-SLN metastasis were analyzed. The optimal cutoff for the Ki67 index and the number of positive and negative SLNs that were predictive of non-SLN metastasis were evaluated using receiver operating characteristic curves.

RESULTS

The median number of SLN and non-SLN was 3 and 11, respectively. Of the 150 patients, 52 (35.0%) had metastases in non-SLNs. The optimal cutoffs for the Ki67 index and the number of positive and negative SLNs were of 12%, 2, and 1, respectively. In the univariate analysis, the Ki67 index and the number of positive SLNs≥2 and negative SLNs≤1 were higher in the non-SLN + group than that in the non-SLN - group. The number of negative SLNs was as a predictive factor for non-SLNs metastasis in the multivariate analysis.

CONCLUSIONS

The number of negative SLNs predicts the risk of non-SLN metastasis in breast cancer. When deciding on whether to omit ALND, the number of positive and negative SLNs should be considered.

Sentinel lymph node biopsy with one-step nucleic acid assay relegates the need for preoperative ultrasound-guided biopsy staging of the axilla in patients with early stage breast cancer

Avoiding axillary node clearance in patients with early stage breast cancer and low-burden node-positive axillary disease is an emerging practice. Informing the decision to adopt axillary conservation is examined by comparing routine preoperative axillary staging using ultrasound (AUS) ± AUS biopsy (AUSB) with intraoperative staging using sentinel lymph node biopsy (SLNB) and a one-step nucleic acid cytokeratin-19 amplification assay (OSNA). A single-centre, retrospective cohort study of 1,315 consecutive new diagnoses of breast cancer in 1,306 patients was undertaken in the present study. An AUS ± AUSB was performed on all patients as part of their initial assessment. Patients who had a normal ultrasound (AUS-) or negative biopsy (AUSB-) followed by SLNB with OSNA ± axillary lymph node dissection (ALND), and those with a positive AUSB (AUSB+), were assessed. Tests for association were determined using a χ² and Fisher's Exact test. A total of 266 (20.4%) patients with cT1-3 cN0 staging received 271 AUSBs. Of these, 205 biopsies were positive and 66 were negative. The 684 patients with an AUS-/AUSB-assessment proceeded to SLNB with OSNA. AUS sensitivity and negative predictive value (NPV) were 0.53 [0.44-0.62; 95% confidence interval (CI)] and 0.58 (0.53-0.64, 95% CI), respectively. Using a total tumour load cut-off of 15,000 copies/µl to predict ≥2 macro-metastases, the sensitivity and NPV for OSNA were 0.82 (0.71-0.92, 95% CI) and 0.98 (0.97-0.99, 95% CI) (OSNA vs. AUS P<0.0001). Of the AUSB+ patients, 51% had ≤2 positive nodes following ALND and were potentially over-treated. Where available, SLNB with OSNA should replace AUSB for axillary assessment in cT1-2 cN0 patients with ≤2 indeterminate nodes seen on AUS.

Associated Features with Non-Sentinel Lymph Node Involvement in Early Stage Breast Cancer Patients who Have Positive Macrometastatic Sentinel Lymph Node

Objective

The main goal of this study is to determine the clinico-pathological factors that correlate non-sentinel lymph nodes (LNs) involvement in clinically node negative breast cancer (BC) patients with positive macrometastatic sentinel lymph node (SLN) in order to derive future evidence to define a subgroup where completion axillary lymph node dissection (cALND) might not be recommended.

Materials and Methods

Total 289 SLN biopsies were performed in clinically node negative BC patients between March 2014 and April 2017. Seventy patients who performed cALND due to positive macrometastatic SLN were retrospectively selected and classified into two groups, according to non-SLN involvement (NSLNI). Clinico-pathological features of patients were examined computerized and documentary archives.

Results

Extracapsular extension (ECE) of SLN, number of harvested SLNs, metastatic rate of SLNs, absence of ductal carcinoma in situ (DCIS) and presence of multilocalization were significantly associated with the likelihood of non-SLN involvement after univariate analysis (p<0,05). Absence of DCIS and presence of multilocalization were found to be significant after multivariate analysis.

Conclusion

Careful examination of clinico-pathological features can help to decide avoiding cALND if enough LNs are removed and the rate of SLN metastases is low, particularly in case DCIS accompanying invasive cancer in patients without multi localized tumour.

Axillary Nodal Evaluation in Breast Cancer: State of the Art

Axillary lymph node (LN) metastasis is the most important predictor of overall recurrence and survival in patients with breast cancer, and accurate assessment of axillary LN involvement is an essential component in staging breast cancer. Axillary management in patients with breast cancer has become much less invasive and individualized with the introduction of sentinel LN biopsy (SLNB). Emerging evidence indicates that axillary LN dissection may be avoided in selected patients with node-positive as well as node-negative cancer. Thus, assessment of nodal disease burden to guide multidisciplinary treatment decision making is now considered to be a critical role of axillary imaging and can be achieved with axillary US, MRI, and US-guided biopsy. For the node-positive patients treated with neoadjuvant chemotherapy, restaging of the axilla with US and MRI and targeted axillary dissection in addition to SLNB is highly recommended to minimize the false-negative rate of SLNB. Efforts continue to develop prediction models that incorporate imaging features to predict nodal disease burden and to select proper candidates for SLNB. As methods of axillary nodal evaluation evolve, breast radiologists and surgeons must work closely to maximize the potential role of imaging and to provide the most optimized treatment for patients.

Nomograms for predicting the likelihood of non-sentinel lymph node metastases in breast cancer patients with a positive sentinel node biopsy

BACKGROUND

Breast cancer patients with sentinel lymph node (SLN) metastases may have a low risk of non-SLN metastases. Accurate estimates of the likelihood of additional disease in the non-SLN metastases can avoid many complications mentioned the axillary lymph node dissection (ALND). This study aims to develop a new model based on Chinese real-world patients to ascertain the likelihood of non-SLN metastases in a breast cancer patient with disease-positive SLN, enabling the surgeons to make a better choice of surgical procedures.

METHODS

Out of the 470 patients from CSCO Breast Cancer Database collaborated Group, a proportion of 3 (347 cases): 1 (123 cases) was considered for assigning patients to training and validation groups, respectively. Two training models were created to predict the likelihood of having additional, non-SLN metastases in an individual patient. Training model 1 was created with pathological size of the tumor, pathological type, lymphovascular invasion, the number of positive SLNs/number of total SLNs ratio, and the Her-2 status based on multivariable logistic regression (P < .05). Training model 2 was based on the variables in model 1 and age, estrogen receptor status, progesterone receptor status, Ki-67 count, menopause status.

RESULTS

The area under the receiver operating characteristic (ROC) curve of the training model 1 was 0.754, while the area of training model 2 was 0.766. There was no difference between model 1 and model 2 regarding the ROC curve, P = .243. Next, the validation cohort (n = 123) was developed to confirm the model 1's performance and the ROC curve was 0.703. The nomogram achieved good concordance indexes of 0.754 (95% CI, 0.702-0.807) and 0.703 (95% CI, 0.609-0.796) in predicting the non-SLN metastases in the training and validation cohorts, respectively, with well-fitted calibration curves. The positive and negative predictive values of the nomogram were calculated, resulting in positive values of 59.3% and 48.6% and negative predictive values of 79.7% and 83.0% for the training and validation cohorts, respectively.

CONCLUSION

We developed 2 models that used information commonly available to the surgeon to calculate the likelihood of having non-SLN metastases in an individual patient. The numbers of variables in model 1 were less than in model 2, while model 1 had similar results as model 2 in calculating the likelihood of having non-SLN metastases in an individual patient. Model 1 was more user-friendly nomogram than model 2. Using model 1, the risk for an individual patient having ALND could be determined, which would lead to a rational therapeutic choice.

Intraoperative Prediction Of Non-Sentinel Lymph Node Metastasis Based On The Molecular Assay In Breast Cancer Patients

Purpose

The aim of the study is to construct an intraoperative nomogram for the prediction of non-sentinel lymph node (NSLN) metastasis based on the one-step nucleic acid amplification assay in breast cancer patients.

Methods

A total of 552 patients were enrolled in the training study and 1090 patients were enrolled in the validation study. The nomogram was constructed based on the molecular assay with logistic multivariate regression analysis in the training study and was validated in the validation study.

Results

A novel nomogram model was constructed with the total tumor load, the clinical primary tumor size, the number of positive and negative sentinel lymph nodes. The area under the receiver operating characteristic curve (AUC) of the model was 0.842. The AUC of the model which was sensitive to discern the patients with the stage of pN1 and ≥pN2 was 0.861.

Conclusion

The nomogram model will help to guide the axillary management intraoperatively and precisely confirm the target region of radiotherapy postoperatively.

Proposal for a Histological Staging System of Mammary Carcinomas in Dogs and Cats. Part 2: Feline Mammary Carcinomas

Background: Feline mammary carcinomas (FMCs) are characterized by a high frequency of metastatic spread. The clinical TNM (Tumor, Node, Metastasis) system is used to describe local, regional, and distant tumor extent within the patient, but few publications confirmed its association with survival in cats with FMC. The purpose of this study was to determine if the histological staging system proposed for dogs in part 1 of this article had significant association with prognosis in cats.

Materials and Methods: This retrospective study included 395 female cats with a surgically removed mammary carcinoma, with a 2-year follow-up. Invasiveness (distinction between in situ and invasive FMCs), the pathologic tumor size (pT), lymphovascular invasion (LVI), and the pathologic nodal stage (pN) defined a 5-stage system: Stage 0 (FMCs in situ), Stage I (pT1, LVI–, pN0–pNX), Stage II (pT2, LVI–, pN0–pNX), Stage IIIA (pT1, LVI+ and/or pN+), and Stage IIIB (pT2, LVI+ and/or pN+), where pT1 was ≤20 mm, pT2 was >20 mm, and pNX corresponded to unsampled draining lymph node.

Results: Higher histological stages were associated with reduced disease-free interval, overall survival, and specific survival. For cancer-specific survival, by univariate analysis (p < 0.0001), median survival times and 1-year specific survival rates (1ySSR) were: stage 0 (1484 days; 1ySSR = 85%; N = 55; 14% of the cats), stage I (808 days; 1ySSR = 76%; N = 103; 26%), stage II (377 days; 1ySSR = 51%; N = 56; 14%), stage IIIA (448 days; 1ySSR = 60%; N = 83; 21%), and stage IIIB (207 days; 1ySSR = 29%; N = 98; 25%). The histological stages were also associated with specific survival by multivariate analysis (Hazard Ratio (HR) = 2.72 for stage IIIB, HR = 1.76 for stage IIIA, HR = 1.50 for stage II compared with stage I), independently of Progesterone Receptor expression (HR = 0.34 for PR+ compared with PR– FMCs) and tumor-associated inflammation (HR = 1.33 when moderate to severe compared with absent to mild).

Conclusion: A same histological staging system could be applied in dogs and cats with mammary carcinoma to refine prognosis assessment. In the near future, a preoperative complete tumor clinical staging and treatment based on the published standard of care should be performed in order to better validate the histological staging system here proposed.

Predictors of non-sentinel lymph node metastasis in clinical early stage (cT1-2N0) breast cancer patients with 1-2 metastatic sentinel lymph nodes

OBJECTIVE

The purpose of this study was to determine the risk factors that caused non-sentinel lymph nodes (nonSLNs) metastasis by considering the clinicopathological characteristics of patients who have 1-2 sentinel lymph node (SLN) metastasis in the clinical early stage (T1-2, N0) breast cancer.

METHODS

The demographic and clinicopathological characteristics of the patients were recorded retrospectively. Among these, age, size of the primary breast tumor, tumor localization and multifocality/multicentricity status, preoperative serum Neutrophil/Lymphocyte rate (NLR), c-erbB2/HER2-neu status, Estrogen Receptor (ER) and Progesterone Receptor (PR) status, primary tumor proliferation index (Ki-67), histopathological grade, molecular subtypes, histopathological subtypes, nipple/areola infiltration, Lymphatic Invasion (LI), Vascular Invasion (VI), Perineural Invasion (PNI), number of metastatic SLN m(SLN), mSLN diameter, SLN Extranodal Extension (ENE) status, and number of metastatic nonSLNs were recorded.

RESULTS

According to the univariate analysis, the HER2 positivity, Ki-67≥%20, mSLN diameter, LI, VI, PNI, ENE and molecular subtypes were found to be significant. However, the age, tumor localization, multifocality/multicentricity, T stage, ER and PR status, tumor size, histopathological grade and subtypes, nipple/areola infiltration and NLR were not found to be significant. In the multivariate analysis, significant independent predictors in nonSLN metastasis development were found to be HER2 positivity, PNI, mSLN diameter ≥10,5 mm and ENE.

CONCLUSION

The HER2 positivity, ENE, PNI and mSLN diameter ≥10,5 mm were found to be very strong predictors in nonSLN metastasis development. The findings of this study have the potential to be a guideline for surgeons and oncologists when determining their patients' treatment plan. These components are candidates for inclusion among the clinicopathological factors that may be used in the new nomograms due to their higher sensitivity and specificity.

Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool

Background

This study aimed to develop an easy to use prediction model to predict the risk of having a total of 1 to 2, ≥3, or ≥4 positive axillary lymph nodes (LNs), for patients with sentinel lymph node (SLN) positive breast cancer.

Methods

Data of 911 SLN positive breast cancer patients were used for model development. The model was validated externally in an independent population of 180 patients with SLN positive breast cancer.

Results

Final pathology after ALND showed additional positive LN for 259 (28%) of the patients. A total of 726 (81%) out of 911 patients had a total of 1 to 2 positive nodes, whereas 175 (19%) had ≥3 positive LNs. The model included three predictors: the tumor size (in mm), the presence of a negative SLN, and the size of the SLN metastases (in mm). At external validation, the model showed a good discriminative ability (area under the curve = 0.82; 95% confidence interval = 0.74‐0.90) and good calibration over the full range of predicted probabilities.

Conclusion

This new and validated model predicts the extent of nodal involvement in node‐positive breast cancer and will be useful for counseling patients regarding their personalized axillary treatment.

Nomogram to predict non-sentinel lymph node status using total tumor load determined by one-step nucleic acid amplification: first report from Thailand

BACKGROUND

Axillary staging is a significant prognostic factor often used to determine the treatment course for breast cancer. One-step nucleic acid amplification (OSNA) is now the most accepted method for intra-operative assessment of sentinel lymph nodes (SLN) as it can semi-quantitatively determine the tumor burden in these SLN. Axillary lymph node dissection (ALND) may be omitted in patients with limited disease in the axilla. The objective was to create nomogram for prediction of non-sentinel lymph node (NSLN) status using OSNA to avoid unnecessary ALND.

PATIENTS AND METHODS

Patients with invasive breast cancer T1-T3 and clinically negative axillary lymph nodes underwent SLN biopsy assessed by OSNA. The patients with positive SLN underwent ALND. Correlations between total tumor load (TTL), clinicopathological parameters, and NSLN status were analyzed by Chi square statistic and logistic regression. Model discrimination was evaluated using receiver-operating characteristic (ROC) analysis.

RESULTS

The total number of patients who underwent SLN biopsies was 278. There were 89 patients with positive SLN. NSLNs were positive in 40 patients. Larger tumor size, presence of lymphovascular invasion (LVI) and higher log TTL were independent factors that predicted positive NSLN. TTL can discriminate NSLN status with area under the ROC curve of 0.789 (95% CI 0.686-0.892). Two nomograms using different parameters obtained pre- and post-operatively can predict NSLN involvement with better area under the ROC curve (0.801, 95% CI 0.702-0.900 and 0.849, 95% CI 0.766-0.932, respectively).

CONCLUSIONS

Nomograms using results obtained via OSNA can predict NSLN status, as well as aid in deciding to omit the use of ALND.

Predictors of nonsentinel lymph node metastasis in patients with breast cancer with metastasis in the sentinel node

To predict the factors related to axillary nonsentinel lymph node (NSLN) metastasis in patients with positive sentinel lymph node (SLN) of early breast cancer.The retrospective data are collected from the patients with positive SLN who received further completion axillary lymph node dissection (cALND) in Peking Union Medical Hospital between March 2016 and December 2017. Univariate analysis was conducted on data with various clinicopathologic factors at first. Those factors with statistic significance (P < .05) in univariate analysis were then used to implement multivariate analysis and logistic regression.There were total of 734 patients who received SLN biopsy , among whom 153 cases were included in our study. About 39.22% (60/153) of 153 paitents with positive SLN had no NSLN metastasisted to SLN. Univariate analysis showed that 3 variables were significantly correlated with NSLN involvement: tumor size (X = 10.384, P = .001), SLN metastasis ratio (number of positive SLNs/number of SLNs removed × 100%) (X = 10.365, P = .001) and the number of negative sentinel nodes (X = 10.384, P = .006). In multivariate analysis and logistic regression, tumor size (odds ratio [OR] = 3.392, 95% confidence interval [CI]: 1.409-8.166, P = .006) and SLN metastasis ratio (OR = 3.514, 95% CI: 1.416-8.72, P = .007) were the independent risk factors. While the number of negative sentinel nodes (OR = 0.211, 95% CI: 0.063-0.709, P = .014) was the independent protective factor. The calculated risk resulted in an area under the curve of 0.746 (95% CI: 0.644-0.848), suggesting stable discriminative capability in Chinese population.For those patients with positive SLN, larger tumor burden and SLN metastasis ratio are independent risk factors for NSLN metastasis. However, the more of the detected negative SLN, the less possibility with NSLN involvement.

Validation of Breast Cancer Models for Predicting the Nonsentinel Lymph Node Metastasis After a Positive Sentinel Lymph Node Biopsy in a Chinese Population

Objectives:

Over the years, completion axillary lymph node dissection is recommended for the patients with breast cancer if sentinel lymph node metastasis is found. However, not all of these patients had nonsentinel lymph node metastasis on final histology. Some predicting models have been developed for calculating the risk of nonsentinel lymph node metastasis. The aim of our study was to validate some of the predicting models in a Chinese population.

Method:

Two hundred thirty-six patients with positive sentinel lymph node and complete axillary lymph node dissection were included. Patients were applied to 6 models for evaluation of the risk of nonsentinel lymph node involvement. The receiver–operating characteristic curves were shown in our study. The calculation of area under the curves and false negative rate was done for each model to assess the discriminative power of the models.

Results:

There are 105 (44.5%) patients who had metastatic nonsentinel lymph node(s) in our population. Primary tumor size, the number of metastatic sentinel lymph node, and the proportion of metastatic sentinel lymph nodes/total sentinel lymph nodes were identified as the independent predictors of nonsentinel lymph node metastasis. The Seoul National University Hospital and Louisville scoring system outperformed the others, with area under the curves of 0.706 and 0.702, respectively. The area under the curve values were 0.677, 0.673, 0.432, and 0.674 for the Memorial Sloan-Kettering Cancer Center, Tenon, Stanford, and Shanghai Cancer Hospital models, respectively. With adjusted cutoff points, the Louisville scoring system outperformed the others by classifying 26.51% of patients with breast cancer to the low-risk group.

Conclusion:

The Louisville and Seoul National University Hospital scoring system were found to be more predictive among the 6 models when applied to the Chinese patients with breast cancer in our database. Models developed at other institutions should be used cautiously for decision-making regarding complete axillary lymph node dissection after a positive biopsy in sentinel lymph node.

Validation of a Nomogram Predicting Non-Sentinel Lymph Node Metastases among Patients with Breast Cancer after Primary Systemic Therapy - a transSENTINA Substudy

Background

Prediction of non-sentinel lymph node (SLN) status after primary systemic therapy (PST) may allow tailored axillary staging. The aim of this analysis was to compare established nomograms from i) the primary operative (n = 6) and ii) the neoadjuvant (n = 1) setting with an optimized nomogram to predict non-SLN status in patients after PST.

Methods

181 patients converting from cN1 prior to PST to ycN0 but found to have a histologically positive SLN in the SENTINA trial were analyzed. Established models were applied. An optimized model was compiled using univariate and subsequent multivariable logistic regression (backward selection, likelihood ratio test).

Results

Area-under-the-curve (AUC) values from the primary operative models showed sufficient performance (0.82-0.71). For the neoadjuvant model, the AUC was found to be inferior to prior analyses (0.66) but within published confidence intervals. The SENTINA nomogram comprised the diameter of the largest lymph node (p = 0.006, odds ratio (OR) = 1.19), tumor size prior to PST (p = 0.085, OR = 1.31), and number of all positive SLN (p = 0.083, OR = 2.04). This model was validated using a separate cohort of arm C (n = 168, AUC 0.79, 95% confidence interval 0.74-0.85).

Conclusion

We validated 7 models of prediction of non-SLN among patients showing axillary conversion through PST. Our own 'SENTINA nomogram' yielded AUC values comparable to previous nomograms.

Is completion axillary lymph node dissection necessary in patients who are underrepresented in the ACOSOG Z0011 trial?

PURPOSE

The American College of Surgeons Oncology Group trial Z0011 demonstrated that axillary node dissection (ALND) can be omitted in patients managed with breast conserving surgery and 1 to 2 positive sentinel lymph nodes (SLNs) without adverse effects on locoregional recurrence or disease-free survival (DFS). We investigated patients with breast cancer for whom clinicopathologic features were underrepresented in the Z0011 trial and analyzed radiation therapy treatment patterns and clinical outcomes.

METHODS AND MATERIALS

We retrospectively reviewed records of patients who underwent a lumpectomy and SLN biopsy with positive SLNs but not an ALND and completed adjuvant radiation therapy. Eligible patients had T3 tumors, >2 positive SLNs, invasive lobular carcinoma, estrogen receptor negative status, extranodal extension, Nottingham Grade 3, or were age <50 years.

RESULTS

We identified 105 women treated between July 2011 and July 2016 with a median follow-up time of 48.5 months (Range, 11-83 months). There were 40 women with an extranodal extension (38.9%) and 42 women with grade 3 disease (40.0%). Nineteen patients received whole breast irradiation alone (18.1%) and 86 patients were treated with modified tangent fields including the superior axilla level I/II (81.9%). Thirty-three patients (31.4%) also received a 3rd supraclavicular, nodal-directed field. Among the 86 patients who received axillary nodal irradiation, nodal volume contouring was performed in 77 patients (89.5%). Fifty-one patients (48.6%) also received adjuvant chemotherapy. The overall rates of 4-year DFS and locoregional control (LRC) were 94.3% and 98.1%, respectively. Off all patients, 1 patient experienced an internal mammary nodal recurrence, another patient a contralateral breast tumor, and two patients distant metastases. There were no axillary or ipsilateral breast tumor recurrences.

CONCLUSIONS

This retrospective analysis of women who were underrepresented or excluded from the Z11 trial and underwent a lumpectomy and SLN biopsy with positive SLNs demonstrated comparable rates of LRC and DFS. The high rates of LRC and DFS suggest that completion ALND may be safely omitted in this patient population but larger data sets and longer follow-up times are needed to confirm this finding.

A mathematical prediction model incorporating molecular subtype for risk of non-sentinel lymph node metastasis in sentinel lymph node-positive breast cancer patients: a retrospective analysis and nomogram development

BACKGROUND

Molecular subtype of breast cancer is associated with sentinel lymph node status. We sought to establish a mathematical prediction model that included breast cancer molecular subtype for risk of positive non-sentinel lymph nodes in breast cancer patients with sentinel lymph node metastasis and further validate the model in a separate validation cohort.

METHODS

We reviewed the clinicopathologic data of breast cancer patients with sentinel lymph node metastasis who underwent axillary lymph node dissection between June 16, 2014 and November 16, 2017 at our hospital. Sentinel lymph node biopsy was performed and patients with pathologically proven sentinel lymph node metastasis underwent axillary lymph node dissection. Independent risks for non-sentinel lymph node metastasis were assessed in a training cohort by multivariate analysis and incorporated into a mathematical prediction model. The model was further validated in a separate validation cohort, and a nomogram was developed and evaluated for diagnostic performance in predicting the risk of non-sentinel lymph node metastasis. Moreover, we assessed the performance of five different models in predicting non-sentinel lymph node metastasis in training cohort.

RESULTS

Totally, 495 cases were eligible for the study, including 291 patients in the training cohort and 204 in the validation cohort. Non-sentinel lymph node metastasis was observed in 33.3% (97/291) patients in the training cohort. The AUC of MSKCC, Tenon, MDA, Ljubljana, and Louisville models in training cohort were 0.7613, 0.7142, 0.7076, 0.7483, and 0.671, respectively. Multivariate regression analysis indicated that tumor size (OR = 1.439; 95% CI 1.025-2.021; P = 0.036), sentinel lymph node macro-metastasis versus micro-metastasis (OR = 5.063; 95% CI 1.111-23.074; P = 0.036), the number of positive sentinel lymph nodes (OR = 2.583, 95% CI 1.714-3.892; P < 0.001), and the number of negative sentinel lymph nodes (OR = 0.686, 95% CI 0.575-0.817; P < 0.001) were independent statistically significant predictors of non-sentinel lymph node metastasis. Furthermore, luminal B (OR = 3.311, 95% CI 1.593-6.884; P = 0.001) and HER2 overexpression (OR = 4.308, 95% CI 1.097-16.912; P = 0.036) were independent and statistically significant predictor of non-sentinel lymph node metastasis versus luminal A. A regression model based on the results of multivariate analysis was established to predict the risk of non-sentinel lymph node metastasis, which had an AUC of 0.8188. The model was validated in the validation cohort and showed excellent diagnostic performance.

CONCLUSIONS

The mathematical prediction model that incorporates five variables including breast cancer molecular subtype demonstrates excellent diagnostic performance in assessing the risk of non-sentinel lymph node metastasis in sentinel lymph node-positive patients. The prediction model could be of help surgeons in evaluating the risk of non-sentinel lymph node involvement for breast cancer patients; however, the model requires further validation in prospective studies.

Analysis of factors related to non-sentinel lymph node metastasis in 296 sentinel lymph node-positive Chinese breast cancer patients

Objective:

Axillary lymph node dissection (ALND) may be unnecessary in 20%–60% of breast cancer patients with sentinel lymph node (NSLN) metastasis. The aim of the present study was to review the medical records of Chinese patients with early-stage breast cancer and positive NSLN metastasis to identify clinicopathological characteristics as risk factors for non-NSLN metastasis.

Methods:

The medical records of 2008 early-stage breast cancer patients who received intraoperative sentinel lymph node biopsy (SLNB) between 2006 and 2016 were retrospectively reviewed. These patients were clinically and radiologically lymph node-negative and had no prior history of receiving neoadjuvant chemotherapy or endocrinotherapy. The clinicopathological characteristics of patients with positive NSLN metastasis who underwent ALND were investigated.

Results:

In the present study, 296 patients with positive NSLN metastases underwent ALND. Positive non-NSLN metastases were confirmed in 95 patients (32.1%). On univariate analysis, ≥ 3 positive NSLN metastases (P <0.01), NSLN macrometastases ( P = 0.023), and lymphovascular invasion (P = 0.04) were associated with non-NSLN metastasis (P <0.05). In multivariate analysis, the number of positive SLNs was the most significant predictor of non-SLN metastasis. For patients with 0, 1, 2, or 3 associated risk factors, the non-SLN metastatic rates were 11.5%, 22.5%, 35.2%, and 73.1%, respectively.

Conclusions:

The number of positive NSLNs, NSLN macrometastases, and lymphovascular invasion were correlated with non-SLN metastasis. The number of positive SLNs was an independent predictor for non-NSLN metastasis. When 2 or 3 risk factors were present in one patient, the probability of non-NSLN was higher than that in the American College of Surgeons Oncology Group Z0011 trial (27.3%); thus, avoiding ALND should be considered carefully.

Intraoperative prediction of the two axillary lymph node macrometastases threshold in patients with breast cancer using a one-step nucleic acid cytokeratin-19 amplification assay

The aim of the present study was to assess the sensitivity, specificity and practicality of using a one-step nucleic acid amplification (OSNA) assay during breast cancer staging surgery to predict and discriminate between at least 2 involved nodes and more than 2 involved nodes and facilitate the decision to provide axillary conservation in the presence of a low total axillary node tumour burden. A total of 700 consecutive patients, not treated with neo-adjuvant chemotherapy, received intraoperative sentinel lymph node (SLN) analysis using OSNA for cT1-T3 cN0 invasive breast cancer. Patients with at least one macrometastasis on whole-node SLN analysis underwent axillary lymph node dissection (ALND). The total tumour load (TTL) of the macrometastatic SLN sample was compared with the non-sentinel lymph node (NSLN) status of the ALND specimen using routine histological assessment. In total, 122/683 patients (17.9%) were found to have an OSNA TTL indicative of macrometastasis. In addition, 45/122 (37%) patients had NSLN metastases on ALND with a total positive lymph node burden exceeding the American College of Surgeons Oncology Group Z0011 trial threshold of two macrometastatic nodes. The TTL negative predictive value was 0.975 [95% confidence interval (CI), 0.962-0.988]. The area under the curve for the receiver operating characteristic curve was 0.86 (95% CI, 0.81-0.91), indicating that SLN TTL was associated with the prediction (and partitioning) of total axillary disease burden. OSNA identifies a TTL threshold value where, in the presence of involved SLNs, ALND may be avoided. This technique offers objective confidence in adopting conservative management of the axilla in patients with SLN macrometastases.

Application of artificial neural networks for predicting presence of non-sentinel lymph node metastases in breast cancer patients with positive sentinel lymph node biopsies

INTRODUCTION

The aim of this study was to present a new predictive tool for non-sentinel lymph node (nSLN) metastases.

MATERIAL AND METHODS

One thousand five hundred eighty-three patients with early-stage breast cancer were subjected to sentinel lymph node biopsy (SLNB) between 2004 and 2012. Metastatic SLNs were found in 348 patients - the retrospective group. Selective axillary lymph node dissection (ALND) was performed in 94% of cases. Involvement of the nSLNs was identified in 32.1% of patients following ALND. The correlation between nSLN involvement and selected epidemiological data, primary tumor features and details of the diagnostic and therapeutic management was examined in metastatic SLN group. Multivariate analysis was performed using an artificial neural network to create a new nomogram. The new test was validated using the overall study population consisting of the prospective group (365 patients - SLNB between 01-07.2013).

RESULTS

Accuracy of the new test was calculated using area under the receiver operating characteristics curve (AUC). We obtained AUC coefficient equal to 0.87 (95% confidence interval: 0.81-0.92). Sensitivity amounted to 69%, specificity to 86%, accuracy - 80% (retrospective group) and 77%, 46%, 66% (validation group), respectively. The Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram the calculated AUC value was 0.71, for Stanford - 0.68, for Tenon - 0.67.

CONCLUSIONS

In the analyzed group only the MSKCC nomogram and the new model showed AUC values exceeding the expected level of 0.70. Our nomogram performs well in prospective validation on patient series. The overall assessment of clinical usefulness of this test will be possible after testing it on different patient populations.

Nomograms for preoperative prediction of axillary nodal status in breast cancer

Background

Axillary staging in patients with breast cancer and clinically node‐negative disease is performed by sentinel node biopsy (SLNB). The aim of this study was to integrate feasible preoperative variables into nomograms to guide clinicians in stratifying treatment options into no axillary staging for patients with non‐metastatic disease (N0), SLNB for those with one or two metastases, and axillary lymph node dissection (ALND) for patients with three or more metastases.

Methods

Patients presenting to Skåne University Hospital, Lund, with breast cancer were included in a prospectively maintained registry between January 2009 and December 2012. Those with a preoperative diagnosis of nodal metastases were excluded. Patients with data on hormone receptor status, human epidermal growth factor receptor 2 and Ki‐67 expression were included to allow grouping into surrogate molecular subtypes. Based on logistic regression analyses, nomograms summarizing the strength of the associations between the predictors and each nodal status endpoint were developed. Predictive performance was assessed using the area under the receiver operating characteristic (ROC) curve. Bootstrap resampling was performed for internal validation.

Results

Of the 692 patients eligible for analysis, 248 were diagnosed with node‐positive disease. Molecular subtype, age, mode of detection, tumour size, multifocality and vascular invasion were identified as predictors of any nodal disease. Nomograms that included these predictors demonstrated good predictive abilities, and comparable performances in the internal validation; the area under the ROC curve was 0·74 for N0versus any lymph node metastasis, 0·70 for one or two involved nodes versusN0, and 0·81 for at least three nodes versus two or fewer metastatic nodes.

Conclusion

The nomograms presented facilitate preoperative decision‐making regarding the extent of axillary surgery.

Defines need for staging?

Multicenter prospective evaluation of the reliability of the combined use of two models to predict non-sentinel lymph node status in breast cancer patients with metastatic sentinel lymph nodes: the MSKCC nomogram and the Tenon score. Results of the NOTEGS study

Background:

The purpose of this study was to prospectively evaluate the combined use of The Memorial Sloan Kettering Cancer Center nomogram and Tenon score to select, in patients with metastatic sentinel lymph node (SN), those at low risk of metastatic non-SN for whom additional axillary lymph node dissection (ALND) could be avoided.

Methods:

From January 2011 to July 2012, a prospective non-interventional nationwide study was conducted (NCT01509963). We sought to identify the false reassurance rate (FRR, a negative test result is false) in patients with both a ⩽10% probability of metastatic non-SN with the MSKCC nomogram and a Tenon score ⩽3.5 (low risk): the proportion of patients with metastatic non-SN at additional ALND. Our hypothesis was that these patients would have a FRR⩽5%.

Results:

Data on 2822 patients with breast cancer from 53 institutions were prospectively recorded. At least one SN was metastatic (isolated tumour cells, micro- or macrometastases) in 696 patients (24.7%). Among patients with ALND and complete data to calculate combined risk (n=504), 67 and 437 patients had low and high combined risk, respectively. Patients at low risk had less ALND (47%) compared to patients at high risk (P<0.001). This study did not meet its primary objective because the FRR in patients with low risk was 16.4% (11 out of 67) (95% confidence interval (CI): 9.7–23.1%). In the high-risk group, 33.9% (148 out of 437) (95% CI: 29.6–38.4%) had non-SN metastases (P=0.004).

Conclusions:

In this controlled prospective study, metastatic SN patients with both a ⩽10% probability of metastatic non-SN with the MSKCC nomogram and a Tenon score ⩽3.5 failed to identify patients at low risk of metastatic non-SN when completion ALND was not systematic.

Sentinel Lymph Nodes for Breast Carcinoma: A Paradigm Shift

CONTEXT

-Sentinel lymph node biopsy has been established as the new standard of care for axillary staging in most patients with invasive breast carcinoma. Historically, all patients with a positive sentinel lymph node biopsy result underwent axillary lymph node dissection. Recent trials show that axillary lymph node dissection can be safely omitted in women with clinically node negative, T1 or T2 invasive breast cancer treated with breast-conserving surgery and whole-breast radiotherapy. This change in practice also has implications on the pathologic examination and reporting of sentinel lymph nodes.

OBJECTIVE

-To review recent clinical and pathologic studies of sentinel lymph nodes and explore how these findings influence the pathologic evaluation of sentinel lymph nodes.

DATA SOURCES

-Sources were published articles from peer-reviewed journals in PubMed (US National Library of Medicine) and published guidelines from the American Joint Committee on Cancer, the Union for International Cancer Control, the American Society of Clinical Oncology, and the National Comprehensive Cancer Network.

CONCLUSIONS

-The main goal of sentinel lymph node examination should be to detect all macrometastases (>2 mm). Grossly sectioning sentinel lymph nodes at 2-mm intervals and evaluation of one hematoxylin-eosin-stained section from each block is the preferred method of pathologic evaluation. Axillary lymph node dissection can be safely omitted in clinically node-negative patients with negative sentinel lymph nodes, as well as in a selected group of patients with limited sentinel lymph node involvement. The pathologic features of the primary carcinoma and its sentinel lymph node metastases contribute to estimate the extent of non-sentinel lymph node involvement. This information is important to decide on further axillary treatment.