A logistic regression model for predicting axillary lymph node metastases in early breast carcinoma patients

Assessment of the axilla in women with early-stage breast cancer undergoing primary surgery: a review

Sentinel node biopsy (SNB) is routinely performed in people with node-negative early breast cancer to assess the axilla. SNB has no proven therapeutic benefit. Nodal status information obtained from SNB helps in prognostication and can influence adjuvant systemic and locoregional treatment choices. However, the redundancy of the nodal status information is becoming increasingly apparent. The accuracy of radiological assessment of the axilla, combined with the strong influence of tumour biology on systemic and locoregional therapy requirements, has prompted many to consider alternative options for SNB. SNB contributes significantly to decreased quality of life in early breast cancer patients. Substantial improvements in workflow and cost could accrue by removing SNB from early breast cancer treatment. We review the current viewpoints and ideas for alternative options for assessing and managing a clinically negative axilla in patients with early breast cancer (EBC). Omitting SNB in selected cases or replacing SNB with a non-invasive predictive model appear to be viable options based on current literature.

Association between postoperative pathological results and non-sentinel nodal metastasis in breast cancer patients with sentinel lymph node-positive breast cancer

OBJECTIVE

For patients with 1-2 positive sentinel lymph nodes (SLN) identified by biopsy, the necessity of axillary lymph node dissection (ALND) remains a matter of debate. The primary aim of this study was to investigate the association between postoperative pathological factors and non-sentinel lymph node (NSLN) metastases in Chinese patients diagnosed with sentinel node-positive breast cancer.

METHODS

This research involved a total of 280 individuals with SLN-positive breast cancer. The relationship between postoperative pathological variables and non-sentinel lymph node metastases was scrutinized using univariate, multivariate, and stratified analysis.

RESULTS

Among the 280 patients with a complete count of SLN positives, 126 (45.0%) exhibited NSLN metastasis. Within this group, 45 cases (35.71%) had 1 SLN positive, while 81 cases (64.29%) demonstrated more than 1 SLN positive. Multivariate logistic regression analysis revealed that HER2 expression status (OR 2.25, 95% CI 1.10-4.60, P = 0.0269), LVI (OR 6.08, 95% CI 3.31-11.14, P < 0.0001), and the number of positive SLNs (OR 4.17, 95% CI 2.35-7.42, P < 0.0001) were positively correlated with NSLNM.

CONCLUSION

In our investigation, the risk variables for NSLN metastasis included LVI, HER2 expression, and the quantity of positive sentinel lymph nodes. However, further validation is imperative, including this institution, distinct institutions, and diverse patient populations.

A multivariable model of ultrasound and clinicopathological features for predicting axillary nodal burden of breast cancer: potential to prevent unnecessary axillary lymph node dissection

BACKGROUND

To develop a clinical model for predicting high axillary nodal burden in patients with early breast cancer by integrating ultrasound (US) and clinicopathological features.

METHODS AND MATERIALS

Patients with breast cancer who underwent preoperative US examination and breast surgery at the Affiliated Hospital of Nantong University (centre 1, n = 250) and at the Affiliated Hospital of Jiangsu University (centre 2, n = 97) between January 2012 and December 2016 and between January 2020 and March 2022, respectively, were deemed eligible for this study (n = 347). According to the number of lymph node (LN) metastasis based on pathology, patients were divided into two groups: limited nodal burden (0-2 metastatic LNs) and heavy nodal burden (≥ 3 metastatic LNs). In addition, US features combined with clinicopathological variables were compared between these two groups. Univariate and multivariate logistic regression analysis were conducted to identify the most valuable variables for predicting ≥ 3 LNs in breast cancer. A nomogram was then developed based on these independent factors.

RESULTS

Univariate logistic regression analysis revealed that the cortical thickness (p < 0.001), longitudinal to transverse ratio (p = 0.001), absence of hilum (p < 0.001), T stage (p = 0.002) and Ki-67 (p = 0.039) were significantly associated with heavy nodal burden. In the multivariate logistic regression analysis, cortical thickness (p = 0.001), absence of hilum (p = 0.042) and T stage (p = 0.012) were considered independent predictors of high-burden node. The area under curve (AUC) of the nomogram was 0.749.

CONCLUSION

Our model based on US variables and clinicopathological characteristics demonstrates that can help select patients with ≥ 3 LNs, which can in turn be helpful to predict high axillary nodal burden in early breast cancer patients and prevent unnecessary axillary lymph node dissection.

Predicting the risk of axillary lymph node metastasis in early breast cancer patients based on ultrasonographic-clinicopathologic features and the use of nomograms: a prospective single-center observational study

OBJECTIVES

The purpose of this study was to establish two preoperative nomograms to evaluate the risk for axillary lymph node (ALN) metastasis in early breast cancer patients based on ultrasonographic-clinicopathologic features.

METHODS

We prospectively evaluated 593 consecutive female participants who were diagnosed with cT_1-3N_0-1M₀ breast cancer between March 2018 and May 2019 at Sun Yat-Sen Memorial Hospital. The participants were randomly classified into training and validation sets in a 4:1 ratio for the development and validation of the nomograms, respectively. Multivariate logistic regression analysis was performed to identify independent predictors of ALN status. We developed Nomogram A and Nomogram B to predict ALN metastasis (presence vs. absence) and the number of metastatic ALNs (≤ 2 vs. > 2), respectively.

RESULTS

A total of 528 participants were evaluated in the final analyses. Multivariable analysis revealed that the number of suspicious lymph nodes, long axis, short-to-long axis ratio, cortical thickness, tumor location, and histological grade were independent predictors of ALN status. The AUCs of nomogram A in the training and validation groups were 0.83 and 0.78, respectively. The AUCs of nomogram B in the training and validation groups were 0.87 and 0.87, respectively. Both nomograms were well-calibrated.

CONCLUSION

We developed two preoperative nomograms that can be used to predict ALN metastasis (presence vs. absence) and the number of metastatic ALNs (≤ 2 vs. > 2) in early breast cancer patients. Both nomograms are useful tools that will help clinicians predict the risk of ALN metastasis and facilitate therapy decision-making about axillary surgery.

KEY POINTS

• We developed two preoperative nomograms to predict axillary lymph node status based on ultrasonographic-clinicopathologic features. • Nomogram A was used to predict axillary lymph node metastasis (presence vs. absence). The AUCs in the training and validation groups were 0.83 and 0.78, respectively. Nomogram B was used to estimate the number of metastatic lymph nodes ( ≤ 2 vs. > 2). The AUCs in the training and validation group were 0.87 and 0.87, respectively. • Our nomograms may help clinicians weigh the risks and benefits of axillary surgery more appropriately.

Method for Data Quality Assessment of Synthetic Industrial Data

Sometimes it is difficult, or even impossible, to acquire real data from sensors and machines that must be used in research. Such examples are the modern industrial platforms that frequently are reticent to share data. In such situations, the only option is to work with synthetic data obtained by simulation. Regarding simulated data, a limitation could consist in the fact that the data are not appropriate for research, based on poor quality or limited quantity. In such cases, the design of algorithms that are tested on that data does not give credible results. For avoiding such situations, we consider that mathematically grounded data-quality assessments should be designed according to the specific type of problem that must be solved. In this paper, we approach a multivariate type of prediction whose results finally can be used for binary classification. We propose the use of a mathematically grounded data-quality assessment, which includes, among other things, the analysis of predictive power of independent variables used for prediction. We present the assumptions that should be passed by the synthetic data. Different threshold values are established by a human assessor. In the case of research data, if all the assumptions pass, then we can consider that the data are appropriate for research and can be applied by even using other methods for solving the same type of problem. The applied method finally delivers a classification table on which can be applied any indicators of performed classification quality, such as sensitivity, specificity, accuracy, F1 score, area under curve (AUC), receiver operating characteristics (ROC), true skill statistics (TSS) and Kappa coefficient. These indicators’ values offer the possibility of comparison of the results obtained by applying the considered method with results of any other method applied for solving the same type of problem. For evaluation and validation purposes, we performed an experimental case study on a novel synthetic dataset provided by the well-known UCI data repository.

A Model to Predict the Risk of Lymph Node Metastasis in Breast Cancer Based on Clinicopathological Characteristics

Background

Sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND) may cause lymphatic and nervous system side effects in patients with breast cancer. It is imperative to develop a model to evaluate the risk of sentinel lymph node metastasis to avoid unnecessary operation.

Patients and Methods

A total of 2705 cases of female breast cancer patients enrolled in this retrospective study. We divided into the training group (SLNB group) and the validation group (ALND group) to analyze the relathionship between lymph node metastasis and clinical-pathological factors. Logistic regression analysis was performed to verify the variables which involved in ALN metastasis and established a prediction model. ROC curves were employed to evaluate the predictive ability of the model.

Results

In the SLNB group, 9 variables, including pathological type, histological grade, tumor size, hormone receptor, HER-2, Ki-67, multifocality, and molecular subtypes, were related to breast cancer ALN metastasis. Clinically negative lymph nodes, favorable histologic type, tumor size <2 cm, and Ki-67 <15% were at very low risk for lymph node metastasis. The AUC of the validation group was 0.786.

Conclusion

We successfully establish a mathematics model to predict lymph node metastasis of breast cancer. Axillary surgery should be individual with preoperative clinical characteristics, especially for patients with a longer life expectancy.

Could Ultrasound-Based Radiomics Noninvasively Predict Axillary Lymph Node Metastasis in Breast Cancer?

OBJECTIVES

This work aimed to investigate whether quantitative radiomics imaging features extracted from ultrasound (US) can noninvasively predict breast cancer (BC) metastasis to axillary lymph nodes (ALNs).

METHODS

Presurgical B-mode US data of 196 patients with BC were retrospectively studied. The cases were divided into the training and validation cohorts (n = 141 versus 55). The elastic net regression technique was used for selecting features and building a signature in the training cohort. A linear combination of the selected features weighted by their respective coefficients produced a radiomics signature for each individual. A radiomics nomogram was established based on the radiomics signature and US-reported ALN status. In a receiver operating characteristic curve analysis, areas under the curves (AUCs) were determined for assessing the accuracy of the prediction model in predicting ALN metastasis in both cohorts. The clinical value was assessed by a decision curve analysis.

RESULTS

In all, 843 radiomics features per case were obtained from expert-delineated lesions on US imaging in this study. Through radiomics feature selection, 21 features were selected to constitute the radiomics signature for predicting ALN metastasis. Area under the curve values of 0.778 and 0.725 were obtained in the training and validation cohorts, respectively, indicating moderate predictive ability. The radiomics nomogram comprising the radiomics signature and US-reported ALN status showed the best performance for ALN detection in the training cohort (AUC, 0.816) but moderate performance in the validation cohort (AUC, 0.759). The decision curve showed that both the radiomics signature and nomogram displayed good clinical utility.

CONCLUSIONS

This pilot radiomics study provided a noninvasive method for predicting presurgical ALN metastasis status in BC.

KISS1 in metastatic cancer research and treatment: potential and paradoxes

The significance of KISS1 goes beyond its original discovery as a metastasis suppressor. Its function as a neuropeptide involved in diverse physiologic processes is more well studied. Enthusiasm regarding KISS1 has cumulated in clinical trials in multiple fields related to reproduction and metabolism. But its cancer therapeutic space is unsettled. This review focuses on collating data from cancer and non-cancer fields in order to understand shared and disparate signaling that might inform clinical development in the cancer therapeutic and biomarker space. Research has focused on amino acid residues 68-121 (kisspeptin 54), binding to the KISS1 receptor and cellular responses. Evidence and counterevidence regarding this canonical pathway require closer look at the covariates so that the incredible potential of KISS1 can be realized.

Independent risk factors for axillary lymph node metastasis in breast cancer patients with one or two positive sentinel lymph nodes

Background

The benefit of axillary lymph node dissection (ALND) in breast cancer patients with one or two positive sentinel lymph nodes (SLNs) remains inconclusive. The purpose of this study was to identify risk factors independently associated with axillary lymph node (ALN) metastasis.

Methods

We retrospectively analyzed data from 389 Chinese breast cancer patients with one or two positive SLNs who underwent ALND. Univariate and multivariate logistic regression analyses were performed to identify ALN metastasis-associated risk factors.

Results

Among the 389 patients, 174 (44.7%) had ALN metastasis, while 215 (55.3%) showed no evidence of ALN metastasis. Univariate analysis revealed significant differences in age (< 60 or ≥ 60 years), human epidermal growth factor receptor-2 (Her-2) status, and the ratio of positive to total SLNs between the ALN metastasis and non-metastasis groups (P < 0.05). The multivariate analysis indicated that age, the ratio of positive to total SLNs, and occupations were significantly different between the two groups. Lastly, younger age (< 60 years), a higher ratio of positive to total SLNs, and manual labor jobs were independently associated with ALN metastasis (P < 0.05).

Conclusions

The risk of ALN metastasis in breast cancer patients with one or two positive SLNs can be further increased by younger age, manual labor jobs, and a high ratio of positive to total SLNs. Our findings may also aid in identifying which patients with one or two positive SLNs may not require ALND.

The role of kisspeptin system in cancer biology

Kisspeptins are a family of neuropeptides that are known to be critical in puberty initiation and ovulation. Apart from that kisspeptin derived peptides (KPs) are also known for their antimetastatic activities in several malignancies. Herein we report recent evidence of the role of kisspeptins in cancer biology and we examine the prospective of targeting the kisspeptin pathways leading to a better prognosis in patients with malignant diseases.

Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer

OBJECTIVE

To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients.

METHODS

Preoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram.

RESULTS

The radiomic signature based on 12 LN status-related features was constructed to predict LN metastasis, its prediction ability was moderate, with an area under the curve (AUC) of 0.76 and 0.78 in training and validation cohorts, respectively. Based on a radiomic signature and clinical features, a nomogram was developed and showed excellent predictive ability for LN metastasis (AUC 0.84 and 0.87 in training and validation sets, respectively). Another radiomic signature was constructed to distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes), which also showed moderate performance (AUC 0.79).

CONCLUSIONS

We developed a nomogram and a radiomic signature that can be used to identify LN metastasis and distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes). Both nomogram and radiomic signature can be used as tools to assist clinicians in assessing LN metastasis in breast cancer patients.

KEY POINTS

• ALNM is an important factor affecting breast cancer patients' treatment and prognosis. • Traditional imaging examinations have limited value for evaluating axillary LNs status. • We developed a radiomic nomogram based on MR imagings to predict LN metastasis.

Development and Validation of Nomograms Predictive of Axillary Nodal Status to Guide Surgical Decision-Making in Early-Stage Breast Cancer

Purpose: To develop and validate nomogram models using noninvasive imaging parameters with related clinical variables to predict the extent of axillary nodal involvement and stratify treatment options based on the essential cut-offs for axillary surgery according to the ACOSOG Z0011 criteria.

Materials and Methods: From May 2007 to December 2017, 1799 patients who underwent preoperative breast and axillary magnetic resonance imaging (MRI) were retrospectively studied. Patients with data on axillary ultrasonography (AUS) were enrolled. The MRI images were interpreted according to Breast Imaging Reporting and Data system (BI-RADS). Using logistic regression analyses, nomograms were developed to visualize the associations between the predictors and each lymph node (LN) status endpoint. Predictive performance was assessed based on the area under the receiver operating characteristic curve (AUC). Bootstrap resampling was performed for internal validation. Goodness-of-fit of the models was evaluated using the Hosmer-Lemeshow test.

Results: Of 397 early breast cancer patients, 200 (50.4%) had disease-free axilla, 119 (30.0%) had 1 or 2 positive LNs, and 78 (19.6%) had ≥3 positive LNs. Patient age, MRI features (mass margin, LN margin, presence/absence of LN hilum, and LN symmetry/asymmetry), and AUS descriptors (presence of cortical thickening or hilum) were identified as predictors of nodal disease. Nomograms with these predictors showed good calibration and discrimination; the AUC was 0.809 for negative axillary node (N0) vs. any LN metastasis, 0.749 for 1 or 2 involved nodes vs. N0, and 0.874 for ≥3 nodes vs. ≤2 metastatic nodes. The predictive ability of the 3 nomograms with additional pathological variables was significantly greater.

Conclusion: The nomograms could predict the extent of ALN metastasis and facilitate decision-making preoperatively.

L1 Cell Adhesion Molecule and Its Soluble Form sL1 Exhibit Poor Prognosis in Primary Breast Cancer Patients

INTRODUCTION

The L1 cell adhesion molecule (L1-CAM) and its soluble form sL1 play a prominent role in invasion and metastasis in several cancers. However, its association with breast cancer is still unclear.

PATIENTS AND METHODS

We analyzed L1-CAM expression and serum sL1 levels in cancer and para-carcinoma tissues from 162 consecutive patients with primary invasive breast cancer (PBC) using immunohistochemistry and an enzyme-linked immunosorbent assay, respectively. The serum sL1 levels were also examined in 38 patients with benign breast disease and 36 healthy controls.

RESULTS

L1-CAM was expressed more frequently in cancer tissues than in para-carcinoma tissues (24.1% vs. 5.6%; P < .001), and the mean sL1 levels were significantly greater in PBC than in those with benign breast disease and healthy controls (P = .027). Both L1-CAM⁺ expression and higher mean sL1 levels correlated significantly with larger tumor size, lymph node involvement, higher histologic grade, advanced TNM stage, and shorter disease-free survival for PBC patients. Moreover, higher mean sL1 levels were also significantly associated with estrogen receptor-α-negative expression, human epidermal growth factor receptor 2-positive (HER2⁺) expression, HER2-enriched and triple-negative molecular subtypes, and L1-CAM⁺ expression (P < .05). On multivariate analysis, larger tumor size, nodal involvement, HER2⁺, and higher sL1 levels (≥ 0.7 ng/mL) were independent factors associated with L1-CAM⁺ expression (P < .05). No association was found between L1-CAM expression or sL1 level with age, gender, histologic type, or expression of progesterone receptor, Ki-67, p53, or vascular endothelial growth factor C (P > .05).

CONCLUSION

These results indicate that L1-CAM and sL1 are elevated in PBC and both might affect the prognosis of PBC patients. In addition, sL1 might be a useful marker for screening and diagnosis.

A nomogram to predict the probability of axillary lymph node metastasis in female patients with breast cancer in China: A nationwide, multicenter, 10-year epidemiological study

Axillary lymph node dissection (ALND) or sentinel lymph node biopsy (SLNB) alone may lead to postoperative complications. Among patients with positive ALN in the preoperative examination, approximately 40% patients do not have SLN metastasis. Herein, we aimed to develop a model to predict the probability of ALN metastasis as a preoperative tool to support clinical decision-making. We retrospectively analyzed the clinicopathological features of 4211 female patients with breast cancer who were diagnosed in seven breast cancer centers representing entire China, over 10 years (1999-2008). The patients were randomly categorized into a training cohort or validation cohort (3:1 ratio). Multivariate logistic regression analysis was performed for 1869 patients with complete information on the study variables. Age at diagnosis, tumor size, tumor quadrant, clinical nodal status, local invasion status, pathological type, and molecular subtypes were the independent predictors of ALN metastasis. The nomogram was then developed using the seven variables. Further, it was subsequently validated in 642 patients with complete data on variables in the validation cohort. Coefficient of determination (R²) and the area under the receiver-operating characteristic (ROC) curve (AUC) were calculated to be 0.979 and 0.7007, showing good calibration and discrimination of the model, respectively. The false-negative rates of the nomogram were 0 and 6.9% for the predicted risk cut-off values of 14.03% and 20%, respectively. Therefore, when the predicted risk is less than 20%, SLNB may be avoided. After further validation in various patient populations, this model may support increasingly limited axillary surgery in breast cancer.

Kisspeptin and Cancer: Molecular Interaction, Biological Functions, and Future Perspectives

Cancer disease is the second leading cause of death in the world and one of the main fields of medical research. Although there is now a greater understanding of biological mechanisms of uncontrolled cell growth, invasiveness and metastasization, the multi-step process of cancer development and evolution is still incompletely understood. The inhibition of molecules activated in cancer metastasization is an hot topic in cancer research. Among the known antimetastatic genes, KiSS-1 is involved in the metastatic cascade by preventing growth of metastasis. Moreover, loss of KiSS-1 protein expression by tumor cells has been associated with a more aggressive phenotype. KiSS-1 gene encodes a 145-amino acid protein, which following proteolytic cleavage, generates a family of kisspeptins (Kp-10, -13, and -14), that are endogenous agonists for the G-protein-coupled receptor (GPR54). The antitumor effect of KiSS-1 was primarily associated with the inhibition of proliferation, migration and cell invasion and, consequently, the reduced formation of metastasis and intratumoral microvessels. In this review, we highlight the latest data on the role of kisspeptin signaling in the suppression of metastasis in various cancer types and the use modulators of KiSS/GPR54 signaling as potential novel therapeutic agents for the treatment of cancer.

Prediction of lateral pelvic lymph node metastasis from lower rectal cancer using magnetic resonance imaging and risk factors for metastasis: Multicenter study of the Lymph Node Committee of the Japanese Society for Cancer of the Colon and Rectum

PURPOSE

The goal of the study was to examine prediction of lateral pelvic lymph node (LPLN) metastasis from lower rectal cancer using a logistic model including risk factors for LPLN metastasis and magnetic resonance imaging (MRI) clinical LPLN (cLPLN) status, compared to prediction based on MRI alone.

METHODS

The subjects were 272 patients with lower rectal cancer who underwent MRI prior to mesorectal excision combined with LPLN dissection (LPLD) at six institutes. No patients received neoadjuvant therapy. Prediction models for right and left pathological LPLN (pLPLN) metastasis were developed using cLPLN status, histopathological grade, and perirectal lymph node (PRLN) status. For evaluation, data for patients with left LPLD were substituted into the right-side equation and vice versa.

RESULTS

Left LPLN metastasis was predicted using the right-side model with accuracy of 86.5%, sensitivity 56.4%, specificity 92.7%, positive predictive value (PPV) 61.1%, and negative predictive value (NPV) 91.2%, while these data using MRI cLPLN status alone were 80.4, 76.9, 81.2, 45.5, and 94.5%, respectively. Similarly, right LPLN metastasis was predicted using the left-side equation with accuracy of 83.8%, sensitivity 57.8%, specificity 90.4%, PPV 60.5%, and NPV 89.4%, and the equivalent data using MRI alone were 78.4, 68.9, 80.8, 47.7, and 91.1%, respectively. The AUCs for the right- and left-side equations were significantly higher than the equivalent AUCs for MRI cLPLN status alone.

CONCLUSIONS

A logistic model including risk factors for LPLN metastasis and MRI findings had significantly better performance for prediction of LPLN metastasis compared with a model based on MRI findings alone.

Predictive Value of Clinicopathological Characteristics for Sentinel Lymph Node Metastasis in Early Breast Cancer

BACKGROUND Sentinel lymph node biopsy (SLNB) is one of the preferred treatments for breast cancer including clinically negative lymph node breast cancer. However, for 60-70% of patients this invasive axilla surgery is unnecessary. Our study aimed to identify the predictors for sentinel lymph node (SLN) metastasis in early breast cancer patients and provide evidence for rational decision-making in specified clinical situations. MATERIAL AND METHODS Medical records of 417 breast cancer patients who were treated with a breast surgical procedure and SLNB in Ningbo Medical Center Lihuili Eastern Hospital were retrospectively reviewed. Univariate analysis and multivariate logistic regression analysis were used to analyze the correlation between SLN metastasis and clinicopathological characteristics, including patient age, menstrual status, body mass index (BMI), family history, tumor size, laterality of tumor, histological grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), Ki67 index, and molecular subtypes of the tumor. RESULTS In the cohort of 417 cases, the ratio of SLNM was 23.0%. Univariate analysis found that age, tumor size, histological grade, and Ki67 index were associated with SLN metastasis. However, age, tumor size, and histological grade were the only three independent predictors for SLN metastasis by multivariate logistic regression analysis. When these three factors were considered together, three different levels of SLN metastasis groups could be classified: low-risk group with the ratio of 14.3%, moderate-risk group with the ratio of 31.4%, and high-risk group with the ratio of 66.7%. CONCLUSIONS Our study demonstrated that age, tumor size, and histological grade were three independent predictive factors for SLN metastasis in early breast cancer patients. This finding may help surgeons in the decision-making process for early breast cancer patients before considering axilla surgical procedure.

Development of nomograms to predict axillary lymph node status in breast cancer patients

Background

Prediction of axillary lymph node (ALN) status preoperatively is critical in the management of breast cancer patients. This study aims to develop a new set of nomograms to accurately predict ALN status.

Methods

We searched the National Cancer Database to identify eligible female breast cancer patients with profiles containing critical information. Patients diagnosed in 2010–2011 and 2012–2013 were designated the training (n = 99,618) and validation (n = 101,834) cohorts, respectively. We used binary logistic regression to investigate risk factors for ALN status and to develop a new set of nomograms to determine the probability of having any positive ALNs and N2–3 disease. We used ROC analysis and calibration plots to assess the discriminative ability and accuracy of the nomograms, respectively.

Results

In the training cohort, we identified age, quadrant of the tumor, tumor size, histology, ER, PR, HER2, tumor grade and lymphovascular invasion as significant predictors of ALNs status. Nomogram-A was developed to predict the probability of having any positive ALNs (P_any) in the full population with a C-index of 0.788 and 0.786 in the training and validation cohorts, respectively. In patients with positive ALNs, Nomogram-B was developed to predict the conditional probability of having N2–3 disease (P_con) with a C-index of 0.680 and 0.677 in the training and validation cohorts, respectively. The absolute probability of having N2–3 disease can be estimated by P_any*P_con. Both of the nomograms were well-calibrated.

Conclusions

We developed a set of nomograms to predict the ALN status in breast cancer patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3535-7) contains supplementary material, which is available to authorized users.

Computer-aided prediction of axillary lymph node status in breast cancer using tumor surrounding tissue features in ultrasound images

BACKGROUND AND OBJECTIVE

The presence or absence of axillary lymph node (ALN) metastasis is the most important prognostic factor for patients with early-stage breast cancer. In this study, a computer-aided prediction (CAP) system using the tumor surrounding tissue features in ultrasound (US) images was proposed to determine the ALN status in breast cancer.

METHODS

The US imaging database used in this study contained 114 cases of invasive breast cancer and 49 of them were ALN metastasis. After the tumor region segmentation by the level set method, image matting method was used to extract surrounding abnormal tissue of tumor from the acquired images. Then, 21 features composed of 2 intensity, 3 morphology, and 16 textural features are extracted from the surrounding tissue and processed by a logistic regression model. Finally, the prediction model is trained and tested from the selected features.

RESULTS

In the experiments, the textural feature set extracted from surrounding tissue showed higher performance than intensity and morphology feature sets (Az, 0.7756 vs 0.7071 and 0.6431). The accuracy, sensitivity, specificity and the area index Az under the receiver operating characteristic (ROC) curve for the CAP system were 81.58% (93/114), 81.63% (40/49), 81.54% (53/65), and 0.8269 for using combined feature set.

CONCLUSIONS

These results indicated that the proposed CAP system can be helpful to determine the ALN status in patients with breast cancer.

Predicting level 2 axillary lymph node metastasis in a Chinese breast cancer population post-neoadjuvant chemotherapy: development and assessment of a new predictive nomogram

Background

We aimed to develop a new nomogram to predict the probability of level 2 axillary lymph node metastasis (L-2-ALNM) in breast cancer (BC) patients treated with neoadjuvant chemotherapy (NAC).

Methods

Data were collected from 709 patients who received neoadjuvant chemotherapy and then underwent axillary lymph node (ALN) dissection between May 2009 and December 2015 at the Liaoning Cancer Hospital. The level 2 axillary lymph node metastasis (L-2-ALNM ) nomogram was created from the logistic regression model. An additional set of 141 consecutive patients treated at the same institution between January 2015 and December 2015 were enrolled as the validation group. The predictive accuracy of the L-2-ALNM nomogram was measured by calculating the area under the receiver operating characteristic curve (AUC).

Results

In multivariate analysis, age, tumor size, histological grade, skin invasion, and response to neoadjuvant chemotherapy were identified as independent predictors of L-2-ALNM. The new model was accurate and discriminating for both the modeling and validation groups (AUC: 0.819 vs 0.849). The false-negative rates of the L-2-ALNM nomogram were 4.44% and 7.69% for the predicted probability cut-off points of 10% and 20%.

Conclusion

The L-2-ALNM nomogram shows reasonable accuracy for making clinical decisions. The omission of level 2 axillary lymph node dissection after neoadjuvant chemotherapy might be possible if the probability of level 2 lymph node involvement was < 10% or < 20% in accordance with the acceptable risk determined by medical staff and patients.

Prognostic and clinicopathological value of NM23 expression in patients with breast cancer: A systematic review and meta-analysis

It is hypothesized that, NM23, as a metastasis suppressor gene, may be a good indicator of patients with breast cancer in most reports. The aim of our meta-analysis was to determine the prognostic value of NM23 in patients with breast cancer synthetically, by searching 3 databases, PubMed, EMBASE, and Web of Science, for relevant articles. The inclusion criteria, exclusion criteria, and the standard-of-quality assessment were used according to a previous protocol. The pooled odd ratios (ORs) and corresponding 95% CI were calculated to assess the primary end point, survival data, and the secondary end point, associations between NM23 expression and clinicopathological factors. Finally, funnel plots and Egger׳s linear regression test were used to assess the potential publication bias. Overall, 792 articles were retrieved in the initial search of databases, and 4968 patients were eventually pooled from 26 available studies selected out by 2 independent reviewers. The incorporative OR showed that elevated NM23 expression was associated with better overall survival (OR = 0.62; 95% CI: 0.52-0.74; P < 0.00001; I² = 0%; Ph = 0.46). In disease-free survival, we also obtained a good prognosis (OR = 0.30; 95% CI: 0.18-0.48; P < 0.00001; I² = 46%; Ph = 0.13). In addition, high-NM23 expression was correlated with well or moderate histologic grade, negative lymph node metastasis, and early tumor staging. Furthermore, publication bias was detected in overall survival but not in disease-free survival, and it could also be verified by Egger׳s test (P = 0.009 and P = 0.687, respectively). These results implied that NM23 might be an indicator of good prognosis in patients with breast cancer, although further researches need to be performed to confirm the prognostic value of NM23.

Placental Kisspeptins Differentially Modulate Vital Parameters of Estrogen Receptor-Positive and -Negative Breast Cancer Cells

Kisspeptins (KPs) are major regulators of trophoblast and cancer invasion. Thus far, limited and conflicting data are available on KP-mediated modulation of breast cancer (BC) metastasis; mostly based on synthetic KP-10, the most active fragment of KP. Here, we report for the first time comprehensive functional effects of term placental KPs on proliferation, adhesion, Matrigel invasion, motility, MMP activity and pro-inflammatory cytokine production in MDA-MB-231 (estrogen receptor-negative) and MCF-7 (estrogen receptor-positive). KPs were expressed at high level by term placental syncytiotrophoblasts and released in soluble form. Placental explant conditioned medium containing KPs (CM) significantly reduced proliferation of both cell types compared to CM without (w/o) KP (CM-w/o KP) in a dose- and time-dependent manner. In MDA-MB-231 cells, placental KPs significantly reduced adhesive properties, while increased MMP9 and MMP2 activity and stimulated invasion. Increased invasiveness of MDA-MB-231 cells after CM treatment was inhibited by KP receptor antagonist, P-234. CM significantly reduced motility of MCF-7 cells at all time points (2–30 hr), while it stimulated motility of MDA-MB-231 cells. These effects were reversed by P-234. Co-treatment with selective ER modulators, Tamoxifen and Raloxifene, inhibited the effect of CM on motility of MCF-7 cells. The level of IL-6 in supernatant of MCF-7 cells treated with CM was higher compared to those treated with CM-w/o KP. Both cell types produced more IL-8 after treatment with CM compared to those treated with CM-w/o KP. Taken together, our observations suggest that placental KPs differentially modulate vital parameters of estrogen receptor-positive and -negative BC cells possibly through modulation of pro-inflammatory cytokine production.

A nomogram to predict the probability of axillary lymph node metastasis in early breast cancer patients with positive axillary ultrasound

Among patients with a preoperative positive axillary ultrasound, around 40% of them are pathologically proved to be free from axillary lymph node (ALN) metastasis. We aimed to develop and validate a model to predict the probability of ALN metastasis as a preoperative tool to support clinical decision-making. Clinicopathological features of 322 early breast cancer patients with positive axillary ultrasound findings were analyzed. Multivariate logistic regression analysis was performed to identify independent predictors of ALN metastasis. A model was created from the logistic regression analysis, comprising lymph node transverse diameter, cortex thickness, hilum status, clinical tumour size, histological grade and estrogen receptor, and it was subsequently validated in another 234 patients. Coefficient of determination (R(2)) and the area under the ROC curve (AUC) were calculated to be 0.9375 and 0.864, showing good calibration and discrimination of the model, respectively. The false-negative rates of the model were 0% and 5.3% for the predicted probability cut-off points of 7.1% and 13.8%, respectively. This means that omission of axillary surgery may be safe for patients with a predictive probability of less than 13.8%. After further validation in clinical practice, this model may support increasingly limited surgical approaches to the axilla in breast cancer.

Polymorphisms rs12998 and rs5780218 in KiSS1 suppressor metastasis gene in Mexican patients with breast cancer

AIMS

KiSS1 is a metastasis suppressor gene associated with inhibition of cellular chemotaxis and invasion attenuating the metastasis in melanoma and breast cancer cell lines. Along the KiSS-1 gene at least 294 SNPs have been described; however the association of these polymorphisms as genetic markers for metastasis in breast cancer studies has not been investigated. Here we describe two simple PCR-RFLPs protocols to identify the rs5780218 (9DelT) and the rs12998 (E20K) KiSS1 polymorphisms and the allelic, genotypic, and haplotypic frequencies in Mexican general population (GP) and patients with benign breast disease (BBD) or breast cancer (BC).

RESULTS

The rs5780218 polymorphism was individually associated with breast cancer (P = 0.0332) and the rs12998 polymorphism shows statistically significant differences when GP versus case (BC and BBD) groups were compared (P < 0.0001). The H1 Haplotype (G/-) occurred more frequently in BC group (0.4256) whereas H2 haplotype (G/T) was the most prevalent in BBD group (0.4674).

CONCLUSIONS

Our data indicated that the rs5780218 polymorphism individually confers susceptibility for development of breast cancer in Mexican population and a possible role as a genetic marker in breast cancer metastasis for H1 haplotype (Wt/variant) in KiSS1 gene must be analyzed in other populations.

Association between molecular subtypes and lymph node status in invasive breast cancer

BACKGROUND

The predictors for the involvement of lymph node (LN) have been widely studied. But the implication of the molecular type has not been well studied. Using the database of our institution, we investigated this relation.

METHODS

Patients with T1 and T2 primary breast cancer without distant metastasis were included in our study from 2012 Jan to 2013 Dec. All patients undertook the resection of the primary and the axillary lymph nodes (ALNs). We collected the clinical data including age at diagnosis, the status of ER, PR and HER2, tumor size, nodal status, and histological type. The relationship between demographic, tumor characteristics and lymph node status was evaluated.

RESULTS

814 patients were included in our study. The number and the percentage (in parentheses) of each type of breast cancer is as follows: Luminal A 230 (28.3%), Luminal Her2- 284 (34.9%), Luminal Her2+ 104 (12.8%), HER2+ 72 (8.8%), TNBC 124 (15.2%). On univariate and multivariate analysis, tumor size and tumor subtype show statistical significance with LN involvement. Using TNBC as a reference, both Luminal B type (Luminal HER2-, Luminal HER2+) shows significant higher probability of LN involvement.

CONCLUSIONS

LN involvement is an intrinsic characteristic for molecular subtype of breast cancer. Triple positive and triple negative breast cancer accounts the most and least possibility of LN involvement.

A new model for predicting non-sentinel lymph node status in Chinese sentinel lymph node positive breast cancer patients

BACKGROUND

Our goal is to validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram and Stanford Online Calculator (SOC) for predicting non-sentinel lymph node (NSLN) metastasis in Chinese patients, and develop a new model for better prediction of NSLN metastasis.

METHODS

The MSKCC nomogram and SOC were used to calculate the probability of NSLN metastasis in 120 breast cancer patients. Univariate and multivariate analyses were performed to evaluate the relationship between NSLN metastasis and clinicopathologic factors, using the medical records of the first 80 breast cancer patients. A new model predicting NSLN metastasis was developed from the 80 patients.

RESULTS

The MSKCC and SOC predicted NSLN metastasis in a series of 120 patients with an area under the receiver operating characteristic curve (AUC) of 0.688 and 0.734, respectively. For predicted probability cut-off points of 10%, the false-negative (FN) rates of MSKCC and SOC were both 4.4%, and the negative predictive value (NPV) 75.0% and 90.0%, respectively. Tumor size, Kiss-1 expression in positive SLN and size of SLN metastasis were independently associated with NSLN metastasis (p<0.05). A new model (Peking University People's Hospital, PKUPH) was developed using these three variables. The MSKCC, SOC and PKUPH predicted NSLN metastasis in the second 40 patients from the 120 patients with an AUC of 0.624, 0.679 and 0.795, respectively.

CONCLUSION

MSKCC nomogram and SOC did not perform as well as their original researches in Chinese patients. As a new predictor, Kiss-1 expression in positive SLN correlated independently with NSLN metastasis strongly. PKUPH model achieved higher accuracy than MSKCC and SOC in predicting NSLN metastasis in Chinese patients.

Nipple discharge of CA15-3, CA125, CEA and TSGF as a new biomarker panel for breast cancer

Breast cancer is the second leading cause of cancer death in women. Serum biomarkers such as cancer antigen 15-3 (CA15-3), cancer antigen 125 (CA125), and carcinoembryonic antigen (CEA) can be used as diagnostic and prognostic factors and can also provide valuable information during follow-up. However, serum protein biomarkers show limited diagnostic sensitivity and specificity in stand-alone assays because their levels reflect tumor burden. To validate whether biomarkers in nipple discharge may serve as novel biomarkers for breast cancer, we composed a panel of potential cancer biomarkers, including CA15-3, CA125, CEA, and malignant tumor-specific growth factor (TSGF), and evaluated their expression in both serum and nipple discharge in order to explore the expression and significance of estrogen receptor (ER), progestrone receptor (PR), epidermal growth factor receptor type 2 (HER2/neu), CA15-3, CA125, CEA, and TSGF expression for their combined predictive value for breast cancer and in judging the prognosis of breast cancer. Univariate analysis revealed that combined detection of CA15-3, CA125, CEA, and TSGF in nipple discharge served as novel biomarkers for the diagnosis and prognosis of breast cancer, but in the multivariate analyses the adverse effects of the four biomarkers combination in nipple discharge positivity on overall survival were lost. Multivariate analysis revealed that the positivity of the combined detection of the four biomarkers in both nipple discharge and serum was significantly higher than that of other detection methods. Thus, the combined detection of these four biomarkers both in serum and nipple discharge was retained as an independent prognostic variable in breast cancer patients. Our results indicate that CA15-3, CA125, CEA, and TSGF in nipple discharge can serve as novel biomarkers in the diagnosis and prognosis of breast cancer.

Breast cancer prognosis risk estimation using integrated gene expression and clinical data

BACKGROUND

Novel prognostic markers are needed so newly diagnosed breast cancer patients do not undergo any unnecessary therapy. Various microarray gene expression datasets based studies have generated gene signatures to predict the prognosis outcomes, while ignoring the large amount of information contained in established clinical markers. Nevertheless, small sample sizes in individual microarray datasets remain a bottleneck in generating robust gene signatures that show limited predictive power. The aim of this study is to achieve high classification accuracy for the good prognosis group and then achieve high classification accuracy for the poor prognosis group.

METHODS

We propose a novel algorithm called the IPRE (integrated prognosis risk estimation) algorithm. We used integrated microarray datasets from multiple studies to increase the sample sizes (∼ 2,700 samples). The IPRE algorithm consists of a virtual chromosome for the extraction of the prognostic gene signature that has 79 genes, and a multivariate logistic regression model that incorporates clinical data along with expression data to generate the risk score formula that accurately categorizes breast cancer patients into two prognosis groups.

RESULTS

The evaluation on two testing datasets showed that the IPRE algorithm achieved high classification accuracies of 82% and 87%, which was far greater than any existing algorithms.

Kisspeptin/KISS1R System in Breast Cancer

Kisspeptins (KP), peptide products of the kisspeptin-1 (KISS1) gene are the endogenous ligands for a G protein-coupled receptor (GPCR) - KP receptor (KISS1R). KISS1R couples to the Gα_q/11 signaling pathway. KISS1 is a metastasis suppressor gene and the KP/KISS1R signaling has anti-metastatic and tumor-suppressant effects in numerous human cancers. On the other hand, recent studies indicate that KP/KISS1R pathway plays detrimental roles in breast cancer. In this review, we summarize recent developments in the understanding of the mechanisms regulating KP/KISS1R signaling in breast cancer metastasis.