Predictive factors for sentinel node metastases in primary invasive breast cancer: a population-based cohort study of 2552 consecutive patients

Construction and validation of a nomogram prediction model for axillary lymph node metastasis of cT1 invasive breast cancer

OBJECTIVE

Based on the ultrasonic characteristics of the breast mass and axillary lymph nodes as well as the clinicopathological information, a model was developed for predicting axillary lymph node metastasis in cT1 breast cancer, and relevant features associated with axillary lymph node metastasis were identified.

METHODS

Our retrospective study included 808 patients with cT1 invasive breast cancer treated at the Second Affiliated Hospital and the Cancer Hospital Affiliated with Harbin Medical University from February 2012 to August 2021 (250 cases in the positive axillary lymph node group and 558 cases in the negative axillary lymph node group). We allocated 564 cases to the training set and 244 cases to the verification set. R software was used to compare clinicopathological data and ultrasonic features between the two groups. Based on the results of multivariate logistic regression analysis, a nomogram prediction model was developed and verified for axillary lymph node metastasis of cT1 breast cancer.

RESULTS

Univariate and multivariate logistic regression analysis indicated that palpable lymph nodes ( P  = 0.003), tumor location ( P  = 0.010), marginal contour ( P  < 0.001), microcalcification ( P  = 0.010), surrounding tissue invasion ( P  = 0.046), ultrasonic detection of lymph nodes ( P  = 0.001), cortical thickness ( P  < 0.001) and E-cadherin ( P  < 0.001) are independently associated with axillary lymph node metastasis. Using these features, a nomogram was developed for axillary lymph node metastasis. The training set had an area under the curve of 0.869, while the validation set had an area under the curve of 0.820. Based on the calibration curve, the model predicted axillary lymph node metastases were in good agreement with reality ( P  > 0.05). Nomogram's net benefit was good based on decision curve analysis.

CONCLUSION

The nomogram developed in this study has a high negative predictive value for axillary lymph node metastasis in invasive cT1 breast c ancer. Patients with no axillary lymph node metastases can be accurately screened using this nomogram, potentially allowing this group of patients to avoid invasive surgery.

Investigating the role of tumour-to-skin proximity in predicting nodal metastasis in breast cancer

BACKGROUND

Understanding the factors influencing nodal status in breast cancer is vital for axillary staging, therapy, and patient survival. The nodal stage remains a crucial factor in prognostication indices. This study investigates the relationship between tumour-to-skin distance (in T1-T3 tumours where the skin is not clinically involved) and the risk of nodal metastasis.

METHODS

We retrospectively reviewed data from 100 patients who underwent neoadjuvant chemotherapy (NACT). Besides patient demographics and tumour variables, a radiologist retrospectively reviewed pre-operative MRI to measure tumour-to-skin distance. R core packages were used for univariate (χ2 and T-Wilcoxon tests) and bivariate logistic regression statistical analysis.

RESULTS

Of 95 analysable datasets, patients' median age was 51 years (IQR: 42-61), 97% were symptomatic (rest screen detected), and the median tumour size was 43 mm (IQR, 26-52). On multivariate analysis, increasing invasive tumour size (p = 0.02), ER positivity (p = 0.007) and shorter tumour-to-skin distance (p = 0.05) correlated with nodal metastasis.  HER2 was not included in multivariate analysis as there was no association with nodal status on univariate analysis. In node-positive tumours, as tumour size increased, the tumour-to-skin distance decreased (r = - 0.34, p = 0.026). In node-negative tumours, there was no correlation (r = + 0.18, p = 0.23).

CONCLUSION

This study shows that non-locally advanced cancers closer to the skin (and consequent proximity to subdermal lymphatics) are associated with a greater risk of nodal metastasis. Pre-operative identification of those more likely to be node positive may suggest the need for a second-look USS since a higher nodal stage may lead to a change in therapeutic strategies, such as upfront systemic therapy, node marking, and axillary clearance without the need to return to theatre following sentinel node biopsy.

Predictors of nodal metastases in early stage HER2+ breast cancer: Deciding on treatment approach with neoadjuvant chemotherapy vs. upfront surgery

BACKGROUND

The purpose of this study is to evaluate preoperative predictors of nodal metastases in patients with early-stage, HER2-positive (HER2+) breast cancer.

METHODS

The SEER Database was queried to identify women with a first diagnosis of stage I-II (T1-T2) HER2-positive breast cancer treated with upfront surgery in 2018. Multivariable logistic regression was used to identify clinical characteristics independently associated with nodal involvement.

RESULTS

Overall, 3333 women with stage I-II HER2+ breast cancer met inclusion criteria and were included in the study. The median age at diagnosis was 59 years (IQR, 51-69 years). Most patients underwent breast-conserving surgery (60.9%), with a median of 3 (IQR 2-4) axillary lymph nodes removed. On final pathology, 762 (22.9%) of T1-T2 HER2+ patients were node positive; 2.7% pN0[i+], 3.7% pN1mi, 15.1% pN1, and 1.4% pN2. Women less than 40 years and those between 40 and 49 years showed the highest proportion of axillary lymph node metastasis, in 33.7% and 30.7% respectively, and declining with age (p < 0.001). Patients with triple-positive breast cancer had the highest rates of nodal involvement (24.8%), compared to 20.7% ER+/PR-/HER2+ and 19.6% of HER2-enriched patients (p = 0.006). On adjusted analysis, age, biologic subtype, tumour size, and type of surgery remained independent predictors of nodal involvement. On subgroup analysis, women under age 50 with T1c HER2-enriched or triple-positive breast cancer had a 33% and 35% incidence of nodal involvement, which declined with age.

CONCLUSIONS

The likelihood of pathologic nodal involvement in early-stage HER2+ breast cancer is contingent on age, ER/PR status, and tumour size.

Assessing Metabolic Risk Factors for LVSI in Endometrial Cancer: A Cross-Sectional Study

Objective

This study analyzed metabolic factors associated with lymphovascular space invasion (LVSI) and compared the difference between type 1 and type 2 endometrial cancer (EC).

Methods

Four hundred patients primarily diagnosed with EC who underwent hysterectomy with pathological results at Fujian Medical University Cancer Hospital from January 2019 to January 2021 were included. Demographic variable data were collected as well as pathological results. Laboratory evaluations included fasting blood glucose (FBG), serum cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), apolipoprotein A (Apo A) and apolipoprotein B (Apo B). Characterization of binary logistic regression models was used to test the odds ratios (ORs) between LVSI and its metabolic parameters with different subtypes of EC.

Results

The results indicated that CA125, ROMA, Ki67 score, FBG and TC were higher in EC patients with LVSI (all p<0.05). Negative ER and PR expression was positively associated with LVSI (P<0.05). CA125, ROMA, FBG, TC and ER were found to be independent risk factors for LVSI. CA125, ROMA and FBG were significantly elevated in type 1 EC patients with LVSI compared with those without LVSI (all p<0.05). TC and Ki67 scores were much higher in type 2 EC patients with vs without LVSI (all p<0.05). Negative PR expression was positively related to both type 1 and type 2 EC patients with LVSI. Consequently, CA125, ROMA, FBG and Apo B were found to be independent risk factors for LVSI in type 1 EC, and TC was found to be an independent risk factor for LVSI in type 2 EC.

Conclusion

FBG and TC were both independent risk factors for LVSI in EC. FBG and Apo B were independent risk factors for LVSI in type 1 EC. TC was an independent risk factor for LVSI in type 2 EC.

Prediction of Sentinel Lymph Node Biopsy Status in Breast Cancers with PET/CT Negative Axilla

Background and Aim  Prior knowledge of axillary node status can spare a lot of patients with early breast cancer morbidity due to an unnecessary axillary dissection. Our study compared various metabolic and pathological features that can predict the sentinel lymph node biopsy (SLNB) status in patients with positron emission tomography/computed tomography (PET/CT) negative axilla.

Patients and Methods  All consecutive patients with early breast cancers (< 5 cm) with PET/CT negative axilla who underwent breast surgery and SLNB from November 2016 to February 2020 were included. Various primary tumor (PT) pathological variables and metabolic variables on PET/CT such as maximum standardized uptake value (PT-SUV _max ), metabolic tumor volume (PT-MTV), and total lesion glycolysis (PT-TLG) were compared using univariate and multivariate analyses for prediction of SLNB status.

Results  Overall 70 patients, all female, with mean age 55.6 years (range: 33–77) and mean tumor size 2.2 cm (range: 0.7–4.5), were included. SLNB was positive in 20% of patients ( n  = 14) with nonsentinel nodes positive in 4% ( n  = 3) patients. Comparing SLNB positive and negative groups, univariate analysis showed significant association of SLNB with low tumor grade, positive lymphovascular invasion (LVI), positive estrogen receptor (ER) status with lower mean K _i -67 index (34.41 vs. 52.02%; p  = 0.02), PT-SUV _max (5.40 vs. 8.68; p  = 0.036), PT-MTV (4.71 cc vs. 7.46 cc; p  = 0.05), and PT-TLG (15.12 g/mL.cc vs. 37.10 g/mL.cc; p  = 0.006). On multivariate analysis, only LVI status was a significant independent predictor of SLNB status (odds ratio = 6.23; 95% confidence interval: 1.15–33.6; p  = 0.033).

Conclusion  SLNB is positive in approximately 20% of early breast cancers with PET/CT negative axilla and SLNB status appears to be independent of PT size. SLNB+ PTs were more likely to be LVI+ and ER + ve, with lower grade/K _i -67/metabolic activity (SUV _max /MTV/TLG) compared with SLNB–ve tumors. Logistic regression analysis revealed LVI status as the only significant independent predictor of sentinel lymph node status.

Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

Purpose

To explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer.

Methods

The clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (n=123) and validation set (n= 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated.

Results

There is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set.

Conclusions

We revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients.

Sentinel Lymph Node Biopsy in Early Breast Cancer Using Methylene Blue Dye Alone: a Safe, Simple, and Cost-Effective Procedure in Resource-Constrained Settings

Sentinel lymph node biopsy (SLNB) is done by different techniques in clinically node-negative patients with early breast cancer. In this study, we aim to estimate the identification rates, positivity rates, cost-effectiveness, and outcomes for patients who underwent sentinel node biopsy using methylene blue dye alone. This was a retrospective review of 172 patients with early breast cancer (cT1-3, N0) who underwent SLNB using methylene blue dye alone between January 2014 and December 2018 including their follow-up details until December 2019. The mean age was 51 ± 10.3 (range: 28 to 76) years. There were 63 (36.6%) patients with cT1 tumor, 108 (62.7%) with cT2, and only 1 patient with cT3 tumor. Breast conservation surgery was performed in 62 (36%) while the remaining 110 (64%) underwent simple mastectomy. Sentinel nodes were successfully identified in 165 (95.9%) with a positivity rate of 23.6%. There was no dye-related adverse reactions intra-operatively. The mean duration of follow-up was 26.68 ± 15.9 months (range: 1-60). Chronic arm pain was present in 7 (4%) while none of the patients had lymphedema or restriction of shoulder joint motion. There were no documented axillary nodal recurrences in this cohort. Eight (4.65%) patients were detected to have systemic metastasis. One patient died of brain metastasis from bilateral breast cancer. The mean disease-free survival was 57 months (95% CI: 55-59). Sentinel lymph node biopsy using methylene dye alone is a safe, simple, and cost-effective alternative to isosulfan blue or radio isotope technique in surgical centers with resource constraints.

Delay in surgery is associated with axillary upstaging of clinically node negative breast cancer patients

BACKGROUND AND OBJECTIVES

Most breast cancer (BC) patients present with early disease and clinically negative lymph nodes (cN0). Timing of surgery has not been standardized. We hypothesized that surgical delay results in an increased likelihood of nodal metastasis.

METHODS

Patients diagnosed with cN0 BC undergoing surgery with sentinel lymph node biopsy as initial therapy between 2006 and 2014 were identified in the NCDB and divided into four groups based on time intervals between diagnosis and surgery (<4 weeks, 4-8 weeks, 8-12 weeks, and >12 weeks). Regression analysis evaluated the independent impact of surgical timing on axillary upstaging and survival.

RESULTS

Of 355,443 patients with cN0 BC, 39.6% had surgery within 4 weeks and 5.4% more than 12 weeks from diagnosis. After controlling for relevant factors, a month delay in surgery was associated with an increased likelihood of nodal positivity (odds ratio: 1.04; 95% confidence interval [CI]: 1.04-1.05; p < .001) and decreased overall survival (hazard ratio: 1.03; 95% CI: 1.02-1.04; p < .001). When compared to patients who underwent surgery less than 4 weeks from diagnosis, the absolute increase in nodal positivity and relative risks were 5.3% (95% CI: 0.047-0.059) and 1.34 (95% CI: 1.30-1.38), respectively, in the more than 12 weeks group.

CONCLUSIONS

Delay in BC surgery in cN0 patients was associated with an increased likelihood of axillary upstaging and decreased survival.

Axillary metastasis in clinically node-negative breast cancer

AIM OF WORK

This study investigates the factors related to metastasis detection in a sentinel lymph node biopsy (SLN) in patients without clinical axillary involvement.

PATIENTS AND METHODS

The medical records of patients who underwent an SLN biopsy after diagnosis with early-stage breast cancer were evaluated retrospectively. The study sample included 64 patients divided into two groups according to the histopathological examination of the SLN biopsy: Group I (positive for axillary metastasis) and Group II (negative for axillary metastasis).

RESULTS

The frequency of lymphovascular invasion was significantly higher in Group I (57%) than in Group II (13%) (p = 0.003). The progesterone receptor status (p = 0.036), tumor T-stage(p = 0,047), and Ki-67 index differed significantly (p = 0.045) between the two groups. While in univariate analysis, lymphovascular invasion, T-stage and KI-67 index were significant, in multivariate analysis only lymphovascular invasion was found to be significant. No significant differences were found in terms of estrogen receptor and HER2 considering tumor invasion type, histologic grade, vascular invasion, neural invasion, multifocality or bilaterality, hormone receptor status, menopause status, total number of lymph nodes, presence of non-sentinel lymph nodes and the number of SLNs in the groups (p > 0.05).

CONCLUSIONS

The results of this study indicate that lymphovascular invasion is associated with axillary metastasis, based on an SLN biopsy.

Associations between obesity, smoking and lymph node status at breast cancer diagnosis in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial

INTRODUCTION

There is evidence suggesting that smoking and obesity prior to a breast cancer diagnosis is associated with poorer outcomes. In this study, we investigate the associations between smoking and obesity prior to a breast cancer diagnosis and the presence of lymph node metastases at diagnosis.

METHODS

Women with stage I-III breast cancer (n = 3,304) were identified from the National Cancer Institute's Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Univariable and multivariable log-binomial models were used to estimate relative risks (RR) and 95% confidence intervals (CIs) for associations between lymph node positive breast cancer and; i) smoking, and ii) obesity prior to diagnosis.

RESULTS

Pre-diagnostic smoking/obesity was not associated with lymph node metastasis at diagnosis in multivariable analyses; (RR 0.82, 95%CI 0.61, 1.10) and (RR 0.95, 95% CI 0.81, 1.12), respectively.

CONCLUSION

Obesity and smoking information was recorded a number of years prior to breast cancer diagnosis, therefore these findings should to be replicated in a larger cohort of women, with more detailed smoking and obesity information.