Clinical and Pathological Factors Affecting the Sentinel Lymph Node Metastasis in Patients with Breast Cancer

A nomogram model for predicting the risk of axillary lymph node metastasis in patients with early breast cancer and cN0 status

Axillary staging is commonly performed via sentinel lymph node biopsy for patients with early breast cancer (EBC) presenting with clinically negative axillary lymph nodes (cN0). The present study aimed to investigate the association between axillary lymph node metastasis (ALNM), clinicopathological characteristics of tumors and results from axillary ultrasound (US) scanning. Moreover, a nomogram model was developed to predict the risk for ALNM based on relevant factors. Data from 998 patients who met the inclusion criteria were retrospectively reviewed. These patients were then randomly divided into a training and validation group in a 7:3 ratio. In the training group, receiver operating characteristic curve analysis was used to identify the cutoff values for continuous measurement data. R software was used to identify independent ALNM risk variables in the training group using univariate and multivariate logistic regression analysis. The selected independent risk factors were incorporated into a nomogram. The model differentiation was assessed using the area under the curve (AUC), while calibration was evaluated through calibration charts and the Hosmer-Lemeshow test. To assess clinical applicability, a decision curve analysis (DCA) was conducted. Internal verification was performed via 1000 rounds of bootstrap resampling. Among the 998 patients with EBC, 228 (22.84%) developed ALNM. Multivariate logistic analysis identified lymphovascular invasion, axillary US findings, maximum diameter and molecular subtype as independent risk factors for ALNM. The Akaike Information Criterion served as the basis for both nomogram development and model selection. Robust differentiation was shown by the AUC values of 0.855 (95% CI, 0.817-0.892) and 0.793 (95% CI, 0.725-0.857) for the training and validation groups, respectively. The Hosmer-Lemeshow test yielded P-values of 0.869 and 0.847 for the training and validation groups, respectively, and the calibration chart aligned closely with the ideal curve, affirming excellent calibration. DCA showed that the net benefit from the nomogram significantly outweighed both the 'no intervention' and the 'full intervention' approaches, falling within the threshold probability interval of 12-97% for the training group and 17-82% for the validation group. This underscores the robust clinical utility of the model. A nomogram model was successfully constructed and validated to predict the risk of ALNM in patients with EBC and cN0 status. The model demonstrated favorable differentiation, calibration and clinical applicability, offering valuable guidance for assessing axillary lymph node status in this population.

Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics

BACKGROUND

Preoperative assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) is of high clinical relevance for treatment decision-making and prognosis.

PURPOSE

To investigate the associations of preoperative clinical and magnetic resonance imaging (MRI) characteristics with LVI and disease-free survival (DFS) by using machine learning methods in patients with IBC.

STUDY TYPE

Retrospective.

POPULATION

Five hundred and seventy-five women (range: 24-79 years) with IBC who underwent preoperative MRI examinations at two hospitals, divided into the training (N = 386) and validation datasets (N = 189).

FIELD STRENGTH/SEQUENCE

Axial fat-suppressed T2-weighted turbo spin-echo sequence and dynamic contrast-enhanced with fat-suppressed T1-weighted three-dimensional gradient echo imaging.

ASSESSMENT

MRI characteristics (clinical T stage, breast edema score, MRI axillary lymph node status, multicentricity or multifocality, enhancement pattern, adjacent vessel sign, and increased ipsilateral vascularity) were reviewed independently by three radiologists. Logistic regression (LR), eXtreme Gradient Boosting (XGBoost), k-Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms were used to establish the models by combing preoperative clinical and MRI characteristics for assessing LVI status in the training dataset, and the methods were further applied in the validation dataset. The LVI score was calculated using the best-performing of the four models to analyze the association with DFS.

STATISTICAL TESTS

Chi-squared tests, variance inflation factors, receiver operating characteristics (ROC), Kaplan-Meier curve, log-rank, Cox regression, and intraclass correlation coefficient were performed. The area under the ROC curve (AUC) and hazard ratios (HR) were calculated. A P-value <0.05 was considered statistically significant.

RESULTS

The model established by the XGBoost algorithm had better performance than LR, SVM, and KNN models, achieving an AUC of 0.832 (95% confidence interval [CI]: 0.789, 0.876) in the training dataset and 0.838 (95% CI: 0.775, 0.901) in the validation dataset. The LVI score established by the XGBoost model was an independent indicator of DFS (adjusted HR: 2.66, 95% CI: 1.22-5.80).

DATA CONCLUSION

The XGBoost model based on preoperative clinical and MRI characteristics may help to investigate the LVI status and survival in patients with IBC.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

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.

Value of digital mammography in predicting lymphovascular invasion of breast cancer

Background

Lymphovascular invasion (LVI) has never been revealed by preoperative scans. It is necessary to use digital mammography in predicting LVI in patients with breast cancer preoperatively.

Methods

Overall 122 cases of invasive ductal carcinoma diagnosed between May 2017 and September 2018 were enrolled and assigned into the LVI positive group (n = 42) and the LVI negative group (n = 80). Independent t-test and χ2 test were performed.

Results

Difference in Ki-67 between the two groups was statistically significant (P = 0.012). Differences in interstitial edema (P = 0.013) and skin thickening (P = 0.000) were statistically significant between the two groups. Multiple factor analysis showed that there were three independent risk factors for LVI: interstitial edema (odds ratio [OR] = 12.610; 95% confidence interval [CI]: 1.061–149.922; P = 0.045), blurring of subcutaneous fat (OR = 0.081; 95% CI: 0.012–0.645; P = 0.017) and skin thickening (OR = 9.041; 95% CI: 2.553–32.022; P = 0.001).

Conclusions

Interstitial edema, blurring of subcutaneous fat, and skin thickening are independent risk factors for LVI. The specificity of LVI prediction is as high as 98.8% when the three are used together.

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.

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.