Predictors of sentinel lymph node metastases in breast cancer-radioactivity and Ki-67

Prediction of axillary lymph node metastasis using a magnetic resonance imaging radiomics model of invasive breast cancer primary tumor

BACKGROUND

This study investigated the clinical value of breast magnetic resonance imaging (MRI) radiomics for predicting axillary lymph node metastasis (ALNM) and to compare the discriminative abilities of different combinations of MRI sequences.

METHODS

This study included 141 patients diagnosed with invasive breast cancer from two centers (center 1: n = 101, center 2: n = 40). Patients from center 1 were randomly divided into training set and test set 1. Patients from center 2 were assigned to the test set 2. All participants underwent preoperative MRI, and four distinct MRI sequences were obtained. The volume of interest (VOI) of the breast tumor was delineated on the dynamic contrast-enhanced (DCE) postcontrast phase 2 sequence, and the VOIs of other sequences were adjusted when required. Subsequently, radiomics features were extracted from the VOIs using an open-source package. Both single- and multisequence radiomics models were constructed using the logistic regression method in the training set. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and precision of the radiomics model for the test set 1 and test set 2 were calculated. Finally, the diagnostic performance of each model was compared with the diagnostic level of junior and senior radiologists.

RESULTS

The single-sequence ALNM classifier derived from DCE postcontrast phase 1 had the best performance for both test set 1 (AUC = 0.891) and test set 2 (AUC = 0.619). The best-performing multisequence ALNM classifiers for both test set 1 (AUC = 0.910) and test set 2 (AUC = 0.717) were generated from DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging single-sequence ALNM classifiers. Both had a higher diagnostic level than the junior and senior radiologists.

CONCLUSIONS

The combination of DCE postcontrast phase 1, T2-weighted imaging, and diffusion-weighted imaging radiomics features had the best performance in predicting ALNM from breast cancer. Our study presents a well-performing and noninvasive tool for ALNM prediction in patients with breast cancer.

Toward Exempting from Sentinel Lymph Node Biopsy in T1 Breast Cancer Patients: A Retrospective Study

Background and Objective

Sentinel lymph node biopsy (SLNB) is used to assess the status of axillary lymph node (ALN), but it causes many adverse reactions. Considering the low rate of sentinel lymph node (SLN) metastasis in T1 breast cancer, this study aims to identify the characteristics of T1 breast cancer without SLN metastasis and to select T1 breast cancer patients who avoid SLNB through constructing a nomogram.

Methods

A total of 1,619 T1 breast cancer patients with SLNB in our hospital were enrolled in this study. Through univariate and multivariate logistic regression analysis, we analyzed the tumor anatomical and clinicopathological factors and constructed the Heilongjiang Medical University (HMU) nomogram. We selected the patients exempt from SLNB by using the nomogram.

Results

In the training cohort of 1,000 cases, the SLN metastasis rate was 23.8%. Tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes were found to be independent predictors for SLN metastasis in multivariate regression analysis. Distance from nipple or surface and position of tumor have no effect on SLN metastasis. A regression model based on the results of the multivariate analysis was developed to predict the risk of SLN metastasis, indicating an AUC of 0.798. It showed excellent diagnostic performance (AUC = 0.773) in the validation cohort.

Conclusion

The HMU nomogram for predicting SLN metastasis incorporates four variables, including tumor volume, swollen axillary lymph nodes, pathological types, and molecular subtypes. The SLN metastasis rates of intraductal carcinoma and HER2 enriched are 2.05% and 6.67%. These patients could be included in trials investigating the SLNB exemption.

Clinicopathologic characteristics of HER2-positive metaplastic squamous cell carcinoma of the breast

AIMS

The aim of this study was to analyse the clinicopathological features and prognosis of human epidermal growth factor receptor-2 (HER2)-positive metaplastic squamous cell carcinoma (MSCC).

METHODS

Fifty-eight patients with MSCC of the breast who were classified into 45 triple-negative and 13 HER2-positive subgroups diagnosed at the West China Hospital, Sichuan University, from 2004 to 2018, were enrolled. Clinicopathological features were collected and compared between HER2-positive MSCC, triple-negative MSCC, HER2-positive invasive breast carcinoma of no special type (NST) and triple-negative NST groups. In the prognostic survival analysis, HER2-positive MSCCs was compared with triple-negative MSCCs, HER2-positive NSTs and triple-negative NSTs.

RESULTS

Compared with triple-negative MSCCs, more patients with Ki-67 low expression were in HER2-positive MSCCs (p<0.05). More patients with HER2-positive MSCC than patients with HER2-positive NST were postmenopausal (p<0.05). Compared among HER2-positive MSCCs, triple-negative MSCCs and triple-negative NSTs, patients of HER2-positive MSCCs with high Ki-67 expression were the least, and HER2-positive MSCCs had more strongly associated with postmenopausal disease status (p<0.05). In survival analyses, HER2-positive MSCCs had a high risk of recurrence and poor prognosis (p<0.05). Lymph node status was significantly associated with the disease-free survival of patients with HER2-positive MSCC.

CONCLUSION

In conclusion, our study indicates that HER2-positive MSCC is an aggressive disease with unique clinicopathological characteristics. Both HER2-positive status and an SCC component are critical factors for poor prognosis. HER2-positive MSCC and triple-negative MSCC are distinct subgroups. Corresponding targeted therapy recommendations should be made for this HER2-positive MSCC group.

Biomarker phenotyping drives clinical management in axillary sentinel node: A retrospective study on women with primary breast cancer in 2002

The current study examined if cancer biomarker phenotyping could predict the clinical/pathological status of axillary nodes in women with primary breast cancer. Primary breast cancers from 2002 were analyzed for tumor size, estrogen receptor (ER), progesterone receptor (PgR), Ki-67MIB expression and Her2/neu amplification. Relationships between the clinical and pathological status of the axilla and the biological subtypes classification were analyzed using univariate, multivariate and regression tree analysis. A total of 65% of women with axillary nodes clinically involved had complete axillary node dissection (ALND) while 705 women with clinically negative axillary underwent sentinel lymph node biopsy (SLNB), 18.5% of the latter had at least one pathologically SLNB involved node. Multivariate analysis revealed that the Luminal A subtype was significantly associated (OR 0.62; P<10^-9) with clinical negative axilla while HER2pos/not Luminal was associated with clinical positivity (OR 1.71; P<0.01). No significant association between biological subtypes and SLNB status was demonstrated. Regression tree analysis revealed that subgroups with significantly different probability of SLNB status were separated according to tumor size and PgR values. In conclusion, the current study demonstrated that biomarker breast cancer phenotyping is significantly associated with clinical status of axillary nodes but not with pathological involvement of nodes at SLNB. Regression tree analysis could represent a valid attempt to individualize some patients subgroups candidate to different surgical axilla approaches.

Axillary nodal involvement by primary tumor features in early breast cancer: an analysis of 2600 patients

BACKGROUND

Primary tumor characteristics, which are readily available to all clinicians, may aid in selecting the optimal adjuvant therapy for patients with breast cancer (BC). Herein, we investigated the relationship between tumor size, hormone receptor and HER2 status, Ki67 and age with axillary lymph node metastases (ALNM) in early-BC patients.

METHODS

We analyzed data on consecutive 2600 early-BC cases collected in the registry of Fondazione IRCC Istituto Nazionale dei Tumori, Milano, Italy. Correlation between Ki67 and primary tumor size (T-size) was calculated by Spearman's rank correlation coefficient. Association of ALNM with Ki67 and other tumor characteristics was investigated by logistic regression. Adjusted odds ratios (ORs) with 95% confidence intervals (95% CIs) were estimated in all cases, and separately analyzed according to age, T-size and BC subtype.

RESULTS

Large tumor size strongly associated to ALNM, with an adjusted odds ratio (OR) for each 5-mm increase of 1.32 (95% CI 1.24-1.41), except for triple-negative BC (TNBC) cases. In tumors =10 mm, without lymphovascular invasion, representing the strongest predictor of ALNM (OR 6.09, 95% CI 4.93-7.53), Ki67 resulted particularly informative, with a fourfold increased odds of ALNM for values > 30%.

CONCLUSIONS

These results raise the question whether axillary node status is redundant in cases with exceptionally good features, i.e., small tumors with low Ki67, or in those candidate to adjuvant systemic treatment/radiotherapy anyway including TNBC, and support the incorporation of primary BC tumor characteristics as stratification factors in ongoing trials aiming at de-escalating axillary surgical procedures.

Differentiating the lymph node metastasis of breast cancer through dynamic contrast-enhanced magnetic resonance imaging

Objective:

Lymph node metastasis is an important trait of breast cancer, and tumors with different lymph node statuses require various clinical treatments. This study was designed to evaluate the lymph node metastasis of breast cancer through pharmacokinetic and histogram analysis via dynamic contrast-enhanced (DCE) MRI.

Methods and materials:

A retrospective analysis was conducted to quantitatively evaluate the lymph node statuses of patients with breast cancer. A total of 75 patients, i.e. 34 patients with lymph node metastasis and 41 patients without lymph node metastasis, were involved in this research. Of the patients with lymph node metastases, 19 had sentinel lymph node metastasis, and 15 had axillary lymph node metastasis. MRI was conducted using a 3.0 T imaging device. Segmentation was carried out on the regions of interest (ROIs) in breast tumors under DCE-MRI, and pharmacokinetic and histogram parameters were calculated from the same ROIs. Mann–Whitney U test was performed, and receiver operating characteristic curves for the parameters of the two groups were constructed to determine their diagnostic values.

Results:

Pharmacokinetic parameters, including Ktrans, Kep, area under the curve of time–concentration, and time to peak, which were derived from the extended Tofts linear model for DCE-MRI, could highlight the tumor areas in the breast and reveal the increased perfusion. Conversely, the pharmacokinetic parameters showed no significant difference between the patients with and without lymph node metastases. By contrast, the parameters from the histogram analysis yielded promising results. The entropy of the ROIs exhibited the best diagnostic ability between patients with and without lymph node metastases (p < 0.01, area under the curve of receiver operating characteristic = 0.765, specificity = 0.706, sensitivity = 0.780).

Conclusion:

In comparison with the pharmacokinetic parameters, the histogram analysis of the MR images could reveal the differences between patients with and without lymph node metastases. The entropy from the histogram indicated that the diagnostic ability was highly sensitive and specific.

Advances in knowledge:

This research gave out a promising result on the differentiating lymph node metastases through histogram analysis on tumors in DCE-MR images. Histogram could reveal the tumors heterogenicity between patients with different lymph node status.