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

Correlation Analysis of Pathological Features and Axillary Lymph Node Metastasis in Patients with Invasive Breast Cancer

Objective

To investigate the risk factors of axillary lymph node metastasis in patients with invasive breast cancer.

Methods

This study retrospectively included 122 cases of invasive breast cancer patients admitted to the First Medical Center of PLA General Hospital from January 2019 to September 2020. According to postoperative pathological results, axillary lymph node metastasis was divided into axillary lymph node metastasis (ALNM) group (n =40) and non-axillary lymph node metastasis (NALNM) group (n =82). General demographic information was collected and compared between the two groups. Collected pathological results included lymphovascular invasion (LVI) and the expression of estrogen receptor (ER), progestogen receptor (PR), human epidermal growth factor receptor 2 (HER-2), and Ki-67 detected by immunohistochemistry. Imaging parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) including apparent diffusion coefficient (ADC), early enhanced rate, and time-intensity curve (TIC) were also included into univariate analysis. The variables with differences between the two groups were compared by univariate analysis, and the related factors of axillary lymph node metastasis were analyzed by logistic regression model.

Results

There was no significant difference in general demographic information between the two groups. No significant differences were found in the positive rates of HER-2, ER, PR, Ki-67, pathological types, and clavicular lymph node metastasis and skin chest wall invasion between the two groups (P > 0.05). The proportion of LVI in ALNM group was significantly higher than that in NALNM group (37.50% vs. 6.10%, P < 0.001). The proportion of breast cancer on the left side in the ALNM group was higher than that in the NALNM group, and the difference was statistically significant (70.00% vs. 47.56%, P = 0.019). There were no significant differences in the imaging parameters obtained by DCE-MRI between the two groups. Binary logistics regression analysis showed that LVI (OR =12.258, 95% CI =3.681-40.812, P < 0.001) and left breast cancer (OR =3.598, 95% CI =1.404-9.219, P = 0.008) were risk factors for axillary lymph node metastasis in patients with invasive breast cancer.

Conclusion

The formation of vascular tumor thrombi in breast cancer tissue and left breast cancer are risk factors for axillary lymph node metastasis in invasive breast cancer and might be helpful for preoperative detailed assessment of the patient's condition.

Development and Internal Validation of a Preoperative Prediction Model for Sentinel Lymph Node Status in Breast Cancer: Combining Radiomics Signature and Clinical Factors

Purpose

To develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients.

Methods

This study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive (n = 93) and negative (n = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong's test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the "Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" (TRIPOD) statement.

Results

The AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717-0.849) and 0.680 (95% CI, 0.604-0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737-0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783-0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all p < 0.05).

Conclusion

FGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.

Blood-Brain Barrier Repair of Bevacizumab and Corticosteroid as Prediction of Clinical Improvement and Relapse Risk in Radiation-Induced Brain Necrosis: A Retrospective Observational Study

Background

Blood-brain barrier (BBB) disruption after endothelial damage is a crucial part of radiation-induced brain necrosis (RN), but little is known of BBB disruption quantification and its role in the evaluation of therapeutic effect and prognosis for drug treatment. In this retrospective study, BBB repair by bevacizumab and corticosteroid and the correlation between BBB permeability and treatment response and relapse were evaluated by dynamic contrast-enhanced MRI (DCE-MRI).

Methods

Forty-one patients with RN after radiotherapy for nasopharyngeal carcinoma (NPC) (28 treated with bevacizumab and 13 with corticosteroid), 12 patients with no RN after NPC radiotherapy, and 12 patients with no radiotherapy history were included as RN, non-RN, and normal groups, respectively. DCE-MRI assessed BBB permeability in white matter of bilateral temporal lobe. DCE parameters were compared at baseline among the three groups. DCE parameters after treatment were compared and correlated with RN volume decrease, neurological improvement, and relapse.

Results

The extent of BBB leakage at baseline increased from the normal group and non-RN group and to RN necrosis lesions, especially K ^trans (Kruskal-Wallis test, P < 0.001). In the RN group, bevacizumab-induced K ^trans and v _e decrease in radiation necrosis lesions (both P < 0.001), while corticosteroid showed no obvious effect on BBB. The treatment response rate of bevacizumab was significantly higher than that of corticosteroid [30/34 (88.2%) vs. 10/22 (45.4%), P < 0.001]. Spearman analysis showed baseline K ^trans, K _ep, and v _p positively correlated with RN volume decrease and improvement of cognition and quality of life in bevacizumab treatment. After a 6-month follow-up for treatment response cases, the relapse rate of bevacizumab and corticosteroid was 10/30 (33.3%) and 2/9 (22.2%), respectively, with no statistical difference. Post-bevacizumab K ^trans level predicted relapse in 6 months with AUC 0.745 (P < 0.05, 95% CI 0.546-0.943, sensitivity = 0.800, specificity = 0.631).

Conclusions

Bevacizumab improved BBB leakage in RN necrosis. DCE parameters may be useful to predict therapeutic effect and relapse after bevacizumab.