Examination of Axillary Lymph Node Metastasis in Patients with Sentinel Lymph Node Metastasis-Positive in Breast Cancer

2012 ◽  
Vol 172 (2) ◽  
pp. 343
Author(s):  
S. Akiyoshi ◽  
E. Tokunaga ◽  
N. Yamashita ◽  
K. Ando ◽  
H. Saeki ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Lymph Node Metastasis ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Sentinel Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Node Metastasis

Prediction of the non-sentinel node metastasis in patients without clinically axillary lymph node metastasis, with implication of breast cancer subtypes.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e13060-e13060
Author(s):  
Shusei Tominaga
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Lymph Node Metastasis ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Breast Cancer Patients ◽  
Luminal A ◽  
Node Metastasis

e13060 Background: The accuracy of the nomogram about non-sentinel lymph node metastasis (NSLNM ) in breast cancer patients is still controversial to avoid axillary dissection particularly sentinel lymph node biopsy was positive. The aim of this study was to evaluate the necessity of adding breast cancer subtypes to the NSLNM nomgram variables. Methods: Between 2009 and 2011, consecutive breast cancer patients without clinically axillary lymph node metastasis (n=140) who received sentinel lymph node biopsy at Higashiosaka General Hospital were studied retrospectively. Twenty-two patients were turned out that breast cancer already spread to the sentinel nodes and all of 22 patients received complete axillary lymph node dissection. Results: Twelve patients had only sentinel lymph node metastases(Group S), 10 patients had non-SLN metastases (Group A). Patient characteristics and average probability of spread to additional lymph node developed by Memorial Sloan-Kettering Cancer Center (MSKCC) Nomogram were almost the same results in both groups. However, subtypes of Group S consisted of 8 HER2 positive , 2 triple negative, and 2 luminal A cases, subtypes of Group A consisted of 4 luminal A and 6 luminal B cases. Conclusions: Our data suggested that luminal type breast cancer tends to spread to non-sentinel lymph node metastasis and adding HER2, Ki-67, and intrinsic biological subtypes may improve predictivity of MSKCC nomogram.

scholarly journals Correlation between Grading Histopathology and Sentinel Lymph Node Metastasis in Early Breast Cancer in University of Sumatera Utara Hospital

2021 ◽  
Vol 9 (B) ◽  
pp. 679-682
Author(s):  
Dedy Hermansyah ◽  
Gracia Pricilia ◽  
Arjumardi Azrah ◽  
Yolanda Rahayu ◽  
Desiree A. Paramita ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Lymph Node ◽  
Sentinel Lymph Node ◽  
Lymph Node Metastasis ◽  
Axillary Lymph Node ◽  
Sentinel Lymph Node Metastasis ◽  
Lymph Node Status ◽  
Axillary Lymph ◽  
Node Metastasis

BACKGROUND: Breast cancer is a malignancy in breast tissue from the duct or lobar epithelium. American Joint Committee on Cancer has specified important prognostic factors such as primary tumor size, regional lymph node status, and distant metastasis. Axillary lymph node status has been one of the most reliable prognostic factors in early breast cancer in women. Axillary lymph node dissection is an old method to identify metastasis in axillary lymph nodes and started being replaced by sentinel lymph node biopsy (SLNB). SLNB has been introduced as a minimal invasive procedure, but in Indonesia, this procedure cannot be done due to technology limitation. Grading tumor is one of the predictor factors that can predict lymph node metastasis. This predictor factor has been associated with sentinel lymph node metastasis significantly. AIM: According to this, we conduct this study to analyze the correlation between grading histopathology in breast cancer with sentinel lymph node metastasis to lower false-negative rate in SLNB using methylene blue dye. MATERIALS AND METHODS: In this study, we included 51 patients that qualified using inclusion and exclusion criteria. Then, sentinel lymph node metastasis and grading histopathology data were retrieved from the patient’s medical record. This data are analyzed using SPSS with Chi-square test. RESULTS: The most type of breast cancer in this study is invasive ductal carcinoma was found in 40 patients (78.4%). There are 22 of 51 patients (51.6%) with metastasis to sentinel lymph node, have Grade 3 in histopathologic findings. CONCLUSIONS: The statistical evaluation showed that there is significant correlation between grading histopathology and SLNB with p = 0.001.

scholarly journals Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 757
Author(s):  
Sanaz Samiei ◽  
Renée W. Y. Granzier ◽  
Abdalla Ibrahim ◽  
Sergey Primakov ◽  
Marc B. I. Lobbes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Mr Images ◽  
Node Metastasis

Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51–68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41–0.74 and 0.48–0.89 in the training cohorts, respectively, and between 0.30–0.98 and 0.37–0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.

IV-DSA is a new diagnostic tool for axillary lymph node metastasis in breast cancer patients

2004 ◽  
Vol 87 (2) ◽  
pp. 75-79 ◽  
Author(s):  
Osamu Watanabe ◽  
Tadao Shimizu ◽  
Hiroshi Imamura ◽  
Jun Kinoshita ◽  
Yoshihito Utada ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cancer Patients ◽  
Diagnostic Tool ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Breast Cancer Patients ◽  
Node Metastasis

scholarly journals Use of CEA and CA15-3 to Predict Axillary Lymph Node Metastasis in Patients with Breast Cancer

2016 ◽  
Vol 7 (1) ◽  
pp. 37-41 ◽  
Author(s):  
San-Gang Wu ◽  
Zhen-Yu He ◽  
Hong-Yue Ren ◽  
Li-Chao Yang ◽  
Jia-Yuan Sun ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Node Metastasis

scholarly journals Artificial Intelligence Algorithm-Based Ultrasound Image Segmentation Technology in the Diagnosis of Breast Cancer Axillary Lymph Node Metastasis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lianhua Zhang ◽  
Zhiying Jia ◽  
Xiaoling Leng ◽  
Fucheng Ma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Node Metastasis ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

This paper aimed to investigate the application of ultrasound image segmentation technology based on the back propagation neural network (BPNN) artificial intelligence algorithm in the diagnosis of breast cancer axillary lymph node metastasis, thereby providing a theoretical basis for clinical diagnosis. In this study, 90 breast cancer patients with axillary lymph node metastasis were selected as the research objects and rolled randomly into an experimental group and a control group. Besides, all of them were examined by ultrasound. The BPNN algorithm for the ultrasound image segmentation diagnosis method was applied to the patiens from the experimental group, while the control group was given routine ultrasound diagnosis. Thus, the value of this algorithm in ultrasonic diagnosis was compared and explored. The results showed that when the number of hidden layer nodes based on the BPNN artificial intelligence algorithm was 2, 3, 4, 5, 6, 7, and 8, the corresponding segmentation accuracy was 97.3%, 96.5%, 94.8%, 94.8%, and 94.1% in turn. Among them, the segmentation accuracy was the highest when the number of hidden layer nodes was 2. The correlation of independent variable bubble plot analysis showed that the presence or absence of capsules, the presence of crab feet or burrs in breast cancer lesions was critical influencing factors for the occurrence of axillary lymph node metastasis, and the standardized importance was 99.7% and 70.8%, respectively. Besides, the area under the two-dimensional receiver operating characteristic (ROC) curve of the BPNN artificial intelligence algorithm model classification was always greater than the area under the curve of manual segmentation, and the segmentation accuracy was 90.31%, 94.88%, 95.48%, 95.44%, and 97.65% in sequence. In addition, the segmentation specificity of different running times was higher than that of manual segmentation. In conclusion, the BPNN artificial intelligence algorithm had high accuracy, sensitivity, and specificity for ultrasound image segmentation, with a better segmentation effect. Therefore, it had a better diagnostic effect for breast cancer axillary lymph node metastasis.

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