scholarly journals PB.1. Axillary lymph node ultrasound and needle sampling in preoperative staging of breast cancer: re-audit

2014 ◽  
Vol 16 (S1) ◽  
Author(s):  
P Hamilton ◽  
H Kazi ◽  
L Clarke ◽  
A Robinson ◽  
A Leaver
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Axillary Lymph Node ◽  
Preoperative Staging ◽  
Axillary Lymph ◽  
Needle Sampling ◽  
Lymph Node Ultrasound

Axillary Ultrasound as Preoperative Staging in Clinical Negative Axillary Lymph Node Breast Cancer Patient

2021 ◽  
Vol 15 (5) ◽  
pp. 1222-1224
Author(s):  
M. S. Javid ◽  
M. Barry
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Axillary Lymph Node ◽  
Preoperative Staging ◽  
Nodal Status ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Nodal Disease ◽  
Axillary Ultrasound

Objective: To determine the diagnostic accuracy of axillary US as a preoperative investigation by comparing it with the histology outcome of nodal status. Methods: This validation analysis was conducted in Mater Misericordia university hospital, Dublin Ireland form Feb 2007 to Feb 2015. All female patients with impalpable axillary lymph node and histology proven unifocal breast cancer between ages 18 to 75 years were included. Patients with the diagnosis of breast cancer were followed with Ultrasound imaging and results in Picture Archiving and communication system (PACS) and histology was confirmed using the patient center data base in both preoperative and postoperative course of breast cancer, including both sentinel lymph nodes and axillary lymph nodes. Results: A total of 625 patients had axillary ultrasound (US) to assess the preoperative axillary nodal status with mean age of 56±12 years. cN0 was diagnosed in 469 (75%) cases, cN1 in 136 (21.8%) cases and cN2 in 20 (3.2%) cases. After negative axillary ultrasound cN0 pathology shows positive pN2 and pN3 disease in 14 (2.9%) cases with the NPV of 97.01%. Axillary ultrasound had shown cN1 disease in 136 cases with the pathology outcome of pN2 and pN3 in 41 (30.14%) cases with the negative predictive value (NPV) of 69.85%. The overall sensitivity and specificity of the axillary US in detection of the positive node was 51.6% and 92.8% with PPV of 82.69% and NPV of 74.2%. Conclusion: Axillary US is a useful modality for screening of breast cancer patients. The negative US findings exclude the presence of advanced nodal disease. However, it cannot accurate distinguish between pN1 and pN2 or pN3 nodal disease. Keywords: Axillary ultrasound, Axillary lymph nodes, Breast cancer.

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.

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