Ovarian carcinoma presenting with axillary lymph node metastasis: A case diagnosed by fine-needle aspiration and brief review of the literature

2008 ◽  
Vol 36 (12) ◽  
pp. 891-893 ◽  
Author(s):  
Lazaros Skagias ◽  
Apostolos Ntinis ◽  
Olympia Vasou ◽  
Agathi Kondi-Pafiti ◽  
Ekaterini Politi
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Fine Needle Aspiration ◽  
Axillary Lymph Node ◽  
Needle Aspiration ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Review Of The Literature ◽  
Fine Needle ◽  
Node Metastasis

scholarly journals Axillary Lymph Node Metastasis: CA-15-3 and Carcinoembryonic Antigen Concentrations in Fine-Needle Aspirates for Preoperative Diagnosis in Patients with Breast Cancer

Radiology ◽  
2010 ◽  
Vol 254 (3) ◽  
pp. 691-697 ◽  
Author(s):  
Min Jung Kim ◽  
Byeong-Woo Park ◽  
Jong-Baeck Lim ◽  
Hyon-Suk Kim ◽  
Jin Young Kwak ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Axillary Lymph Node ◽  
Preoperative Diagnosis ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Fine Needle ◽  
Fine Needle Aspirates ◽  
Node Metastasis

Axillary lymph node metastasis from serous ovarian cancer: a case report and review of the literature

2008 ◽  
Vol 279 (2) ◽  
pp. 203-207 ◽  
Author(s):  
Çetin Aydin ◽  
Haluk R. Ünalp ◽  
Ali Baloğlu ◽  
Ayşe Gül İnci ◽  
Seyran Yiğit ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Case Report ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Serous Ovarian Cancer ◽  
Review Of The Literature ◽  
Node Metastasis

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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