Analysis of estrogen receptor polymorphism in codon 325 by PCR-SSCP in breast cancer: Association with lymph node metastasis

2002 ◽  
Vol 38 (11) ◽  
pp. S55-S56
Author(s):  
A Vasconcelos
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Node Metastasis ◽  
Receptor Polymorphism

Analysis of Estrogen Receptor Polymorphism in Codon 325 by PCR-SSCP in Breast Cancer: Association With Lymph Node Metastasis

2002 ◽  
Vol 8 (4) ◽  
pp. 226-229 ◽  
Author(s):  
Andre Vasconcelos ◽  
Rui Medeiros ◽  
Isabel Veiga ◽  
Deolinda Pereira ◽  
Susana Carrilho ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Node Metastasis ◽  
Receptor Polymorphism

scholarly journals Impact of Molecular Subtypes of Breast Cancer on Axillary Lymph Node Metastasis: A Tertiary Center Experience

2021 ◽  
pp. 305-312
Author(s):  
Dharmendra Singh ◽  
Soumen Mukherjee
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Axillary Lymph Node ◽  
Molecular Subtypes ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph ◽  
Node Metastasis ◽  
Tertiary Center

Background: Axillary lymph node metastasis (ALNM) is one of the important prognostic factors of breast cancer. The objective of this study was to assess the risk of ALNM in different molecular subtypes determined by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (her2neu) of breast cancer. Methods: This retrospective study was conducted on patients who had undergone upfront breast conserving surgery (BCS) or modified radical mastectomy (MRM). Patients were classified as HR (hormone receptor) +/ her2neu- (ER or PR positive and her2neu negative), HR+/her2neu+ (ER or PR positive and her2neu positive), HR-/her2neu- (ER, PR and her2neu negative or triple negative or basal type), and HR-/her2neu+ (ER or PR negative and her2neu positive). The association between clinicopathological variables and ALNM was evaluated in logistic regression analyses. Results: In this study, 476 patients met the inclusion criteria, and had 67.2% ALNM at diagnosis. ALNM was statistically significantly correlated with age ≤ 40 years (p=0.026), tumor grade (p=0.007), pathological tumor size (P<0.001), estrogen receptor (P=0.045), molecular subtypes (P=0.021), LVI (P<0.001), and PNI (P<0.001). Post Hoc test revealed that HR-/her2neu+ subtypes of breast cancer had the highest and HR+/her2neu- had the lowest risk of ALNM.   Conclusion: ALNM may be predicted by molecular subtypes of breast cancer. The risk of ALNM is less in TNBC although it is clinically more aggressive. These findings may play an important role in gauging the individualized axillary management in breast cancer.

Estrogen Insensitivity in a Model of Estrogen Receptor–Positive Breast Cancer Lymph Node Metastasis

2007 ◽  
Vol 67 (21) ◽  
pp. 10582-10591 ◽  
Author(s):  
Joshua Chuck Harrell ◽  
Wendy W. Dye ◽  
Djuana M.E. Harvell ◽  
Mauricio Pinto ◽  
Paul Jedlicka ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Node Metastasis ◽  
Estrogen Receptor Positive ◽  
Positive 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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