scholarly journals Age-dependent heterogeneity of lymph node metastases and survival in breast cancer: A population-based study

2021 ◽  
Author(s):  
Michael Behring ◽  
Sumit Agarwal ◽  
Prachi Bajpai ◽  
Amr Elkholy ◽  
Hyung-Gyoon Kim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Age Of Onset ◽  
Young Age ◽  
Receptor Subtype ◽  
Node Metastasis ◽  
Pathologic Features ◽  
Age Dependent ◽  
Age Of Diagnosis

AbstractBackgroundFor several cancers, including those of the breast, young age at diagnosis is associated with an adverse prognosis. Although this effect is often attributed to heritable mutations such as BRCA1/2, the relationship between pathologic features, young age of onset, and prognosis for breast cancer remains unclear. In the present study, we highlight links between age of onset and lymph node metastasis (NM) in US women with breast cancer.MethodsCase listings from Surveillance, Epidemiology, and End Result (SEER) 18 registry data for women with breast cancer, which include information on race, were used. NM and its associated outcomes were evaluated for a subset of women with receptor subtype information and then compared against a larger, pre-subtype validation set of data from the same registry. Age of diagnosis was a 5-category variable; under 40 years, 40-49 years, 50-59 years, 60-69 years and 70+ years. Univariate and adjusted multivariate survival models were applied to both sets of data.ResultsAs determined with adjusted logistic regression models, women under 40 years old at diagnosis had 1.55 times the odds of NM as women 60-69 years of age. The odds of NM for (HR = hormone receptor) HR+/HER2+, HR-/HER2+, and triple-negative breast cancer subtypes were significantly lower than those for HR+/HER2-. In subtype-stratified adjusted models, age of diagnosis had a consistent trend of decreasing odds of NM by age category, most noticeable for HR+ subtypes of luminal A and B. Univariate 5-year survival by age was worst for women under 40 years, with NM attributable for 49% of the hazard of death from cancer in adjusted multivariate models.ConclusionsLymph node metastasis is age-dependent, yet not all molecular subtypes are clearly affected by this relationship. For <40-yr-old women, NM is a major cause for shorter survival. When stratified by subtype, the strongest associations were in HR+ groups, suggesting a possible hormonal connection between young age of breast cancer onset and NM.

Doctor, What Are My Chances of Having a Positive Sentinel Node? A Validated Nomogram for Risk Estimation

2007 ◽  
Vol 25 (24) ◽  
pp. 3670-3679 ◽  
Author(s):  
José Luiz B. Bevilacqua ◽  
Michael W. Kattan ◽  
Jane V. Fey ◽  
Hiram S. Cody ◽  
Patrick I. Borgen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cancer Center ◽  
Tumor Type ◽  
Data Set ◽  
Specific Patient ◽  
Node Metastasis ◽  
Pathologic Features

Purpose Lymph node metastasis is a multifactorial event. Several variables have been described as predictors of lymph node metastasis in breast cancer. However, it is difficult to apply these data—usually expressed as odds ratios—to calculate the probability of sentinel lymph node (SLN) metastasis for a specific patient. We developed a user-friendly prediction model (nomogram) based on a large data set to assist in predicting the presence of SLN metastasis. Patients and Methods Clinical and pathologic features of 3,786 sequential SLN biopsy procedures were assessed with multivariable logistic regression to predict the presence of SLN metastasis in breast cancer. The model was subsequently applied to 1,545 sequential SLN biopsies. A nomogram was created from the logistic regression model. A computerized version of the nomogram was developed and is available on the Memorial Sloan-Kettering Cancer Center (New York, NY) Web site. Results Age, tumor size, tumor type, lymphovascular invasion, tumor location, multifocality, and estrogen and progesterone receptors were associated with SLN metastasis in multivariate analysis. The nomogram was accurate and discriminating, with an area under the receiver operating characteristic curve of 0.754 when applied to the validation group. Conclusion Newly diagnosed breast cancer patients are increasingly interested in information about their disease. This nomogram is a useful tool that helps physicians and patients to accurately predict the likelihood of SLN 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.

Axillary schwannoma mimicking a breast cancer lymph node metastasis

2021 ◽  
Author(s):  
Hanae Ramdani ◽  
Siham El Haddad ◽  
Latifa Chat ◽  
Abdelilah Souadka ◽  
Nazik Allali
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Node Metastasis
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