Long-term outcomes among breast cancer patients with extensive regional lymph node involvement: implications for locoregional management

2015 ◽  
Vol 154 (3) ◽  
pp. 633-639 ◽  
Author(s):  
Lior Z. Braunstein ◽  
Sigolene Galland-Girodet ◽  
Saveli Goldberg ◽  
Laura E. G. Warren ◽  
Betro T. Sadek ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Cancer Patients ◽  
Regional Lymph Node ◽  
Lymph Node Involvement ◽  
Breast Cancer Patients ◽  
Long Term Outcomes ◽  
Regional Lymph Node Involvement ◽  
Node Involvement

Molecular genetic prognostic factors for metastatic regional lymph node involvement in breast cancer patients

2020 ◽  
Vol 3_2020 ◽  
pp. 141-147
Author(s):  
Dergunova Yu.A. Dergunova ◽  
Bozhenko V.K. Bozhenko ◽  
Podionov V.V. Podionov ◽  
Kometova V.V. Kometova ◽  
Makarova M.V. Makarova ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Lymph Node ◽  
Cancer Patients ◽  
Regional Lymph Node ◽  
Molecular Genetic ◽  
Lymph Node Involvement ◽  
Breast Cancer Patients ◽  
Regional Lymph Node Involvement ◽  
Node Involvement

scholarly journals Clinical profile of carcinoma breast cases at a tertiary care hospital

2017 ◽  
Vol 4 (3) ◽  
pp. 913 ◽  
Author(s):  
Arifa Almas
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Regional Lymph Node ◽  
Clinical Stage ◽  
Distant Metastases ◽  
Lymph Node Involvement ◽  
Breast Cancer Patients ◽  
Medical College ◽  
Regional Lymph Node Involvement ◽  
Node Involvement

Background: Breast carcinoma is a serious health problem and is among the major health issues in India. Also, it has a relatively early age presentation in this part of the world. Present study describes the features like age at presentation, clinical stage at presentation and incidence of regional lymph node involvement and distant metastases among the breast cancer patients at Government Medical College, Jabalpur in Madhya Pradesh, India.Methods: This hospital-based descriptive study was conducted at the Department of Surgery, Government Medical College, Jabalpur in Madhya Pradesh state of India. Fifty patients with a confirmed diagnosis of breast cancer were included in the study. The details like age at presentation, clinical stage at presentation and incidence of regional lymph node involvement and distant metastases among the breast cancer patients were described and tabulated.Results: Youngest patient of the series was aged 30 years whereas the oldest was aged 65 years. Maximum patients belonged to 41 to 50 years age group. Stage wise distribution of cases reflected that Stage III was the most common stage at presentation. Majority of the cases had lymph node involvement (90%). Liver metastasis was seen in 8 cases (16%), of which, 2 cases had ascites. There were 6 cases of lung metastasis, of which 2 cases had pleural effusion and remaining 4 cases showed solitary metastasis. 6 cases of osseous metastasis were observed, out of which, 2 cases had skull metastasis and 4 cases had spine involvement.Conclusions: It was found that the age group in majority of the patients was younger as compared to Western world and this finding is in line with data from India and other Asian regions. Also, there was a presentation at a later stage of disease in our study population.

scholarly journals Influence of Covariates on Involved Lymph Nodes in Primary Breast Cancer Patients: Mixture Distribution Zero-Inflated Modeling Methodological Framework

2020 ◽  
Author(s):  
Madiha Liaqat ◽  
Shahid Kamal ◽  
Florian Fischer ◽  
Nadeem Zia
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Cancer Patients ◽  
Primary Breast Cancer ◽  
Negative Binomial ◽  
Lymph Node Involvement ◽  
Breast Cancer Patients ◽  
Node Involvement ◽  
Study Population

Abstract Background: Involvement of lymph nodes has been an integral part of breast cancer prognosis and survival. This study aimed to explore factors influencing on the number of auxiliary lymph nodes in women diagnosed with primary breast cancer by choosing an efficient model to assess excess of zeros and over-dispersion presented in the study population. Methods: The study is based on a retrospective analysis of hospital records among 5,196 female breast cancer patients in Pakistan. Zero-inflated Poisson and zero-inflated negative binomial modeling techniques are used to assess the association between under-study factors and the number of involved lymph nodes in breast cancer patients. Results: The most common breast cancer was invasive ductal carcinoma (54.5%). Patients median age was 48 years, from which women aged 46 years and above are the majority of the study population (64.8%). Examination of tumors revealed that over 2,662 (51.2%) women were ER-positive, 2,652 (51.0%) PR-positive, and 2,754 (53.0%) were Her2.neu-positive. The mean tumor size was 3.06 cm and histological grade 1 (n=2021, 38.9%) was most common in this sample. The model performance was best in the zero-inflated negative binomial model. Findings indicate that most factors related to breast cancer have a significant impact on the number of involved lymph nodes. Age is not contributed to lymph node status. Women having a larger tumor size suffered from greater number of involved lymph nodes. Tumor grades 11 and 111 contributed to higher numbers of positive lymph node.Conclusions: Zero-inflated models have successfully demonstrated the advantage of fitting count nodal data when both “at-harm” (lymph node involvement) and “not-at-harm” (no lymph node involvement) groups are important in predicting disease on set and disease progression. Our analysis showed that ZINB is the best model for predicting and describing the number of involved nodes in primary breast cancer, when overdispersion arises due to a large number of patients with no lymph node involvement. This is important for accurate prediction both for therapy and prognosis of breast cancer patients.

Validation of clinical prediction rules for a low probability of nonsentinel and extensive lymph node involvement in breast cancer patients

2007 ◽  
Vol 194 (3) ◽  
pp. 288-293 ◽  
Author(s):  
Gábor Cserni ◽  
Simonetta Bianchi ◽  
Vania Vezzosi ◽  
Riccardo Arisio ◽  
Johannes L. Peterse ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Cancer Patients ◽  
Lymph Node Involvement ◽  
Clinical Prediction ◽  
Breast Cancer Patients ◽  
Prediction Rules ◽  
Node Involvement ◽  
Low Probability ◽  
Extensive Lymph Node
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