A Clinical Nomogram Predicting Pathologic Lymph Node Involvement in Esophageal Cancer Patients

2010 ◽  
Vol 128 ◽  
pp. 215-222 ◽  
Author(s):  
Puja Gaur ◽  
Boris Sepesi ◽  
Wayne L. Hofstetter ◽  
Arlene M. Correa ◽  
Manoop S. Bhutani ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Lymph Node ◽  
Cancer Patients ◽  
Lymph Node Involvement ◽  
Node Involvement ◽  
Pathologic Lymph Node

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

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.

Prognostic Significance of Tumor Stage and Lymph Node Involvement in Thoracic Esophageal Cancer

1988 ◽  
pp. 709-713 ◽  
Author(s):  
P. Sorrentino ◽  
A. Ruol ◽  
C. Castoro ◽  
M. Ferrini ◽  
A. Piccoli ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Lymph Node ◽  
Prognostic Significance ◽  
Tumor Stage ◽  
Lymph Node Involvement ◽  
Thoracic Esophageal Cancer ◽  
Node Involvement

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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