Factors Correlating With Lymph Node Metastases in Patients With T1 Breast Cancer

Brenin, David R.; Manasseh, Donna‐Marie; El‐Tamer, Mahmoud; Troxel, Andrea B.; Schnabel, Freya; Ditkoff, Beth Ann; Kinne, David W.

doi:10.1007/s10434-001-0432-7

Cited by 30 publications

(17 citation statements)

References 18 publications

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“…However, the predictive power of available standard parameters was found not high enough to limit the number of axillary lymph node dissections. Interestingly, among the factors identified to correlate with nodal involvement by Brenin et al (36) was ER status, which we also found influential in the multivariate analysis, but not in a previous study (37). In this study, core biopsies were taken from invasive tumors of different sizes, 40% of which were classified to be T 1 ( Table 1).…”

Section: Discussionmentioning

confidence: 52%

Prediction of Nodal Involvement in Breast Cancer Based on Multiparametric Protein Analyses from Preoperative Core Needle Biopsies of the Primary Lesion

Sauer

Schneiderhan‐Marra

Kazmaier

et al. 2008

Clinical Cancer Research

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Purpose: Identification of molecular characteristics that are useful to define subgroups of patients fitting into differential treatment schemes is considered a most promising approach in cancer research. In this first study of such type, we therefore investigated the potential of multiplexed sandwich immunoassays to define protein expression profiles indicative of clinically relevant properties of malignant tumors. Experimental Design: Lysates prepared from large core needle biopsies of 113 invasive breast carcinomas were analyzed with bead-based miniaturized sandwich immunoassays specific for 54 preselected proteins. Results: Five protein concentrations [fibroblast growth factor-2 (FGF-2), Fas, Fas ligand, tissue inhibitor of metalloproteinase-1, and RANTES] were significantly different in the groups of patients with or without axillary lymph node metastasis. All 15 protein parameters that resulted in P values <0.2 and other diagnostic information [estrogen receptor (ER) status, tumor size, and histologic grading] were analyzed together by multivariate logistic regression.This yielded sets of five (FGF-2, Fas, Fas ligand, IP10, and PDGF-AB/BB) or six (ER staining intensity, FGF-2, Fas ligand, matrix metalloproteinase-13, PDGF-AB/BB, and IP10) parameters for which receiveroperator characteristic analyses revealed high sensitivities and specificities [area under curve (AUC) = 0.75 and AUC = 0.83] to predict the nodal status. A similar analysis including all identified parameters of potential value (15 proteins, ER staining intensity, T) without selection resulted in a receiver-operator characteristic curve with an AUC of 0.87. Conclusion: We clearly showed that this approach can be used to quantify numerous proteins from breast biopsies accurately in parallel and define sets of proteins whose combined analyses allow the prediction of nodal involvement with high specificity and sensitivity.

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Section: Discussionmentioning

confidence: 52%

Prediction of Nodal Involvement in Breast Cancer Based on Multiparametric Protein Analyses from Preoperative Core Needle Biopsies of the Primary Lesion

Sauer

Schneiderhan‐Marra

Kazmaier

et al. 2008

Clinical Cancer Research

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“…We considered that molecular markers for nodal metastasis might be more valuable than the conventional pathological risk factors (Brenin et al, 2001), because the genetic changes can (Fisher et al, 1984;Fitzgibbons et al, 2000), and background aberrant hypermethylation increases with age (Toyota and Issa, 1999). We defined nodal metastasis based on staining with H&E as the gold standard in this study.…”

Section: Discussionmentioning

confidence: 99%

“…There are several known risk factors of lymph node metastasis for invasive breast cancer to date. For nodal metastasis of T1 breast cancer, lymphovascular invasion, tumor size, histologic grade, histologic type, and estrogen receptor (ER) status are the major risk factors of pathological findings (Brenin et al, 2001). We hypothesized that additional information about genetic alteration of cancer cells could be helpful for a prediction of nodal metastasis, and aimed to find new genetic markers of nodal metastasis.…”

Section: Introductionmentioning

confidence: 99%

Aberrant hypermethylation of ID4 gene promoter region increases risk of lymph node metastasis in T1 breast cancer

et al. 2005

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ID4 gene is a member of the inhibitor of DNA-binding (ID) family, which inhibits DNA binding of basic helixloop-helix transcription factors. Certain human primary breast cancers reportedly have low or no expression of ID4 protein, but its role in carcinogenesis and cancer progression is unknown. To determine its possible role, we examined epigenetic inactivation of ID4 gene by promoter hypermethylation in human breast cell lines and T1 breast cancer tissues. Methylation status of ID4 promoter CpG island was assessed by methylation-specific PCR (MSP); ID4 mRNA level was assessed by quantitative real-time RT-PCR. Of eight cell lines, two were fully methylated, four were partially methylated, and two were not methylated. ID4 mRNA level was suppressed in fully methylated cell lines. ID4 hypermethylation was observed in 16 of 24 (67%) node-positive and seven of 36 (19%) node-negative T1 primary breast cancers matched by patient age and tumor diameter. It was a significant risk factor for nodal metastasis (OR 13.1, P ¼ 0.0004). ID4 mRNA level was suppressed in hypermethylated cancer specimens (P ¼ 0.014). ID4 may play an important suppressive role in tumor progression, and its silencing by hypermethylation may increase the risk of regional lymph node metastasis.

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“…The known risk factors are age at diagnosis, age of menarche, genetic risk, and family history [3][4][5][6] . The most widely accepted prognostic factor for breast cancer is the American Joint Commission on Cancer (AJCC) staging system based on the TNM system (T, tumor; N, node; M, metastasis) [7][8][9][10][11] . The Nottingham prognosis index is a grading system that incorporates the evaluation of tumor size, stage of disease, and tumor grade 12)13) .…”

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confidence: 99%

A Hybrid Bayesian Network Model for Predicting Breast Cancer Prognosis

Choi

Han

Park

2009

J Korean Soc Med Inform

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Objective: Breast cancer is one of the most common cancers affecting women. Both physicians and patients have concerned about breast cancer survivability. Many researchers have studied the breast cancer survivability applying artificial nerural network model (ANN). Usually ANN model outperformed in classification of breast cancer survivability than other models such as logistic regression, Bayesian network (BN), or decision tree models. However, physicians in the fields hesitate to use ANN model, because ANN is a black-box model, and hard to explain the classification result to patients. In this study, we proposed a hybrid model with a degree of the accuracy and interpretation by combining the ANN for accuracy and BN for interpretation. Methods: We developed an artificial neural network, a Bayesian network, and a hybrid Bayesian network model to predict breast cancer prognosis. The hybrid model combined the artificial neural network and the Bayesian network to obtain a good estimation of prognosis as well as a good explanation of the results. The National Cancer Institute's SEER program public-use data were used to construct and evaluate the proposed models. Nine variables, which are clinically acceptable, were selected for input to the proposed models' nodes. A confidence value of the neural network served as an additional input node to the hybrid Bayesian network model. Ten iterations of random subsampling were performed to evaluate performance of the models. . The known risk factors are age at diagnosis, age of menarche, genetic risk, and family history [3][4][5][6] . The most widely accepted prognostic factor for breast cancer is the American Joint Commission on Cancer (AJCC) staging system based on the TNM system (T, tumor; N, node; M, metastasis) [7][8][9][10][11] . The Nottingham prognosis index is a grading system that incorporates the evaluation of tumor size, stage of disease, and tumor grade 12)13). Recently, molecular biologic markers, such as the estrogen receptor, progesterone receptor, and HER-2/neu, have become important prognostic factors [14][15][16] .Several studies have reported on breast cancer prognosis prediction using data mining techniques. Burke et al. . They used 6,787 cases of the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) breast carcinoma data set (1977)(1978)(1979)(1980)(1981)(1982) . Lee compared various predictive modeling techniques to predict the breast cancer occurance (not prognosis) by using Korean data obtained from 209 subject (109 patients and 100 controls).The AUC of the proposed Naïve Bayes model was 0.90, and it outperformed than other various Bayesian network models or regression models 19) .Lundin et al. compared logistic regression and ANN models for survival estimation in 951 breast cancer cases 20) . Eight variables were entered as input to the network, including tumor size, axillary node, histologic type, mitotic count, nuclear pleomorphism, tubule formation, tumor necrosis, and age. The area under the curve (AUC) of th...

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Factors Correlating With Lymph Node Metastases in Patients With T1 Breast Cancer

Abstract: Axillary staging by either sentinel lymph node biopsy or level I/II axillary dissection is indicated for most T1 breast cancer patients. Omission of axillary staging can be considered for highly selected patients with T1a cancers.

Cited by 30 publications

References 18 publications

Prediction of Nodal Involvement in Breast Cancer Based on Multiparametric Protein Analyses from Preoperative Core Needle Biopsies of the Primary Lesion

Prediction of Nodal Involvement in Breast Cancer Based on Multiparametric Protein Analyses from Preoperative Core Needle Biopsies of the Primary Lesion

Aberrant hypermethylation of ID4 gene promoter region increases risk of lymph node metastasis in T1 breast cancer

A Hybrid Bayesian Network Model for Predicting Breast Cancer Prognosis

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