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A negative binomial regression model for risk estimation of 0-2 axillary lymph node metastases in breast cancer patients.

ABSTRACT: Extensive clinical trials indicate that patients with negative sentinel lymph node biopsy do not need axillary lymph node dissection (ALND). However, the ACOSOG Z0011 trial indicates that patients with clinically negative axillary lymph nodes (ALNs) and 1-2 positive sentinel lymph nodes having breast conserving surgery with whole breast radiotherapy do not benefit from ALND. The aim of this study is therefore to identify those patients with 0-2 positive nodes who might avoid ALND. A total of 486 patients were eligible for the study with 212 patients in the modeling group and 274 patients in the validation group, respectively. Clinical lymph node status, histologic grade, estrogen receptor status, and human epidermal growth factor receptor 2 status were found to be significantly associated with ALN metastasis. A negative binomial regression (NBR) model was developed to predict the probability of having 0-2 ALN metastases with the area under the curve of 0.881 (95% confidence interval 0.829-0.921, P?

SUBMITTER: Zeng

PROVIDER: S-EPMC7736885 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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A negative binomial regression model for risk estimation of 0-2 axillary lymph node metastases in breast cancer patients.

Zeng De Lin Hao-Yu HY Zhang Yu-Ling YL Wu Jun-Dong JD Lin Kun K Xu Ya Y Chen Chun-Fa CF

Scientific reports 20201214 1

Extensive clinical trials indicate that patients with negative sentinel lymph node biopsy do not need axillary lymph node dissection (ALND). However, the ACOSOG Z0011 trial indicates that patients with clinically negative axillary lymph nodes (ALNs) and 1-2 positive sentinel lymph nodes having breast conserving surgery with whole breast radiotherapy do not benefit from ALND. The aim of this study is therefore to identify those patients with 0-2 positive nodes who might avoid ALND. A total of 486 ...[more]

PMID: 33318591

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Project description:Whether the expression status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2) receptor and Ki-67 show concordance between the primary tumors and the synchronous axillary lymph node (ALN) metastases has been discussed in numerous studies. However, to date, the results of these studies remain controversial. Therefore, the present study aimed to investigate whether the expression of ER, PR, HER-2 and Ki-67 was in concordance between the primary tumors and synchronous ALN metastases in patients with operable breast cancer (BC). A total of 60 tissue samples were collected from patients with primary operable BC diagnosed with primary tumors and synchronous ALN metastases. The expression levels of the four biomarkers, ER, PR, HER-2 and Ki-67, were assessed in primary lesions and synchronous ALN metastases samples using immunohistochemistry. The cut-off values were set to 10% for ER and PR, while the labeling index of Ki-67 was set to 14%. The immunostaining intensity of ER and PR was scored as negative (?), 1+, 2+ and 3+. The criteria for HER-2 testing in BC were implemented according to the American Society of Clinical Oncology (ASCO) and the College of American Pathologists (CAP) guidelines. The concordance rates for ER, PR and HER-2 were 96.7 (58/60), 96.7 (58/60) and 90% (54/60), respectively. In addition, the kappa values of consistency in the primary lesions and the synchronous ALN metastases were 0.773 for ER, 0.654 for PR and 0.785 for HER-2. Furthermore, the P-values of ER, PR and Ki-67 numerical variables between the two groups were 0.393, 0.400 and 0.331, respectively, as demonstrated using a non-parametric Wilcoxon signed rank test. The findings of the present study demonstrated a high degree of concordance between the expression of ER, PR, HER-2 and Ki-67 in the primary tumors and that in the synchronous ALN metastases, suggesting that the BC primary tumor biomarkers may be used for the prognosis of synchronous ALN metastases in patients with operable BC.

Project description:PurposeTo develop a clinical-radiomics model based on radiomics features extracted from MRI and clinicopathologic factors for predicting the axillary pathologic complete response (apCR) in breast cancer (BC) patients with axillary lymph node (ALN) metastases.Materials and methodsThe MR images and clinicopathologic data of 248 eligible invasive BC patients at the Peking University First Hospital from January 2013 to December 2020 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and the presence of ALN metastases was confirmed through cytology pre-NAC. The data from January 2013 to December 2018 were randomly divided into the training and validation sets in a ratio of 7:3, and the data from January 2019 to December 2020 served as the independent testing set. The following three types of prediction models were investigated in this study. 1) A clinical model: the model was built by independently predicting clinicopathologic factors through logistic regression. 2) Radiomics models: we used an automatic segmentation model based on deep learning to segment the axillary areas, visible ALNs, and breast tumors on post-NAC dynamic contrast-enhanced MRI. Radiomics features were then extracted from the region of interest (ROI). Radiomics models were built based on different ROIs or their combination. 3) A clinical-radiomics model: it was built by integrating radiomics signature and independent predictive clinical factors by logistic regression. All models were assessed using a receiver operating characteristic curve analysis and by calculating the area under the curve (AUC).ResultsThe clinical model yielded AUC values of 0.759, 0.787, and 0.771 in the training, validation, and testing sets, respectively. The radiomics model based on the combination of MRI features of breast tumors and visible ALNs yielded the best AUC values of 0.894, 0.811, and 0.806 in the training, validation, and testing sets, respectively. The clinical-radiomics model yielded AUC values of 0.924, 0.851, and 0.878 in the training, validation, and testing sets, respectively, for predicting apCR.ConclusionWe developed a clinical-radiomics model by integrating radiomics signature and clinical factors to predict apCR in BC patients with ALN metastases post-NAC. It may help the clinicians to screen out apCR patients to avoid lymph node dissection.

Project description:BackgroundThere are increasing concerns about that sentinel lymph node biopsy (SLNB) could be omitted in patients with clinically T1-2 N0 breast cancers who has negative axillary ultrasound (AUS). This study aims to assess the false negative result (FNR) of AUS, the rate of high nodal burden (HNB) in clinically T1-2 N0 breast cancer patients, and the diagnostic performance of breast magnetic resonance imaging (MRI) and nomogram.MethodsWe identified 948 consecutive patients with clinically T1-2 N0 cancers who had negative AUS, subsequent MRI, and breast conserving therapy between 2013 and 2020 from two tertiary medical centers. Patients from two centers were assigned to development and validation sets, respectively. Among 948 patients, 402 (mean age ± standard deviation, 57.61 ± 11.58) were within development cohort and 546 (54.43 ± 10.02) within validation cohort. Using logistic regression analyses, clinical-imaging factors associated with lymph node (LN) metastasis were analyzed in the development set from which nomogram was created. The performance of MRI and nomogram was assessed. HNB was defined as ≥ 3 positive LNs.ResultsThe FNR of AUS was 20.1% (81 of 402) and 19.2% (105 of 546) and the rates of HNB were 1.2% (5/402) and 2.2% (12/546), respectively. Clinical and imaging features associated with LN metastasis were progesterone receptor positivity, outer tumor location on mammography, breast imaging reporting and data system category 5 assessment of cancer on ultrasound, and positive axilla on MRI. In validation cohorts, the positive predictive value (PPV) and negative predictive value (NPV) of MRI and clinical-imaging nomogram was 58.5% and 86.5%, and 56.0% and 82.0%, respectively.ConclusionThe FNR of AUS was approximately 20% but the rate of HNB was low. The diagnostic performance of MRI was not satisfactory with low PPV but MRI had merit in reaffirming negative AUS with high NPV. Patients who had low probability scores from our clinical-imaging nomogram might be possible candidates for the omission of SLNB.

Dataset Information

A negative binomial regression model for risk estimation of 0-2 axillary lymph node metastases in breast cancer patients.

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A negative binomial regression model for risk estimation of 0-2 axillary lymph node metastases in breast cancer patients.

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