Dataset Information

Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer.

ABSTRACT:

Background

Lymph node metastasis of triple-negative breast cancer (TNBC) is essential in treatment strategy formulation. This study aimed to build a nomogram that predicts lymph node metastasis in patients with TNBC.

Materials and methods

A total of 28,966 TNBC patients diagnosed from 2010 to 2017 in the Surveillance, Epidemiology and End Results (SEER) database were enrolled, and randomized 1:1 into the training and validation sets, respectively. Univariate and multivariate logistic regression analysis were applied to identify the predictive factors, which composed the nomogram. The receiver operating characteristic curves showed the efficacy of the nomogram.

Result

Multivariate logistic regression analyses revealed that age, race, tumor size, tumor primary site, and pathological grade were independent predictive factors of lymph node status. Integrating these independent predictive factors, a nomogram was successfully developed for predicting lymph node status, and further validated in the validation set. The areas under the receiver operating characteristic curves of the nomogram in the training and validation sets were 0.684 and 0.689 respectively, showing a satisfactory performance.

Conclusion

We constructed a nomogram to predict the lymph node status in TNBC patients. After further validation in additional large cohorts, the nomogram developed here would do better in predicting, providing more information for staging and treatment, and enabling tailored treatment in TNBC patients.

SUBMITTER: Cui X

PROVIDER: S-EPMC7747752 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer.

Cui Xiang X Zhu Hao H Huang Jisheng J

Frontiers in oncology 20201204

<h4>Background</h4>Lymph node metastasis of triple-negative breast cancer (TNBC) is essential in treatment strategy formulation. This study aimed to build a nomogram that predicts lymph node metastasis in patients with TNBC.<h4>Materials and methods</h4>A total of 28,966 TNBC patients diagnosed from 2010 to 2017 in the Surveillance, Epidemiology and End Results (SEER) database were enrolled, and randomized 1:1 into the training and validation sets, respectively. Univariate and multivariate logis ...[more]

PMID: 33344259

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Project description:BackgroundPathological complete response (pCR) of axillary lymph nodes (ALNs) is frequently achieved in patients with clinically node-positive breast cancer after neoadjuvant chemotherapy (NAC), and ALN status is an important prognostic factor for breast cancer patients. This study aims to develop a new predictive clinical model to assess the ALN pCR rate after NAC.MethodsThis was a retrospective series of 467 patients who had biopsy-proven positive ALNs at diagnosis and underwent ALN dissection from 2007 to 2014 at the National Cancer Center/Cancer Hospital of the Chinese Academy of Medical Sciences. We analyzed the clinicopathologic features of the patients and developed a nomogram to predict the probability of ALN pCR. A multivariable logistic regression stepwise model was used to construct a nomogram to predict ALN pCR in node-positive patients. The adjusted area under the receiver operating characteristic curve (AUC) was calculated to quantify the ability to rank patients by risk. Internal validation was performed using the 50/50 hold-out validation method. The nomogram was externally validated with prospective cohorts of 167 patients from 2016 to 2018 at the Cancer Hospital of the Chinese Academy of Medical Sciences and 114 patients from 2018 to 2020 at Beijing Tiantan Hospital.ResultsIn this retrospective study, 115 (24.6%) patients achieved ALN pCR after NAC. Multivariate analysis showed that clinical tumor stage (Odds ratio [OR]: 0.321, 95% confidence interval [CI]: 0.121-0.856; P = 0.023); primary tumor response (OR: 0.189; 95% CI: 0.123-0.292; P < 0.001), and estrogen receptor status (OR: 0.530, 95% CI: 0.304-0.925; P = 0.025) were independent predictors of ALN pCR. The nomogram was constructed based on the result of multivariate analysis. In the internal validation of performance of nomogram, the AUCs for the training and test sets were 0.719 and 0.753, respectively. The nomogram was validated in external cohorts with AUCs of 0.720, which demonstrated good discriminatory power in these data sets.ConclusionWe developed a nomogram to predict the likelihood of axillary pCR in node-positive breast cancer patients after NAC. The predictive model performed well in multicenter prospective external validation. This practical tool could provide information to surgeons regarding whether to perform additional ALN dissection after NAC.Trial registrationChiCTR.org.cn, ChiCTR1800014968.

Project description:Purpose:Axillary lymph node (ALN) involvement is an important prognostic factor of early invasive breast cancer. The objective of this study was to establish simple nomograms for predicting ALN involvement based on ultrasound (US) characteristics and evaluate the predictive value of US in the detection of ALN involvement. Patients and Methods:A total of 1328 patients with cT1-2N0 breast cancer by physical exam were retrospectively analyzed. Univariate analysis was used for the comparison of variables, and multivariate analysis was performed by binary logistic regression analysis. The R software was used to establish simple nomograms based on the US characteristics alone. The receiver operating characteristic (ROC) curves of the prediction model and the verification group were drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of the prediction model. A calibration curve was plotted to assess the nomogram predictions vs the actual observations of the ALN metastasis rate and axillary tumor burden rate. Results:The ALN metastasis rates of the training group and the validation group were 35.1% and 34.1%, respectively. Multivariate analysis showed that molecular subtype, lymphovascular invasion, mass descriptors (size, margin, microcalcification and blood flow signal) and LN descriptors (shape, cortical thickness and long-to-short ratio) were independent impact factors in early breast cancer. The AUC of ALN metastasis rate of prediction model based on US features was 0.802, the AUC of high tumor burden rate was 0.873, and the AUC of external validation group was 0.731 and 0.802, respectively. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual metastasis rate and the high tumor burden rate. The results showed that preoperative US had a sensitivity of 59.4% and a specificity of 88.9% for predicting the ALN metastasis rate. Conclusion:The successfully established nomograms based on US characteristics to predict ALN metastasis rate and high axillary tumor burden rate in early breast cancer can achieve individual prediction. Compared with other nomogram predictions, it is more intuitive, and can help clinical decision-making; thus, it should be promoted. However, at this time US features alone are insufficient to replace sentinel lymph node biopsy.

Project description:BackgroundYoung breast cancer (YBC) patients demonstrate a heightened propensity for regional lymph node metastasis (RLNM) in contrast to cohorts across varying age demographics. The aim of our study was to identify clinicopathologic prognostic variables in YBC patients with RLNM and construct a practical and reliable nomogram for the prediction of overall survival (OS) using the Surveillance, Epidemiology, and End Results (SEER) database.MethodsYoung individuals (≤40 years) with a diagnosis of breast cancer with RLNM were recognized from the SEER database between 2010 and 2015, and further randomly split into two cohorts: the training set (n=4,497) and the validation set (n=1,927). We first performed univariate and multivariate Cox regression analyses to confirm independent survival predictors of OS. A novel prognostic nomogram was developed and evaluated using Harrell's concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). To make a clear distinction between high- and low-risk patients in terms of patient survival, Kaplan-Meier survival curves were assessed using the log-rank test.ResultsNine risk factors were found as independent prognostic variables in predicting OS, including race, grade, histology, surgery, radiation, molecular subtype, American Joint Committee on Cancer (AJCC) stage 7th edition, T stage, and N stage. The C-index values of our nomogram were 0.786 [95% confidence interval (CI): 0.767-0.805] and 0.791 (95% CI: 0.760-0.822) in our training and validation groups, respectively. The ROC curves demonstrated sufficient discriminating ability, while the predicted and real survival rates were fairly consistent, as shown by the calibration plots. The prediction model had a higher net benefit and acceptable clinical value, as shown by the DCA curves.ConclusionsIn YBC patients with RLNM, we successfully established a unique nomogram to forecast the 2-, 3-, and 5-year OS. Clinicians may utilize this nomogram to pinpoint patients at higher risk and provide them with appropriate customized therapies.

Dataset Information

Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer.

Background

Materials and methods

Result

Conclusion

Publications

Nomogram for Predicting Lymph Node Involvement in Triple-Negative Breast Cancer.

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