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Development and validation of nomograms for predicting overall and breast cancer-specific survival among patients with triple-negative breast cancer.

ABSTRACT: TNBC is generally more aggressive than other BC subtypes and has limited therapeutic options. We aimed to construct comprehensive and reliable nomograms to predict the OS and BCSS of TNBC patients to offer clinicians therapeutic guidance for improving the prognosis of TNBC patients. We used the SEER 19 Cancer Registry to identify 21,419 eligible TNBC patients diagnosed from January 1, 2010 to December 31, 2015, and divided the database randomly into a training cohort (n=10,709) and a validation cohort (n=10,710). The log-rank test and Cox analysis together with a competing risk model were utilized to identify independent prognostic factors for OS and BCSS, which were then integrated to construct nomograms. According to the training cohort, except for laterality, the following factors were all predictive of OS and BCSS: age at diagnosis, race, tumor size, number of positive lymph nodes, grade, and histological subtype. The 1-, 3-, and 5-year probabilities of BC-specific mortality were 2.7%, 12.5%, and 17.1%, respectively. The precision of the nomograms was assessed by the C-index value and calibration plot diagrams. The C-index value were 0.779 for OS and 0.793 for BCSS in the internal validation and 0.774 for OS and 0.792 for BCSS in the external validation. Both internal and external calibration plot diagrams showed good consistency between the actual and predicted outcomes, especially for 3- and 5-year OS and BCSS. These nomograms hold promise as a novel and accurate tool in predicting OS and BCSS of TNBC patients and could be used in clinical practice to assist clinicians in developing more effective therapeutic strategies and to evaluate prognostic personally.

SUBMITTER: Guo LW

PROVIDER: S-EPMC6250111 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Development and validation of nomograms for predicting overall and breast cancer-specific survival among patients with triple-negative breast cancer.

Guo Lin-Wei LW Jiang Lin-Miao LM Gong Yue Y Zhang Hong-Hua HH Li Xiao-Guang XG He Min M Sun Wei-Li WL Ling Hong H Hu Xin X

Cancer management and research 20181119

<h4>Purpose</h4>TNBC is generally more aggressive than other BC subtypes and has limited therapeutic options. We aimed to construct comprehensive and reliable nomograms to predict the OS and BCSS of TNBC patients to offer clinicians therapeutic guidance for improving the prognosis of TNBC patients.<h4>Patients and methods</h4>We used the SEER 19 Cancer Registry to identify 21,419 eligible TNBC patients diagnosed from January 1, 2010 to December 31, 2015, and divided the database randomly into a ...[more]

PMID: 30510456

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Project description:BackgroundTriple negative breast cancer (TNBC) is an aggressive and heterogeneous disease. Nomograms predicting outcomes of TNBC are needed for risk management.MethodsNomograms were based on an analysis of 296 non-metastatic TNBC patients treated at Sun Yat-sen Memorial Hospital from 2002 to 2014. The end points were disease-free survival (DFS) and overall survival (OS). Predictive accuracy and discriminative ability were evaluated by concordance index (C-index), area under the curve (AUC) and calibration curve, and compared with the American Joint Committee on Cancer (AJCC) staging system, PREDICT and CancerMath. Models were subjected to bootstrap internal validation and external validation using independent cohorts of 191 patients from the second Xiangya Hospital and Peking University Shenzhen Hospital between 2007 and 2012.ResultsOn multivariable analysis of training cohort, independent prognostic factors were stromal tumor-infiltrating lymphocytes (TILs), tumor size, node status, and Ki67 index, which were then selected into the nomograms. The calibration curves for probability of DFS and OS showed optimal agreement between nomogram prediction and actual observation. The C-index of nomograms was significantly higher than that of the seventh and eighth AJCC staging system for predicting DFS (training: 0.743 vs 0.666 (P = 0.003) and 0.664 (P = 0.024); validation: 0.784 vs 0.632 (P = 0.02) and 0.607 (P = 0.002)) and OS (training: 0.791 vs 0.683 (P = 0.004) and 0.677 (P < 0.001); validation: 0.783 vs 0.656 (P = 0.006) and 0.606 (P = 0.001)). Our nomograms had larger AUCs compared with PREDICT and CancerMath. In addition, the nomograms showed good performance in stratifying different risk groups of patients both in the training and validation cohorts.ConclusionWe have developed novel and practical nomograms that can provide individual prediction of DFS and OS for TNBC based on stromal TILs, tumor size, node status, and Ki67 index. Our nomograms may help clinicians in risk consulting and selection of long term survivors.

Project description:Background For patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making. Methods A retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Results The LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751–0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756–0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812–0.904), the CSS was 0.866 (95% CI: 0.817–0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821–0.851), 0.769 (95% CI: 0.759–0.780), and 0.750 (95% CI: 0.738–0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811–0.847), 0.769 (95% CI: 0.757–0.780), and 0.745 (95% CI: 0.732–0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging. Conclusion Two prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.

Project description:Background: The objective of this study was to evaluate the prognostic value of clinical characteristics in elderly patients with triple-negative breast cancer (TNBC). Methods: The cohort was selected from the Surveillance, Epidemiology, and End Results (SEER) program dating from 2010 to 2015. Univariate and multivariate analyses were performed using a Cox proportional risk regression model, and a nomogram was constructed to predict the 1-, 3-, and 5-year prognoses of elderly patients with TNBC. A concordance index (C-index), calibration curve, and decision curve analysis (DCA) were used to verify the nomogram. Results: The results of the study identified a total of 5,677 patients who were randomly divided 6:4 into a training set (n = 3,422) and a validation set (n = 2,255). The multivariate analysis showed that age, race, grade, TN stage, chemotherapy status, radiotherapy status, and tumor size at diagnosis were independent factors affecting the prognosis of elderly patients with TNBC. Together, the 1 -, 3 -, and 5-year nomograms were made up of 8 variables. For the verification of these results, the C-index of the training set and validation set were 0.757 (95% CI 0.743-0.772) and 0.750 (95% CI 0.742-0.768), respectively. The calibration curve also showed that the actual observation of overall survival (OS) was in good agreement with the prediction of the nomograms. Additionally, the DCA showed that the nomogram had good clinical application value. According to the score of each patient, the risk stratification system of elderly patients with TNBC was further established by perfectly dividing these patients into three groups, namely, low risk, medium risk, and high risk, in all queues. In addition, the results showed that radiotherapy could improve prognosis in the low-risk group (P = 0.00056), but had no significant effect in the medium-risk (P < 0.4) and high-risk groups (P < 0.71). An online web app was built based on the proposed nomogram for convenient clinical use. Conclusion: This study was the first to construct a nomogram and risk stratification system for elderly patients with TNBC. The well-established nomogram and the important findings from our study could guide follow-up management strategies for elderly patients with TNBC and help clinicians improve individual treatment.

Dataset Information

Development and validation of nomograms for predicting overall and breast cancer-specific survival among patients with triple-negative breast cancer.

Publications

Development and validation of nomograms for predicting overall and breast cancer-specific survival among patients with triple-negative breast cancer.

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