Development of prognostic nomograms using institutional data for patients with triple-negative breast cancer.

Background: Triple-negative breast cancer (TNBC) is an aggressive disease. Nomograms can predict prognosis of patients with TNBC. Methods: A total of 745 eligible TNBC patients were recruited and randomly divided into training and validation groups. Endpoints were disease-free survival and overall survival. Concordance index, area under the curve and calibration curves were used to analyze the predictive accuracy and discriminative ability of nomograms. Results: Based on the training cohort, neutrophil-to-lymphocyte ratio, positive lymph nodes, tumor size and tumor-infiltrating lymphocytes were used to construct a nomogram for disease-free survival. In addition, age was added to the overall survival nomogram. Conclusion: The current study developed and validated well-calibrated nomograms for predicting disease-free survival and overall survival in patients with TNBC.

Development and Validation of Nomograms for Predicting the Prognosis of Triple-Negative Breast Cancer Patients Based on 379 Chinese Patients.

PURPOSE: We aimed to construct universally applicable nomograms incorporating prognostic factors to predict the prognosis of patients with triple-negative breast cancer (TNBC). PATIENTS AND METHODS: Clinicopathological data of 379 patients with TNBC from March 2008 to June 2014 were retrospectively collected and analyzed. The endpoints were disease-free survival (DFS) and overall survival (OS). Patients were randomly divided into a training group and an independent validation group. In the training group, the prognostic factors were screened to develop nomograms. C-index and calibration curves were used to evaluate the predictive accuracy and discriminative ability of nomograms in both groups. The accuracy of the nomograms was also compared with the traditional American Joint Committee on Cancer Tumor-Node-Metastasis anatomical stage (8th edition). RESULTS: Four prognostic factors (albumin-to-globulin ratio, neutrophil-to-lymphocyte ratio, positive lymph nodes, and tumor size) were used to construct the nomogram of DFS. In addition to the aforementioned factors, age was taken into account in the construction of the OS nomogram. The C-index of the DFS nomogram in the training and validation groups was 0.71 (95% confidence interval [CI]: 0.64-0.77) and 0.69 (95% CI: 0.58-0.79), respectively; the C-index of the OS nomogram was 0.77 (95% CI: 0.70-0.84) and 0.74 (95% CI: 0.62-0.86), respectively. This suggests that the nomograms had high accuracy. Moreover, calibration curves showed good consistencies in both groups. Our models showed superiority in predicting accuracy compared with the AJCC TNM staging system. Furthermore, two web pages of the nomograms were produced: DFS: https://sh-skipper.shinyapps.io/TNBC1/; OS: https://sh-skipper.shinyapps.io/TNBC2/. CONCLUSION: These predictive models are simple and easy to use, particularly the web versions. They have certain clinical value in predicting the prognosis of patients with TNBC. They can assist doctors in identifying patients at different prognostic risks and strengthen the treatment or follow-up accordingly.