Establishment and verification of a nomogram to predict tumor-specific mortality risk in triple-negative breast cancer: a competing risk model based on the SEER cohort study

Abstract

Background: Triple-negative breast cancer (TNBC) is the subtype of breast cancer with the worst prognosis, and traditional survival analysis methods are biased when predicting mortality. To predict the risk of death in patients with triple-negative breast cancer more precisely, a competing risk model was developed. Methods:The clinicopathological data of the TNBC patients from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The data were assigned into a training set and testing set at a ratio of 7:3 in a randomized pattern. Univariate and multivariate competing risk models were applied to find the independent prognostic factors. A prediction nomogram for cancer-specific mortality (CSM) risk was constructed. The accuracy and discrimination of the nomogram were assessed using receiver operating characteristic (ROC) area under the curve (AUC), concordance index (C-index), and a calibration curve using the training and testing sets, respectively.Results: A total of 28,430 TNBC patients were randomly grouped into the training set (n=19,900) and the testing set (n=8,530). The median time for follow-up was 59 [1-107] months. A total of 7,014 (24.67%) patients died, among whom 4,801 (68.45%) died from breast cancer and 2,213 (31.55%) due to non-breast cancer events. The independent risk factors were primary site of tumor, grade, tumor-node-metastasis (TNM) stage, T stage, approach of surgery, chemotherapy, axillary lymph node metastases, brain metastases, and liver metastases. The prediction nomogram was constructed by using the aforementioned variables. The 1-, 3-, and 5-year AUC of CSM were predicted to be 0.856, 0.81, and 0.782, respectively, in the training set, and 0.856, 0.81, and 0.782 in the testing set, respectively. The C-index of the nomogram was 0.801 and 0.799 in the training and testing sets, respectively. As confirmed by the validation and training calibration curves, the nomogram was consistent with the results. Conclusions:We used competing risk models to identify risk factors for CSM and constructed a CSM risk prediction nomogram for TNBC patients, that may be utilized to predict CSM risk in TNBC patients clinically and assist in the creation of individualised clinical treatment options.

Development and validation of a predictive nomogram for the specific mortality risk of luminal B breast cancer patients: a competing risk model based on real populations

“…Radiotherapy, one of the classic adjuvant therapies, provides patients with surgery opportunities or tumor cleaning after surgery. Consistent with Li et al ’ s. study, we found that patients with adjuvant radiotherapy had a better prognosis than those who did not undergo radiotherapy ( 37 ). However, the results in 2 randomized trials conflicts with our study ( 38 , 39 ).…”

Construction and validation of nomogram for the cancer-specific mortality for HER2-positive breast cancer patients

“…CSM calculated by the traditional survival analysis method is higher than that calculated by the competitive risk model. [ 12 ] In this study, we first identified the factors that may affect the prognosis of HER2 + breast cancer currently available in the SEER database using a competitive risk model. The results showed that age, race, tumor grade, clinical stage, T stage, surgery status, radiotherapy, chemotherapy, and regional nodes examined were independent risk factors for the clinical outcome of HER2 + breast cancer.…”

Possible therapeutic targets for NLRP3 inflammasome-induced breast cancer

“…Breast cancer, especially triple-negative breast cancers (TNBC), has a high cell proliferation rate and is prone to recurrence [ 46 ]. Martinon et al discovered the inflammasome in 2002 to link inflammation, tumorigenesis, angiogenesis, and metastasis [ 47 ]. There are three components to the inflammasome complex: the sensor molecule, the adaptor protein, and the receptor (Fig.…”

“…1 ). PLRs, ALRs, RLRs, CLRs, and proteins with tripartite motifs, such as pyrin (also known as marenostrin or TRIM20) are among the molecules involved in the inflammasome [ 47 ].…”