A nomogram for predicting survival in patients with de novo metastatic breast cancer: a population-based study

Abstract

Background 5–10% of patients are diagnosed with metastatic breast cancer (MBC) at the initial diagnosis. This study aimed to develop a nomogram to predict the overall survival (OS) of these patients. Methods de novo MBC patients diagnosed in 2010–2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. They were randomly divided into a training and a validation cohort with a ratio of 2:1. The best subsets of covariates were identified to develop a nomogram predicting OS based on the smallest Akaike Information Criterion (AIC) value in the multivariate Cox models. The discrimination and calibration of the nomogram were evaluated using the Concordance index, the area under the time-dependent receiver operating characteristic curve (AUC) and calibration curves. Results In this study, we included 7986 patients with de novo MBC. The median follow-up time was 36 months (range: 0–83 months). Five thousand three-hundred twenty four patients were allocated into the training cohort while 2662 were allocated into the validation cohort. In the training cohort, age at diagnosis, race, marital status, differentiation grade, subtype, T stage, bone metastasis, brain metastasis, liver metastasis, lung metastasis, surgery and chemotherapy were selected to create the nomogram estimating the 1-, 3- and 5- year OS based on the smallest AIC value in the multivariate Cox models. The nomogram achieved a Concordance index of 0.723 (95% CI, 0.713–0.733) in the training cohort and 0.719 (95% CI, 0.705–0.734) in the validation cohort. AUC values of the nomogram indicated good specificity and sensitivity in the training and validation cohort. Calibration curves showed a favorable consistency between the predicted and actual survival probabilities. Conclusion The developed nomogram reliably predicted OS in patients with de novo MBC and presented a favorable discrimination ability. While further validation is needed, this may be a useful tool in clinical practice.

Risk Factors, Prognostic Factors, and Nomogram for Distant Metastasis in Breast Cancer Patients Without Lymph Node Metastasis

“…Consequently, the clinicians could obtain the risk coefficient in 1-, 3-, and 5-year CSS probability. Compared with other recent works on evaluating the 3-and 5-year CSS in breast cancer women with bone metastasis, the C-index of the present nomogram was 0.694, which was higher than Liu's (0.660) (16) and very close to the C-index of nomograms developed by Wang (0.705) (15) and Zhao (0.723) (37), confirming the promising discrimination of our model. To evaluate the accuracy of the nomogram, an independent cohort was subsequently used for validation.…”

Construction and Validation of New Nomograms to Predict Risk and Prognostic Factors of Breast Cancer Bone Metastasis in Asian Females: A Population-Based Retrospective Study

“… 12 , 13 There are many prediction models for the risk and prognosis of breast cancer bone metastasis, but these models were either not based on Asian females or did not exclude multiple organ metastases. 9 , 14 However, to our knowledge, there is currently no specific prediction model or nomogram for the risk of bone-only metastasis nor for the prognostic value of BCBM in Asian females.…”

Incidence and Survival Outcomes in Patients with Synchronous Lung Metastases Upon Initial Metastatic Breast Cancer Diagnosis in China

“…In our data, the extrapulmonary metastases of bone and liver had 1.33 and 2.57 times of mortality risk than lung-only metastases at MBC diagnosis, respectively. Brain metastases also worsen the outcome of BCLM patients but the difference did not reach signi cance, probably due to the late onset of brain metastases during the clinical course, with an incidence of only 6.90-7.56% in newly MBC diagnosis patients [29][30][31] .…”