Exploring Factors Related to Metastasis Free Survival in Breast Cancer Patients Using Bayesian Cure Models

Abstract

Background: Breast cancer is a fatal disease and the most frequently diagnosed cancer in women with an increasing pattern worldwide. The burden is mostly attributed to metastatic cancers that occur in one-third of patients and the treatments are palliative. It is of great interest to determine factors affecting time from cancer diagnosis to secondary metastasis. Materials and Methods: Cure rate models assume a Poisson distribution for the number of unobservable metastatic-component cells that are completely deleted from the non-metastasis patient body but some may remain and result in metastasis. Time to metastasis is defined as a function of the number of these cells and the time for each cell to develop a detectable sign of metastasis. Covariates are introduced to the model via the rate of metastatic-component cells. We used non-mixture cure rate models with Weibull and log-logistic distributions in a Bayesian setting to assess the relationship between metastasis free survival and covariates. Results: The median of metastasis free survival was 76.9 months. Various models showed that from covariates in the study, lymph node involvement ratio and being progesterone receptor positive were significant, with an adverse and a beneficial effect on metastasis free survival, respectively. The estimated fraction of patients cured from metastasis was almost 48%. The Weibull model had a slightly better performance than log-logistic. Conclusions: Cure rate models are popular in survival studies and outperform other models under certain conditions. We explored the prognostic factors of metastatic breast cancer from a different viewpoint. In this study, metastasis sites were analyzed all together. Conducting similar studies in a larger sample of cancer patients as well as evaluating the prognostic value of covariates in metastasis to each site separately are recommended.

A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning

“…Eighty nine subjects were excluded from the analysis because of primary metastasis, a metastasis diagnosed at the time of registry [30]. A number of 579 breast cancer patients with complete information were involved in our analysis.…”

“…In fact, having metastasis was one of the attributes primarily used by their proposed system. This necessary exclusion has been implemented in similar studies [28], [30]. The reason for such an exclusion is that the survival rate of patients with stage IV (metastasis) breast cancer within 5 years is about 10% to 15%.…”

Factors Affecting Long-Survival of Patients with Breast Cancer by Non-Mixture and Mixture Cure Models Using the Weibull, Log-logistic and Dagum Distributions: A Bayesian Approach

“…Cured or long term survivors and uncured or short term survivors. Let p (0 < p < 1) be the probability of being cured and so (1 -p) is the probability of an individual being susceptible (Jafari-Koshki et al, 2014). The corresponding survival function at time t is as follows:…”

Impact of Age, Tumor Size, Lymph Node Metastasis, Stage, Receptor Status and Menopausal Status on Overall Survival of Breast Cancer Patients in Pakistan

“…The 5 year survival decreases by 40% if the lymphnodes get involved (Jafari-Koshki et al, 2014). Moreover the number of nodes involved also predicts survival.…”