Prognostic Factors Affecting Outcomes in Triple Negative Breast Cancer

Arora, Divya; Hasan, Siti Norhidayu; Male, Eneida; Pruszynski, Jessica; Ord, Celine Bicquart; Rao, A.R.

doi:10.1016/j.ijrobp.2015.07.626

Cited by 2 publications

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“…Previous research showed that tumor size, metastasis, late stage, lymph vascular space invasion, metastases number, endocrine therapy are predictors of survival time of breast cancer outpatients [ 6 , 7 ]. Young age, advanced stage at diagnosis, positive lymph node status, tumor sizes 3 and 4, positive lymph node status, and the presence of hormone receptor-negative status are other predictors of survival [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach

Gashu,

Aguade

2024

BMC Women's Health

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Background Despite the significant weight of difficulty, Ethiopia's survival rate and mortality predictors have not yet been identified. Finding out what influences outpatient breast cancer patients' survival time was the major goal of this study. Methods A retrospective study was conducted on outpatients with breast cancer. In order to accomplish the goal, 382 outpatients with breast cancer were included in the study using information obtained from the medical records of patients registered at the University of Gondar referral hospital in Gondar, Ethiopia, between May 15, 2016, and May 15, 2020. In order to compare survival functions, Kaplan-Meier plots and the log-rank test were used. The Cox-PH model and Bayesian parametric survival models were then used to examine the survival time of breast cancer outpatients. The use of integrated layered Laplace approximation techniques has been made. Results The study included 382 outpatients with breast cancer in total, and 148 (38.7%) patients died. 42 months was the estimated median patient survival time. The Bayesian Weibull accelerated failure time model was determined to be suitable using model selection criteria. Stage, grade 2, 3, and 4, co-morbid, histological type, FIGO stage, chemotherapy, metastatic number 1, 2, and >=3, and tumour size all have a sizable impact on the survival time of outpatients with breast cancer, according to the results of this model. The breast cancer outpatient survival time was correctly predicted by the Bayesian Weibull accelerated failure time model. Conclusions Compared to high- and middle-income countries, the overall survival rate was lower. Notable variables influencing the length of survival following a breast cancer diagnosis were weight loss, invasive medullar histology, comorbid disease, a large tumour size, an increase in metastases, an increase in the International Federation of Gynaecologists and Obstetricians stage, an increase in grade, lymphatic vascular space invasion, positive regional nodes, and late stages of cancer. The authors advise that it is preferable to increase the number of early screening programmes and treatment centres for breast cancer and to work with the public media to raise knowledge of the disease's prevention, screening, and treatment choices.

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Section: Introductionmentioning

confidence: 99%

Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach

Gashu,

Aguade

2024

BMC Women's Health

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“…Previous studies revealed that tumor characteristics, metastasis, advanced disease stage, lymph vascular space invasion, multiple metastases, sites, maintenance, endocrine therapy are predictors of survival of breast cancer patients. 6 , 7 Similarly, young age, late-stage at diagnosis, positive lymph node status, tumor size 3 and 4, and hormone receptor-negative status are predictors of survival. 8 …”

Section: Introductionmentioning

confidence: 99%

Survival and Predictors of Mortality among Breast Cancer Patients in Northwest Ethiopia: A Retrospective Cohort Study

Tiruneh¹,

Tesfaw²,

Tesfa³

2021

CMAR

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Background Breast cancer is the most common malignant tumor and the leading cause of cancer death in over 100 countries. Despite the high burden of difficulty, the survival status and the predictors for mortality are not yet determined in Ethiopia. Studies related to this area are scarce. Therefore, we aimed to estimate the survival status and predictors of mortality among breast cancer patients in Northwest Ethiopia. Methods A retrospective cohort study design was carried out from September 2015 to August 2020 among 482 women who had breast cancer in Northwest Ethiopia. A systematic sampling technique was employed to select the required representative sample. The Cox regression model was used to identify the predictors of mortality among breast cancer patients. Results For this study, 482 participants had followed for 8824 person-months total analysis time or at-risk time. In our findings, the overall survival of breast cancer patients at the end of two and five years was 54.24% and 25.8%, respectively. In the multivariable Cox regression model, age, stage of BC, menopausal status, and surgical therapy were significant predictors of death. Conclusion The overall survival after two years was 54.24%, and after five years was 25.8%. This result is lower than the recently published report and indicates that in LMIC, especially in rural cancer centers, the infrastructure and resources for routine screening mammography are often unavailable. Therefore, there is a need to promote early diagnosis of BC at each level of health-care delivery point.

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Prognostic Factors Affecting Outcomes in Triple Negative Breast Cancer

Cited by 2 publications

References 0 publications

Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach

Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach

Survival and Predictors of Mortality among Breast Cancer Patients in Northwest Ethiopia: A Retrospective Cohort Study

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