Treatment outcomes and its associated factors among breast cancer patients at Kitui Referral Hospital

Survival Patterns Among Patients With Breast Cancer in Sub-Saharan Africa: A Systematic Review and Meta-Analysis

Importance

Breast cancer is the most prevalent cancer globally with tremendous disparities both within specific regions and across different contexts. The survival pattern of patients with breast cancer remains poorly understood in sub-Saharan African (SSA) countries.

Objective

To investigate the survival patterns of patients with breast cancer in SSA countries and compare the variation across countries and over time.

Data Sources

Embase, PubMed, Web of Science, Scopus, and ProQuest were searched from inception to December 31, 2022, with a manual search of the references.

Study Selection

Cohort studies of human participants that reported 1-, 2-, 3-, 4-, 5-, and 10-year survival from diagnosis among men, women, or both with breast cancer in SSA were included.

Data Extraction and Synthesis

Independent extraction of study characteristics by multiple observers was performed using open-source software, then exported to a standard spreadsheet. A random-effects model using the generalized linear mixed-effects model was used to pool data. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guideline for reporting was followed.

Main Outcome and Measures

Survival time from diagnosis.

Results

Forty-nine studies were included in the review with a sample size ranging from 21 to 2311 (total, 14 459; 196 [1.35%] men, 13 556 [93.75%] women, and 707 [4.90%] unspecified; mean age range, 38 to 71 years), of which 40 were summarized using meta-analysis. The pooled 1-year survival rate of patients with breast cancer in SSA was 0.79 (95% CI, 0.67-0.88); 2-year survival rate, 0.70 (95% CI, 0.57-0.80); 3-year survival rate, 0.56 (95% CI, 0.45-0.67); 4-year survival rate, 0.54 (95% CI, 0.43-0.65); and 5-year survival rate, 0.40 (95% CI, 0.32-0.49). The subgroup analysis showed that the 5-year survival rate ranged from 0.26 (95% CI, 0.06-0.65) for studies conducted earlier than 2010 to 0.47 (95% CI, 0.32-0.64) for studies conducted later than 2020. Additionally, the 5-year survival rate was lower in countries with a low human development index (HDI) (0.36 [95% CI, 0.25-0.49) compared with a middle HDI (0.46 [95% CI, 0.33-0.60]) and a high HDI (0.54 [95% CI, 0.04-0.97]).

Conclusions and Relevance

In this systematic review and meta-analysis, the survival rates for patients with breast cancer in SSA were higher in countries with a high HDI compared with a low HDI. Enhancing patient survival necessitates a comprehensive approach that involves collaboration from all relevant stakeholders.

Revolutionizing Breast Healthcare: Harnessing the Role of Artificial Intelligence

Breast cancer has the highest incidence and second-highest mortality rate among all cancers. The management of breast cancer is being revolutionized by artificial intelligence (AI), which is improving early detection, pathological diagnosis, risk assessment, individualized treatment recommendations, and treatment response prediction. Nuclear medicine has used artificial intelligence (AI) for over 50 years, but more recent advances in machine learning (ML) and deep learning (DL) have given AI in nuclear medicine additional capabilities. AI accurately analyzes breast imaging scans for early detection, minimizing false negatives while offering radiologists reliable, swift image processing assistance. It smoothly fits into radiology workflows, which may result in early treatments and reduced expenditures. In pathological diagnosis, artificial intelligence improves the quality of diagnostic data by ensuring accurate diagnoses, lowering inter-observer variability, speeding up the review process, and identifying errors or poor slides. By taking into consideration nutritional, genetic, and environmental factors, providing individualized risk assessments, and recommending more regular tests for higher-risk patients, AI aids with the risk assessment of breast cancer. The integration of clinical and genetic data into individualized treatment recommendations by AI facilitates collaborative decision-making and resource allocation optimization while also enabling patient progress monitoring, drug interaction consideration, and alignment with clinical guidelines. AI is used to analyze patient data, imaging, genomic data, and pathology reports in order to forecast how a treatment would respond. These models anticipate treatment outcomes, make sure that clinical recommendations are followed, and learn from historical data. The implementation of AI in medicine is hampered by issues with data quality, integration with healthcare IT systems, data protection, bias reduction, and ethical considerations, necessitating transparency and constant surveillance. Protecting patient privacy, resolving biases, maintaining transparency, identifying fault for mistakes, and ensuring fair access are just a few examples of ethical considerations. To preserve patient trust and address the effect on the healthcare workforce, ethical frameworks must be developed. The amazing potential of AI in the treatment of breast cancer calls for careful examination of its ethical and practical implications. We aim to review the comprehensive role of artificial intelligence in breast cancer management.

Clinicopathologic Profile and Psychosocial Experiences of Nigerian Breast Cancer Survivors

PURPOSE

Breast cancer survivors are a distinct category of patients with unique characteristics and needs. The population of survivors is expected to increase, given the rising incidence of breast cancer in Nigeria, and the improvements in breast cancer outcomes. This study evaluated the clinicopathologic characteristics and the psychosocial experiences of a cohort of Nigerian breast cancer survivors.

METHODS

From an institutional breast cancer database, patients managed between January 2010 and December 2016 were evaluated. Clinicopathologic characteristics, treatment details, and survival estimates were assessed. These were compared with nonsurvivors managed during the same period. Survivors were defined as those who have been alive for at least 5 years from the date of presentation. Qualitatively, a purposive sample of 20 survivors was evaluated using one-on-one in-depth interviews to assess their experiences and coping mechanisms after treatment.

RESULTS

Of the 355 patients in the database during the study period, there were 163 survivors (45.9%), while 192 (54.1%) died. Age, stage at presentation, tumor size, and receipt of multiple treatment modalities were significantly associated with survival. Five themes were identified in qualitative analysis: initial reaction to the diagnosis, experiences during treatment, social support, coping strategies, and advocacy. Strong family support and spirituality were prominent coping strategies identified in this cohort.

CONCLUSION

Despite obvious infrastructural and manpower limitations, Nigerian patients who present early and receive multimodal therapy and different breast cancer treatments have better odds of survival. Survivors have some unmet psychosocial and physical needs requiring intervention.

Metastatic breast cancer in Kenya: survival, prognosis and management at a tertiary referral centre

There has been an increase in breast cancer in Africa with up to 77% of patients diagnosed with advanced disease. However, there is little data on survival outcomes and prognostic factors affecting survival in patients with metastatic breast cancer (MBC) in Africa. The study objective was to establish the survival of patients with MBC at a single tertiary health facility, the clinical and pathological characteristics affecting survival and describe the treatment modalities used. This was a retrospective descriptive study conducted at Aga Khan University Hospital, Nairobi of patients diagnosed with MBC between 2009 and 2017. Survival data was collected on metastatic free survival, survival time between diagnosis of first metastasis and death and overall survival. Data on patient's age, menopausal status and stage at diagnosis, tumour grade, receptor status, site of metastasis and treatment given was also collected. The Kaplan-Meier Estimator was used to estimate survival. Prognostic factors for survival outcomes were analysed using univariate analysis. Standard descriptive statistics were used to describe patient characteristics. A total of 131 patients were included in the study. The median survival was 22 months. The 3 and 5-year survivals were 31.3% and 10.7%, respectively. On univariate analysis, the Luminal A molecular subtype was a significant positive prognostic factor hazard ratios (HR 0.652 95% confidence interval (CI) 0.473-0.899) while metastasis to the liver or brain were significant negative prognostic factors (HR 0.615 95% CI 0.413-0.915 and HR 0.566 95% CI 0.330-0.973, respectively). A large proportion (87.0%) received some treatment for metastatic disease. Our study concluded that survival rates for patients diagnosed with MBC were lower compared to studies from Western countries but higher than in studies from Sub-Saharan Africa. Luminal A molecular subtype was found to be a positive prognostic factor and metastasis to the liver or brain were found to be negative prognostic factors. Improved access to adequate treatment for MBC is required in the region.