Browsing by Subject "Oncology"

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    Open Access
    A survey of knowledge and attitudes relating to cervical and breast cancer among women in Ethiopia
    (BioMed Central, 2018-08-29) Chaka, Bekele; Sayed, Abdul-Rauf; Goeieman, Bridgette; Rayne, Sarah
    Background Breast cancer and cervical cancer are the two leading cancers among women in Ethiopia. This study investigated knowledge and attitudes related to these two types of cancer among women in 4 zones of Ethiopia. This is the first study employing a validated questionnaire to investigate knowledge and attitudes relating to breast and cervical cancer in Ethiopia. Methods A community based cross-sectional study was conducted from September to November 2015 in the North Shewa zone (Amhara region), Gamo Gofa zone (Southern Nations, Nationalities and Peoples’ region) and zones 1 and 3 (Afar region) of Ethiopia. A total of 799 women aged 18 years and older participated in the survey. Multiple logistic regression analysis was used to investigate the association of possible predictors with breast and cervical cancer knowledge. Results A total of 799 women aged 18 years and older participated in the survey. Of the women interviewed, 63.0% had heard of breast cancer and 42.2% had heard of cervical cancer. Among those who had heard of breast cancer, 21.3% (107/503) had heard of breast cancer screening and 1.4% of women aged 40 years and older had undergone at least one breast screening examination. Fewer than half of the participants provided the correct response to questions related to risk factors for breast and cervical cancer. Among those who had heard of cervical cancer, 41.5% (140/337) had heard of cervical cancer screening and 3.3% had undergone at least one cervical cancer screening examination. Women with primary and higher levels of education were more likely to have heard of breast cancers (OR = 3.0; 95% CI: 2.1–4.2; p < 0.001) and cervical cancer (OR = 1.9; 95% CI: 1.4–2.6; p < 0.001). From the overall attitude score, the majority of the women were found to have negative attitudes towards breast cancer (67.4%) and cervical cancer (70.6%). Conclusions This study found that the overall knowledge of risk factors for breast cancer and cervical cancer among women was low. Lack of cancer awareness, and lack of education in general, are the most potent barriers to access and care, and should be addressed through multi-faceted strategies including peer-education, mass media and other community-based interventions.
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    Open Access
    Exploring topological data analysis in gene expression data topology-driven biomarker discovery and clinical outcome prediction in oncology
    (2025) Nyase, Ndivhuwo; Mashatola, Lebohang; Muller, Julia; Sinkala, Musalula
    This thesis is grounded in the fundamental observation that biological data has shape and this shape matters. Beneath the high-dimensional, often noisy landscape of gene expression profiles lie hidden topological structures (connected components, loops and voids) that capture the complex relationships driving cancer development and progression. By embracing this perspective, we position Topological Data Analysis (TDA) and persistent homology at the core of a novel analytical framework designed to tackle two key challenges in cancer research: clinical outcome prediction and biomarker discovery. In this study, we employ Weighted Gene Topological Data Analysis (WGTDA) to extract topological features from gene expression data, which serve as prognostic biomarkers for cancer classification, staging, and treatment response. Moreover, by integrating these topological features with machine learning models we aim to enhance the predictive accuracy for clinical outcomes. For clinical outcome prediction, we transformed gene expression profiles into topological fingerprints using multiple co-expression measures—namely, Pearson Correlation, Distance Correlation, and Weighted Topological Overlap (wTO) computed with both Pearson and Distance-based adjacencies. These topological features were analyzed using Random Forests. In parallel, we compared the predictive performance of traditional machine learning models (SVM, Gradient Boosting Decision Trees, Random Forest, and Neural Networks) trained on raw gene expression data against models incorporating the topological fingerprints. This comparative analysis was conducted across three classification tasks: cancer type (using TCGA-SARC, TCGA-PCPG, and TCGA-ESCA datasets), cancer staging (using TCGA-HNSC for stages I–IV), and treatment response (responders vs. non-responders). For biomarker identification, the same three tasks were applied using the best performing co-expression measure to generate a global topological representation of the patient population. This provided a disease-level view, highlighting shared homological patterns to facilitate biomarker discovery. Additionally, a dedicated visualization tool has been developed to aid in interpreting these topological signatures and identifying critical biomarkers. The tool is available at https://nnyase.github.io/MSc-Thesis/ WGTDA significantly enhanced phenotype prediction tasks by overcoming common pitfalls of traditional ML models in RNA-Seq data, such as overfitting and poor handling of class imbalance. TDA-derived features improved generalizability of ML models in tasks such as cancer staging and treatment response prediction. Our findings strongly support the integration of TDA into clinical outcome prediction, demonstrating its value in capturing nuanced patterns that allow ML methods to learn more effectively. Moreover, WGTDA remarkably identified key gene signatures for cancer type, staging, and treatment response without relying on pre-existing biological assumptions, yielding biomarkers that are strongly supported by the existing literature. These results underscore the method's reliability and potential clinical utility in precision oncology.