Browsing by Subject "Breast Cancer"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Open Access
    Breast cancer patients' experience with receiving pathogenic germline genetic results: single center experience in Oman
    (2025) Balushi, Amira; Wessels, Tina; Van Wyk, Chantel
    Introduction: In Oman, little is known about breast cancer patients' experiences of receiving pathogenic germline genetic results. Receiving a positive germline genetic result can have a wide range of emotional and practical effects on breast cancer patients. This study explored the lived experiences of Omani women navigating a positive pathogenic germline genetic test result for hereditary breast cancer. Driven by the increasing prevalence of genetic testing and limited research on its psychosocial impact within this specific cultural context, the study aimed to understand how Omani women experience and make sense of a positive genetic test result. Methods: This is a qualitative study based on an interpretive phenomenological approach. Semi structured interviews were conducted with nine Omani women who had received a positive germline genetic result for breast cancer predisposition gene. Thematic analysis was employed to identify key themes emerging from these women's experiences. Result and Discussion: Four themes emerged from the thematic analysis in this study. These included “Cancer journey”, “Genetic testing motivation and expectations”, “Receiving the positive result”, and “Adapting to hereditary breast cancer diagnosis”. The findings revealed the complex and multifaceted experience of breast cancer women with receiving positive germline genetic result. The perceived causation of their breast cancer, such as stress and pre-existing cultural beliefs, as well as their lived experiences during the cancer diagnosis and treatment, all influenced these women's response and understanding of their genetic test result. Emotional responses varied, ranging from anxiety and fear to relief and empowerment, highlighting the individual nature of this experience. Coping strategies were active coping such as leaving the matter to God's hand, increased surveillance and risk-reducing surgeries, engagement coping with family and friends for support, and meaning-focused coping, often grounded in religious and spiritual beliefs. Family dynamics and cultural norms played a crucial role in disclosure practices, with concerns about protecting family members from psychological stress having influenced their decisions about information sharing. x Conclusion: This research contributes valuable insights into the lived experiences of Omani women with hereditary breast cancer, highlighting the need for culturally sensitive and individualised support throughout the testing and decision-making process. The findings have implications for healthcare professionals, genetic counsellors, and policymakers, emphasizing the importance of providing comprehensive support that addresses the emotional, social, and cultural dimensions of hereditary cancer risk.
  • No Thumbnail Available
    Item
    Open Access
    Evaluating deep learning for enhanced breast cancer diagnosis: a comparative analysis of CNN architectures
    (2025) Frankle, Solyle; Sinkala, Musalula
    Artificial Intelligence (AI), particularly its machine learning (ML) subfield, has revolutionised various sectors, including healthcare. In breast cancer care, AI's ability to analyse vast datasets and extract complex patterns from medical images has the potential to transform diagnostics and treatment strategies. Breast cancer remains one of the most prevalent cancers affecting women globally, with early and accurate diagnosis being crucial for effective treatment. AI, through its advanced image analysis capabilities, significantly improves the accuracy and efficiency of breast cancer diagnosis, specifically in distinguishing between cancer subtypes. Here, we aim to explore the application of deep learning, particularly convolutional neural networks (CNNs), in breast cancer subtype classification using histology images. A custom CNN model, alongside well-established models like ResNet50 and EfficientNetB0, was developed and evaluated for its accuracy in predicting benign and malignant breast cancer subtypes. The results demonstrated that while the custom CNN achieved an accuracy of 65% for malignant and 67% for benign subtypes with ROC-AUC scores of 0.86 and 0.90, respectively, ResNet50 significantly outperformed both the custom model and EfficientNetB0. ResNet50 attained an accuracy of 77% in classifying malignant subtypes and 77% for benign subtypes, accompanied by ROC-AUC scores of 0.92 and 0.96, respectively. Additionally, ResNet50 exhibited higher precision (0.68 for malignant, 0.67 for benign), recall (0.65 for malignant, 0.67 for benign), and F1 scores (0.65 for malignant, 0.67 for benign) across most subtypes, underscoring its robust performance and reliability in clinical settings. In conclusion, AI, specifically through advanced CNN architectures, can greatly enhance breast cancer diagnosis by providing more accurate subtype classifications. Future work should focus on integrating these models into clinical workflows, enabling faster and more personalised treatment planning. Moreover, continued refinement of these models, including addressing the complexities of tumour heterogeneity and incorporating multimodal data, will be crucial for their widespread adoption in oncology.