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Review Article
The Impact of Radiation on Breast Cancer Treatment (Case Study: Breast Cancer)
Issue:
Volume 13, Issue 4, December 2025
Pages:
91-95
Received:
18 May 2025
Accepted:
12 September 2025
Published:
17 October 2025
DOI:
10.11648/j.jctr.20251304.11
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Abstract: Radiation therapy has long been established as a cornerstone in oncological practice and remains one of the most effective modalities in the management of malignant diseases. It is a specialized field within oncology that utilizes high-energy ionizing radiation, such as X-rays or gamma rays, with the objective of eradicating cancer cells while preserving adjacent healthy tissues. This dual aim of maximizing tumor control and minimizing normal tissue toxicity underscores the critical importance of precision in treatment planning and delivery. The present study specifically examines the role of radiation therapy in the management of breast cancer, which represents one of the most prevalent malignancies among women globally. Given the availability of multiple therapeutic strategies—including surgical resection, systemic chemotherapy, hormonal interventions, immunotherapy, and targeted therapies—radiation therapy occupies a distinct position in ensuring local tumor control and reducing recurrence. This study employs a descriptive-analytical approach, drawing upon a broad review and synthesis of experimental and clinical investigations to evaluate the therapeutic efficacy and limitations of radiation therapy in breast cancer treatment. The findings consistently demonstrate that radiation therapy exerts a markedly positive effect on patient outcomes, particularly in reducing local recurrence rates, improving disease-free survival, and contributing to overall survival. However, the therapeutic impact is contingent upon several decisive factors. These include the accuracy of tumor delineation, the radiation oncologist’s ability to identify and mitigate potential errors during planning and delivery, the reduction of scattered radiation beyond the treatment field, and the maintenance of a homogeneous dose distribution across the target volume. Optimization of these parameters is essential to achieving favorable clinical results while limiting acute and long-term side effects. In conclusion, radiation therapy continues to serve as an indispensable modality in the multidisciplinary management of breast cancer. Ongoing advances in imaging technologies, treatment planning algorithms, and delivery systems, such as intensity-modulated radiotherapy and image-guided radiotherapy, are expected to further enhance the precision, safety, and clinical effectiveness of this therapeutic approach. Collectively, these developments underscore the enduring and evolving role of radiation therapy in improving survival outcomes and quality of life for breast cancer patients.
Abstract: Radiation therapy has long been established as a cornerstone in oncological practice and remains one of the most effective modalities in the management of malignant diseases. It is a specialized field within oncology that utilizes high-energy ionizing radiation, such as X-rays or gamma rays, with the objective of eradicating cancer cells while pres...
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Research Article
Determinants of Health-Related Quality of Life of Patients with Prostate Cancer Attending Cancer Centers in Eastern Kenya
Issue:
Volume 13, Issue 4, December 2025
Pages:
96-106
Received:
4 September 2025
Accepted:
16 September 2025
Published:
18 October 2025
DOI:
10.11648/j.jctr.20251304.12
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Abstract: Introduction: Patients with PCa experience alterations in sexual, genitourinary, and bowel functions, as well as psychological and financial difficulties, which in turn affect their HRQoL. However, there is paucity of data on the HRQoL among of patients with PCa being treated within the rural areas in Kenya. Aim: The aim of this study was to determine determinants of HRQoL among patients with PCa. Methodology: A descriptive cross-sectional design was used. Simple random sampling method was used to recruit 58 participants from two public cancer centers in Eastern Kenya. Data was collected using a researcher developed and administered semi-structured questionnaire. European Organization of Cancer Research and Treatment (EORTC) QLQ - C30 and QLQ – PR25 tools were used to obtain the HRQoL data and data was analyzed with SPSS version 29. Logistic regression tool was used to determine the predictors of HRQoL. A p-value of < 0.05 was considered significant. Ethical clearance and research permit were obtained from relevant authorities. Results: The mean age of the participants was about 73 years (±7.62) with a range of 60 to 90 years. Most participants were diagnosed in the 3rd stage of disease (39.7%, n=23) and had poor HRQoL (58.6%, n = 34). On the EORTC QLQ - C30, social functioning had the lowest average score of 49.43, while role functioning had the highest average score of 67.529%. On symptom scales/items, financial difficulties, fatigue, pain and insomnia were the most frequently reported. On EORTC QLQ – PR 25, urinary symptoms were the most prevalent (34.84%) while sexual activity domain scored relatively high, with a mean of 71.84%. Poor HRQoL was significantly associated with older age and low income while longer illness duration was associated with better HRQoL (p = < 0.05). The findings highlight age, income, and illness duration as key determinants of HRQoL among the participants, with financial strain and advanced age emerging as critical vulnerabilities. Strengthening financial protection mechanisms, geriatric-focused care, and sustained psychosocial support may help optimize long-term outcomes for patients with PCa in Kenya and similar settings.
Abstract: Introduction: Patients with PCa experience alterations in sexual, genitourinary, and bowel functions, as well as psychological and financial difficulties, which in turn affect their HRQoL. However, there is paucity of data on the HRQoL among of patients with PCa being treated within the rural areas in Kenya. Aim: The aim of this study was to determ...
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Research Article
Advancing Automated Brain Tumor Detection: A YOLOv11-Based Deep Learning Approach for Real-Time MRI Analysis
Issue:
Volume 13, Issue 4, December 2025
Pages:
107-118
Received:
6 September 2025
Accepted:
16 September 2025
Published:
18 October 2025
DOI:
10.11648/j.jctr.20251304.13
Downloads:
Views:
Abstract: Accurate and rapid detection of brain tumors in magnetic resonance imaging (MRI) scans is critical for timely diagnosis and effective treatment planning. Manual interpretation of MRI data is time-consuming, subject to inter-observer variability, and limited in scalability, which highlights the need for automated solutions. This study presents a robust deep learning framework based on the latest YOLOv11 object detection architecture for real-time localization of brain tumors. A four-phase pipeline is implemented, consisting of dataset preparation, baseline training, hyperparameter optimization, and model evaluation. The Roboflow Universe Brain Tumor Dataset, including annotated categories of glioma, meningioma, pituitary tumor, and healthy cases, is preprocessed and partitioned into training, validation, and test sets to ensure unbiased assessment. Two YOLOv11 variants are systematically trained and evaluated. The YOLOv11m, achieved an mAP@50 of 0.9063, precision of 0.8858, and recall of 0.8614, delivering highly competitive results compared to YOLOv11s (mAP@50 = 0.9076). While YOLOv11s showed a marginal 0.14% higher detection accuracy. A comprehensive data analysis is performed using precision–recall curves, confusion matrices, ROC curves, and class-wise performance metrics to identify strengths and limitations across tumor categories. Notably, performance varied by tumor type: No Tumor (AP = 0.973) and Meningioma (AP = 0.964) achieved near-perfect detection, while Glioma (AP = 0.741) remained more challenging due to irregular shapes and contrast variations. These results demonstrate that YOLOv11m can deliver competitive detection accuracy with significantly faster inference than YOLOv11s and traditional CNN-based approaches, thereby enhancing both speed and reliability in automated neuro-oncological diagnostics. Future research will focus on integrating cross-dataset generalization, improving boundary localization (mAP@50–95), and extending the framework to multimodal MRI scans to support broader clinical applications. These results demonstrate the potential of advanced real-time object detection architectures in enhancing the speed and reliability of automated neuro-oncological diagnostics, supporting clinical workflows with consistent and precise tumor identification.
Abstract: Accurate and rapid detection of brain tumors in magnetic resonance imaging (MRI) scans is critical for timely diagnosis and effective treatment planning. Manual interpretation of MRI data is time-consuming, subject to inter-observer variability, and limited in scalability, which highlights the need for automated solutions. This study presents a rob...
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,
Fatemeh Sangsefidi
