Cancer stadium
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Understanding Cancer Stadium: Insights from Recent Research
Breast Cancer Stadium and Health Belief Model
Perceived Benefits and Barriers in Breast Cancer Stadium
Breast cancer remains a leading cause of death among women globally. A study conducted at POSA RSUD Dr. Soetomo Surabaya analyzed the relationship between perceived benefits and barriers with the stage (stadium) of breast cancer using the Health Belief Model. The research found significant correlations between these perceptions and the cancer stage. Specifically, perceived benefits (p=0.03) and perceived barriers (p=0.028) were both significantly related to the stage of breast cancer. This suggests that early treatment and addressing perceived barriers can potentially reduce morbidity in breast cancer patients.
Neuroblastoma Stadium and VEGF-C Expression
VEGF-C as a Risk Factor in Stadium 4 Neuroblastoma
Neuroblastoma, particularly in children, presents significant challenges at advanced stages. A study focused on the immunohistochemical expression of VEGF-C, CD34, and VEGFR-2 in children diagnosed with stage 4 neuroblastoma. The findings indicated that VEGF-C expression in tumor cells is a potent risk factor for treatment failure and tumor-related death. The co-localization of CD34 and VEGFR-2 within the endothelial layer of blood vessels suggests a mechanism where VEGF-C promotes early-phase metastasis by facilitating tumor cell invasion into blood vessels. This insight could guide future anti-angiogenic treatment strategies for neuroblastoma.
Cervical Cancer Stadium and Deep Learning
Convolutional Neural Networks in Cervical Cancer Stadium Classification
Cervical cancer, accounting for 6.6% of all female cancers, requires accurate staging for effective treatment. A study utilized convolutional neural networks (CNN) with deep residual network (ResidualNet) architecture to classify cervical cancer stages based on colposcopy images. The stages were categorized into five classes: normal cervix, stadium I, stadium II, stadium III, and stadium IV. Among the models tested, ResidualNet-50 demonstrated superior accuracy, sensitivity, and specificity, achieving 100% accuracy in classification with a shorter elapsed time compared to ResidualNet-101. This highlights the potential of advanced deep learning methods in improving cervical cancer diagnosis and staging.
Conclusion
Recent research underscores the importance of understanding the factors influencing cancer stadium across different types. For breast cancer, addressing perceived benefits and barriers can significantly impact the stage at diagnosis. In neuroblastoma, VEGF-C expression serves as a critical risk factor for advanced stages, guiding potential treatment strategies. Meanwhile, deep learning techniques like ResidualNet offer promising advancements in accurately classifying cervical cancer stages, enhancing diagnostic precision. These insights collectively contribute to better management and treatment outcomes for cancer patients.
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