Obeagu Emmanuel Ifeanyi, Obeagu Getrude Uzoma
Department of Medical Laboratory Science, Kampala International University.
School of Nursing Science, Kampala International University, Ishaka, Uganda.
Ann Med Surg (Lond). 2024 Aug 30;86(10):5980-5987. doi: 10.1097/MS9.0000000000002517. eCollection 2024 Oct.
Stage III breast cancer, characterized by locally advanced tumors and potential regional lymph node involvement, presents a formidable challenge to both patients and healthcare professionals. Accurate prediction of survival outcomes is crucial for guiding treatment decisions and optimizing patient care. This publication explores the potential clinical utility of predictive tools, encompassing genetic markers, imaging techniques, and clinical parameters, to improve survival outcome predictions in stage III breast cancer. Multimodal approaches, integrating these tools, hold the promise of delivering more precise and personalized predictions. Despite the inherent challenges, such as data standardization and genetic heterogeneity, the future offers opportunities for refinement, driven by precision medicine, artificial intelligence, and global collaboration. The goal is to empower healthcare providers to make informed treatment decisions, ultimately leading to improved survival outcomes and a brighter horizon for individuals facing this challenging disease.
III期乳腺癌的特征是局部晚期肿瘤和潜在的区域淋巴结受累，这对患者和医疗保健专业人员来说都是一项艰巨的挑战。准确预测生存结果对于指导治疗决策和优化患者护理至关重要。本出版物探讨了预测工具的潜在临床效用，包括基因标志物、成像技术和临床参数，以改善III期乳腺癌的生存结果预测。整合这些工具的多模式方法有望提供更精确和个性化的预测。尽管存在数据标准化和基因异质性等固有挑战，但在精准医学、人工智能和全球合作的推动下，未来仍有改进的机会。目标是使医疗保健提供者能够做出明智的治疗决策，最终改善生存结果，为面临这种具有挑战性疾病的患者带来更光明的前景。
