Arslan Muhammad, Asim Muhammad, Sattar Hina, Khan Anita, Thoppil Ali Farsina, Zehra Muneeza, Talluri Keerthi
Medicine, Leeds Teaching Hospitals, NHS Trust, Leeds, GBR.
Emergency Medicine, Royal Free Hospital, London, GBR.
Cureus. 2024 Sep 24;16(9):e70097. doi: 10.7759/cureus.70097. eCollection 2024 Sep.
Breast cancer remains a leading cause of morbidity and mortality among women worldwide. Early detection and precise diagnosis are critical for effective treatment and improved patient outcomes. This review explores the evolving role of radiology in the diagnosis and treatment of breast cancer, highlighting advancements in imaging technologies and the integration of artificial intelligence (AI). Traditional imaging modalities such as mammography, ultrasound, and magnetic resonance imaging have been the cornerstone of breast cancer diagnostics, with each modality offering unique advantages. The advent of radiomics, which involves extracting quantitative data from medical images, has further augmented the diagnostic capabilities of these modalities. AI, particularly deep learning algorithms, has shown potential in improving diagnostic accuracy and reducing observer variability across imaging modalities. AI-driven tools are increasingly being integrated into clinical workflows to assist in image interpretation, lesion classification, and treatment planning. Additionally, radiology plays a crucial role in guiding treatment decisions, particularly in the context of image-guided radiotherapy and monitoring response to neoadjuvant chemotherapy. The review also discusses the emerging field of theranostics, where diagnostic imaging is combined with therapeutic interventions to provide personalized cancer care. Despite these advancements, challenges such as the need for large annotated datasets and the integration of AI into clinical practice remain. The review concludes that while the role of radiology in breast cancer management is rapidly evolving, further research is required to fully realize the potential of these technologies in improving patient outcomes.
乳腺癌仍然是全球女性发病和死亡的主要原因。早期检测和精确诊断对于有效治疗和改善患者预后至关重要。本综述探讨了放射学在乳腺癌诊断和治疗中不断演变的作用，重点介绍了成像技术的进步以及人工智能（AI）的整合。传统成像方式如乳腺X线摄影、超声和磁共振成像一直是乳腺癌诊断的基石，每种方式都有其独特优势。放射组学的出现，即从医学图像中提取定量数据，进一步增强了这些方式的诊断能力。人工智能，尤其是深度学习算法，在提高诊断准确性和减少不同成像方式下观察者的变异性方面显示出潜力。人工智能驱动的工具越来越多地被整合到临床工作流程中，以协助图像解读、病变分类和治疗计划。此外，放射学在指导治疗决策方面起着关键作用，特别是在图像引导放疗和监测新辅助化疗反应的背景下。本综述还讨论了治疗诊断学这一新兴领域，其中诊断成像与治疗干预相结合，以提供个性化的癌症护理。尽管有这些进展，但仍存在一些挑战，如需要大量带注释的数据集以及将人工智能整合到临床实践中。综述得出结论，虽然放射学在乳腺癌管理中的作用正在迅速演变，但仍需要进一步研究以充分实现这些技术在改善患者预后方面的潜力。
