School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu-632014, India.
Curr Med Imaging. 2022;18(1):3-17. doi: 10.2174/1573405617666210303102526.
Breast cancer has become a global problem. Though concerns regarding early detection and accurate diagnosis have been raised, continued efforts are required for the development of precision medicine. In the past years, the area of medicinal imaging has seen an unprecedented growth that has led to an advancement of radiomics, which provides countless quantitative biomarkers extracted from modern diagnostic images, including a detailed tumor characterization of breast malignancy.
In this review, we have presented the methodology and implementation of radiomics together with its future trends and challenges on the basis of published papers. Radiomics could distinguish malignant from benign tumors, predict prognostic factors, molecular subtypes of breast carcinoma, treatment response to neoadjuvant chemotherapy (NAC), and recurrence survival. The incorporation of quantitative knowledge with clinical, histopathological, and genomic information will enable physicians to afford customized care of treatment for patients with breast cancer.
This review was intended to help physicians and radiologists gain fundamental knowledge regarding radiomics, and also to work collaboratively with researchers to explore evidence for its further usage in clinical practice.
乳腺癌已成为全球性问题。尽管人们对早期检测和准确诊断提出了关注，但仍需要继续努力开发精准医学。在过去的几年中，医学成像领域经历了前所未有的增长，这导致了放射组学的发展，放射组学提供了无数从现代诊断图像中提取的定量生物标志物，包括对乳腺癌恶性肿瘤的详细肿瘤特征描述。
基于已发表的论文，我们在本文中介绍了放射组学的方法和实施情况，以及其未来的趋势和挑战。放射组学可以区分良恶性肿瘤，预测预后因素、乳腺癌的分子亚型、新辅助化疗（NAC）的治疗反应和复发生存。将定量知识与临床、组织病理学和基因组信息相结合，将使医生能够为乳腺癌患者提供个性化的治疗护理。
本文旨在帮助医生和放射科医生获得关于放射组学的基础知识，并与研究人员合作，探索其在临床实践中进一步应用的证据。
