Li Huanye, Lee Chau Hung, Chia David, Lin Zhiping, Huang Weimin, Tan Cher Heng
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore.
Diagnostics (Basel). 2022 Jan 24;12(2):289. doi: 10.3390/diagnostics12020289.
Advances in our understanding of the role of magnetic resonance imaging (MRI) for the detection of prostate cancer have enabled its integration into clinical routines in the past two decades. The Prostate Imaging Reporting and Data System (PI-RADS) is an established imaging-based scoring system that scores the probability of clinically significant prostate cancer on MRI to guide management. Image fusion technology allows one to combine the superior soft tissue contrast resolution of MRI, with real-time anatomical depiction using ultrasound or computed tomography. This allows the accurate mapping of prostate cancer for targeted biopsy and treatment. Machine learning provides vast opportunities for automated organ and lesion depiction that could increase the reproducibility of PI-RADS categorisation, and improve co-registration across imaging modalities to enhance diagnostic and treatment methods that can then be individualised based on clinical risk of malignancy. In this article, we provide a comprehensive and contemporary review of advancements, and share insights into new opportunities in this field.
在过去二十年里，我们对磁共振成像（MRI）在前列腺癌检测中作用的理解取得了进展，这使得MRI能够融入临床常规操作。前列腺影像报告和数据系统（PI-RADS）是一个既定的基于影像的评分系统，它对MRI上临床显著前列腺癌的可能性进行评分，以指导治疗管理。图像融合技术使人们能够将MRI卓越的软组织对比分辨率与使用超声或计算机断层扫描的实时解剖描绘相结合。这使得能够对前列腺癌进行精确映射，以进行靶向活检和治疗。机器学习为自动器官和病变描绘提供了巨大机会，这可以提高PI-RADS分类的可重复性，并改善不同成像模态之间的配准，从而改进诊断和治疗方法，进而可以根据恶性肿瘤的临床风险进行个体化。在本文中，我们对相关进展进行了全面且与时俱进的综述，并分享了对该领域新机遇的见解。
