MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer.

Xuehua ZHU; Lizhi SHAO; Zhenyu LIU; Zenan LIU; Jide HE; Jiangang LIU; Hao PING; Jian LU

Return

MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer.

Author: Xuehua ZHU ¹ ; Lizhi SHAO ² ; Zhenyu LIU ² ; Zenan LIU ¹ ; Jide HE ¹ ; Jiangang LIU ³ ; Hao PING ⁴ ; Jian LU ⁵
Author Information

1. Department of Urology, Peking University Third Hospital, Beijing 100191, China.
2. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
3. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Engineering Medicine, Beihang University, Beijing 100191, China.
4. Department of Urology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China. pinghaotrh@ccmu.edu.cn.
5. Department of Urology, Peking University Third Hospital, Beijing 100191, China. lujian@bjmu.edu.cn.
Publication Type:Journal Article
Keywords: Magnetic resonance imaging (MRI); Predictive model; Prostate cancer; Radiomics
MeSH: Male; Humans; Artificial Intelligence; Magnetic Resonance Imaging/methods*; Prostatic Neoplasms/diagnostic imaging*; Image Processing, Computer-Assisted/methods*; Precision Medicine; Retrospective Studies
From: Journal of Zhejiang University. Science. B 2023;24(8):663-681
CountryChina
Language:English
Abstract: Prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a conundrum for making a precise diagnosis and choosing an optimal treatment approach. Multiparametric magnetic resonance imaging (mp-MRI) with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of PCa. Moreover, using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence (AI) and image data processing. Radiomics acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise evaluation. Radiomics models provide a reliable and noninvasive alternative to aid in precision medicine, demonstrating advantages over traditional models based on clinicopathological parameters. The purpose of this review is to provide an overview of related studies of radiomics in PCa, specifically around the development and validation of radiomics models using MRI-derived image features. The current landscape of the literature, focusing mainly on PCa detection, aggressiveness, and prognosis evaluation, is reviewed and summarized. Rather than studies that exclusively focus on image biomarker identification and method optimization, models with high potential for universal clinical implementation are identified. Furthermore, we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical scenario. This review will encourage researchers to design models based on actual clinical needs, as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.