前列腺癌（PCa）是一种具有高度异质性的恶性肿瘤，这给精确诊断和选择最佳治疗方案带来了困扰。多参数磁共振成像（mp-MRI）通过解剖和功能成像序列已经成为前列腺癌检测和特征化的常规和重要范式。此外，由于人工智能（AI）和图像数据处理的快速发展，利用放射组学提取定量数据已成为一个有前途的领域。放射组学通过提取成像特征获得新的成像生物标志物，并建立用于精确评估的模型。相比基于临床病理参数的传统模型，放射组学模型提供了一种可靠且非侵入性的选择，有助于精准医学的实施。本综述的目的是概述前列腺癌放射组学方面的相关研究，特别是基于MRI图像特征的放射组学模型的开发和验证。本文综述并总结了目前主要关注前列腺癌检测、侵袭性和预后评估方面的文献现状。本文鉴别了具有通用临床实施潜力的模型，而非仅关注图像生物标记物的识别和方法优化研究。此外，我们深入探讨了不同模型可解决的关键问题以及在临床情景中可能出现的障碍。本综述将鼓励研究人员根据实际临床需求设计模型，同时帮助泌尿外科医生更好地理解放射组学产生的有希望结果。

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.