With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). After defining the difference between ML and classical rule-based algorithms and the distinction among supervised, unsupervised and reinforcement learning, we explain the characteristic of deep learning (DL), a particular new type of ML, including its structure mimicking human neural networks and its ‘black box’ nature. Differences in the pipeline for applying ML and DL to prostate MRI are highlighted. The following potential clinical applications in different settings are outlined, many of them based only on MRI-unenhanced sequences: gland segmentation; assessment of lesion aggressiveness to distinguish between clinically significant and indolent cancers, allowing for active surveillance; cancer detection/diagnosis and localisation (transition versus peripheral zone, use of prostate imaging reporting and data system (PI-RADS) version 2), reading reproducibility, differentiation of cancers from prostatitis benign hyperplasia; local staging and pre-treatment assessment (detection of extraprostatic disease extension, planning of radiation therapy); and prediction of biochemical recurrence. Results are promising, but clinical applicability still requires more robust validation across scanner vendors, field strengths and institutions.

中文翻译：

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机器学习在前列腺癌磁共振成像中的应用。


通过这篇综述，我们的目的是提供最近提出的机器学习 (ML) 在放射学中的应用的概要，重点是前列腺磁共振成像 (MRI)。在定义了机器学习与经典的基于规则的算法之间的区别以及监督学习、无监督学习和强化学习之间的区别之后，我们解释了深度学习（DL）这种特殊的新型机器学习的特征，包括其模仿人类神经网络的结构及其“黑匣子”性质。重点介绍了将 ML 和 DL 应用于前列腺 MRI 的流程差异。概述了以下在不同环境中的潜在临床应用，其中许多仅基于 MRI 未增强序列：腺体分割；评估病变侵袭性，以区分有临床意义的癌症和惰性癌症，从而进行主动监测；癌症检测/诊断和定位（过渡区与周围区、前列腺成像报告和数据系统（PI-RADS）第2版的使用）、读数再现性、癌症与前列腺炎良性增生的鉴别；局部分期和治疗前评估（检测前列腺外疾病扩展、规划放射治疗）；和生化复发的预测。结果令人鼓舞，但临床适用性仍需要扫描仪供应商、领域实力和机构进行更强有力的验证。