Haque Fahmida, Simon Benjamin D, Özyörük Kutsev B, Harmon Stephanie A, Türkbey Barış
Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, USA.
University of Oxford, Institute of Biomedical Engineering, Department of Engineering Science, Oxford, UK.
Balkan Med J. 2025 Jul 1;42(4):286-300. doi: 10.4274/balkanmedj.galenos.2025.2025-4-69.
Prostate cancer (PCa) is the second most common cancer in men and has a significant health and social burden, necessitating advances in early detection, prognosis, and treatment strategies. Improvement in medical imaging has significantly impacted early PCa detection, characterization, and treatment planning. However, with an increasing number of patients with PCa and comparatively fewer PCa imaging experts, interpreting large numbers of imaging data is burdensome, time-consuming, and prone to variability among experts. With the revolutionary advances of artificial intelligence (AI) in medical imaging, image interpretation tasks are becoming easier and exhibit the potential to reduce the workload on physicians. Generative AI (GenAI) is a recently popular sub-domain of AI that creates new data instances, often to resemble patterns and characteristics of the real data. This new field of AI has shown significant potential for generating synthetic medical images with diverse and clinically relevant information. In this narrative review, we discuss the basic concepts of GenAI and cover the recent application of GenAI in the PCa imaging domain. This review will help the readers understand where the PCa research community stands in terms of various medical image applications like generating multi-modal synthetic images, image quality improvement, PCa detection, classification, and digital pathology image generation. We also address the current safety concerns, limitations, and challenges of GenAI for technical and clinical adaptation, as well as the limitations of current literature, potential solutions, and future directions with GenAI for the PCa community.
前列腺癌（PCa）是男性中第二常见的癌症，对健康和社会造成了重大负担，因此需要在早期检测、预后和治疗策略方面取得进展。医学成像技术的改进对早期前列腺癌的检测、特征描述和治疗规划产生了重大影响。然而，随着前列腺癌患者数量的增加，而前列腺癌成像专家相对较少，解读大量成像数据既繁重又耗时，而且专家之间容易出现差异。随着人工智能（AI）在医学成像领域的革命性进展，图像解读任务正变得更加轻松，并且显示出减轻医生工作量的潜力。生成式人工智能（GenAI）是人工智能中最近流行的一个子领域，它可以创建新的数据实例，通常类似于真实数据的模式和特征。这个人工智能新领域在生成具有多样且临床相关信息的合成医学图像方面显示出巨大潜力。在这篇叙述性综述中，我们讨论了生成式人工智能的基本概念，并涵盖了其在前列腺癌成像领域的最新应用。这篇综述将帮助读者了解前列腺癌研究界在各种医学图像应用方面的现状，比如生成多模态合成图像、改善图像质量、前列腺癌检测、分类以及数字病理图像生成。我们还讨论了生成式人工智能在技术和临床应用方面当前的安全问题、局限性和挑战，以及当前文献的局限性、潜在解决方案和前列腺癌领域生成式人工智能的未来发展方向。
