Medical Physiopathology - A.O.U. Città della Salute e della Scienza di Torino, Division of Nuclear Medicine, Department of Medical Science, University of Torino, 10126, Torino, Italy.
Nuclear Medicine Department, S. Croce e Carle Hospital, 12100, Cuneo, Italy.
Eur Radiol Exp. 2022 Jun 15;6(1):27. doi: 10.1186/s41747-022-00282-0.
In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients' risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these "big data" in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.
在前列腺癌（PCa）中，新型放射性药物的应用提高了诊断和分期的准确性，完善了监测策略，并引入了特异性和个体化的放射性受体治疗。核医学因此有望通过使用多组学方法和改善患者对定制药物的风险分层，来提高 PCa 患者的生活质量，处理和分析大量的分子成像数据。人工智能（AI）和放射组学可以使临床医生提高在诊断和治疗领域使用这些“大数据”的整体效率和准确性：从技术方面（如肿瘤分割、图像重建和解释的半自动）到临床结果，从而加深对 PCa 分子环境的理解，完善个性化治疗策略，并提高预测结果的能力。本系统评价旨在描述当前关于 AI 和放射组学应用于前列腺癌分子成像的文献。
