Radiotheranostics

放射疗法
  • 文章类型: Journal Article
    放射热学是指放射性成像生物标志物与递送电离辐射的放射性治疗化合物的配对。鉴于非常有前途的放射性药物的推出,放射治疗方法正在创造一种个性化的新范式,靶向放射性核素治疗(TRT),也称为放射性药物(RPT)。靶向生长抑素受体(SSTR)和前列腺特异性膜抗原(PSMA)的放射治疗对越来越多地用于诊断和治疗患有转移性神经内分泌肿瘤(NET)和前列腺癌的患者。并行,影像组学和人工智能(AI),作为定量图像分析的重要领域,正在为诊断和治疗领域的显着增强的工作流程铺平了道路,从数据和图像处理到临床决策支持,改善患者选择,个性化治疗策略,响应预测,和预测。此外,AI在患者剂量测定中具有巨大的有效性,可以应对RPT工作流程中复杂而耗时的任务。本工作全面概述了放射学和人工智能在放射学中的应用,专注于成对的SSR或PSMA靶向放射性配体,描述基本概念和特定的成像/治疗特征。我们的综述包括68Ga放射性标记的配体,18F,177Lu,64Cu,90Y,225Ac具体来说,通过影像组学和人工智能对改善图像采集的贡献,重建,治疗反应,分割,restaging,病变分类,剂量预测,和估计以及正在进行的发展和未来的方向进行了讨论。
    Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.
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