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治疗诊断学与人工智能:个性化医疗的新前沿。

Theranostics and artificial intelligence: new frontiers in personalized medicine.

作者信息

Belge Bilgin Gokce, Bilgin Cem, Burkett Brian J, Orme Jacob J, Childs Daniel S, Thorpe Matthew P, Halfdanarson Thorvardur R, Johnson Geoffrey B, Kendi Ayse Tuba, Sartor Oliver

机构信息

Department of Radiology, Mayo Clinic Rochester, MN, USA.

Department of Oncology, Mayo Clinic Rochester, MN, USA.

出版信息

Theranostics. 2024 Mar 25;14(6):2367-2378. doi: 10.7150/thno.94788. eCollection 2024.

Abstract

The field of theranostics is rapidly advancing, driven by the goals of enhancing patient care. Recent breakthroughs in artificial intelligence (AI) and its innovative theranostic applications have marked a critical step forward in nuclear medicine, leading to a significant paradigm shift in precision oncology. For instance, AI-assisted tumor characterization, including automated image interpretation, tumor segmentation, feature identification, and prediction of high-risk lesions, improves diagnostic processes, offering a precise and detailed evaluation. With a comprehensive assessment tailored to an individual's unique clinical profile, AI algorithms promise to enhance patient risk classification, thereby benefiting the alignment of patient needs with the most appropriate treatment plans. By uncovering potential factors unseeable to the human eye, such as intrinsic variations in tumor radiosensitivity or molecular profile, AI software has the potential to revolutionize the prediction of response heterogeneity. For accurate and efficient dosimetry calculations, AI technology offers significant advantages by providing customized phantoms and streamlining complex mathematical algorithms, making personalized dosimetry feasible and accessible in busy clinical settings. AI tools have the potential to be leveraged to predict and mitigate treatment-related adverse events, allowing early interventions. Additionally, generative AI can be utilized to find new targets for developing novel radiopharmaceuticals and facilitate drug discovery. However, while there is immense potential and notable interest in the role of AI in theranostics, these technologies do not lack limitations and challenges. There remains still much to be explored and understood. In this study, we investigate the current applications of AI in theranostics and seek to broaden the horizons for future research and innovation.

摘要

在提高患者护理水平目标的推动下,诊疗一体化领域正在迅速发展。人工智能(AI)及其创新性诊疗应用的最新突破标志着核医学向前迈出了关键一步,导致精准肿瘤学发生了重大的范式转变。例如,AI辅助的肿瘤特征分析,包括自动图像解读、肿瘤分割、特征识别以及高危病变预测,改善了诊断流程,提供了精确而详细的评估。通过根据个体独特的临床特征进行全面评估,AI算法有望加强患者风险分类,从而使患者需求与最合适的治疗方案相匹配。通过揭示人眼不可见的潜在因素,如肿瘤放射敏感性或分子特征的内在差异,AI软件有可能彻底改变反应异质性的预测。对于准确高效的剂量计算,AI技术通过提供定制体模和简化复杂的数学算法具有显著优势,使个性化剂量计算在繁忙的临床环境中可行且易于实现。AI工具有可能用于预测和减轻与治疗相关的不良事件,实现早期干预。此外,生成式AI可用于寻找开发新型放射性药物的新靶点并促进药物发现。然而,尽管AI在诊疗一体化中的作用具有巨大潜力且备受关注,但这些技术并非没有局限性和挑战。仍有许多有待探索和理解的地方。在本研究中,我们调查了AI在诊疗一体化中的当前应用,并试图拓宽未来研究与创新的视野。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b3ac/11024845/66aa0ab4a8f4/thnov14p2367g001.jpg

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