首页 > 最新文献

Nuklearmedizin. Nuclear medicine最新文献

英文 中文
Artificial Intelligence-powered automatic volume calculation in medical images - available tools, performance and challenges for nuclear medicine. 医学图像中人工智能驱动的自动体积计算——核医学的可用工具、性能和挑战。
Pub Date : 2023-12-01 Epub Date: 2023-11-23 DOI: 10.1055/a-2200-2145
Thomas Wendler, Michael C Kreissl, Benedikt Schemmer, Julian Manuel Michael Rogasch, Francesca De Benetti

Volumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging than organ volumetry. Liver volumetry has shown progression in accuracy with a decrease in error. If a relative error below 10 % is acceptable, ML-based liver volumetry can be considered reliable for standardized imaging protocols if used in patients without major anomalies. Similarly, ML-supported automatic kidney volumetry has also shown consistency and reliability in volumetric calculations. In contrast, AI-supported thyroid volumetry has not been extensively developed, despite initial works in 3D ultrasound showing promising results in terms of accuracy and reproducibility. Despite the advancements presented in the reviewed literature, the lack of standardization limits the generalizability of ML methods across diverse scenarios. The domain gap, i. e., the difference in probability distribution of training and inference data, is of paramount importance before clinical deployment of AI, to maintain accuracy and reliability in patient care. The increasing availability of improved segmentation tools is expected to further incorporate AI methods into routine workflows where volumetry will play a more prominent role in radionuclide therapy planning and quantitative follow-up of disease evolution.

体积测量在肿瘤学和内分泌学中至关重要,用于诊断、治疗计划和评估几种疾病的治疗反应。人工智能(AI)和深度学习(DL)的集成大大加速了体积计算的自动化,提高了准确性,减少了可变性和劳动力。在这篇综述中,我们发现在肿瘤体积测量中,机器学习(ML)方法和专家评估之间存在高度相关性;然而,它被认为比器官体积测定法更具挑战性。肝容量测量显示准确度的提高和误差的减少。如果相对误差低于10%是可以接受的,那么在没有重大异常的患者中,基于ml的肝脏体积测量可以被认为是可靠的标准化成像方案。同样,ml支持的自动肾脏体积测定在体积计算中也显示出一致性和可靠性。相比之下,人工智能支持的甲状腺体积测量尚未得到广泛发展,尽管3D超声的初步工作在准确性和可重复性方面显示出有希望的结果。尽管在文献综述中提出了进步,但缺乏标准化限制了机器学习方法在不同场景中的推广。域间隙,即。在临床部署人工智能之前,训练数据和推理数据的概率分布差异是至关重要的,以保持患者护理的准确性和可靠性。改进的分割工具的日益可用性预计将进一步将人工智能方法纳入常规工作流程,其中体积法将在放射性核素治疗计划和疾病演变的定量随访中发挥更突出的作用。
{"title":"Artificial Intelligence-powered automatic volume calculation in medical images - available tools, performance and challenges for nuclear medicine.","authors":"Thomas Wendler, Michael C Kreissl, Benedikt Schemmer, Julian Manuel Michael Rogasch, Francesca De Benetti","doi":"10.1055/a-2200-2145","DOIUrl":"10.1055/a-2200-2145","url":null,"abstract":"<p><p>Volumetry is crucial in oncology and endocrinology, for diagnosis, treatment planning, and evaluating response to therapy for several diseases. The integration of Artificial Intelligence (AI) and Deep Learning (DL) has significantly accelerated the automatization of volumetric calculations, enhancing accuracy and reducing variability and labor. In this review, we show that a high correlation has been observed between Machine Learning (ML) methods and expert assessments in tumor volumetry; Yet, it is recognized as more challenging than organ volumetry. Liver volumetry has shown progression in accuracy with a decrease in error. If a relative error below 10 % is acceptable, ML-based liver volumetry can be considered reliable for standardized imaging protocols if used in patients without major anomalies. Similarly, ML-supported automatic kidney volumetry has also shown consistency and reliability in volumetric calculations. In contrast, AI-supported thyroid volumetry has not been extensively developed, despite initial works in 3D ultrasound showing promising results in terms of accuracy and reproducibility. Despite the advancements presented in the reviewed literature, the lack of standardization limits the generalizability of ML methods across diverse scenarios. The domain gap, i. e., the difference in probability distribution of training and inference data, is of paramount importance before clinical deployment of AI, to maintain accuracy and reliability in patient care. The increasing availability of improved segmentation tools is expected to further incorporate AI methods into routine workflows where volumetry will play a more prominent role in radionuclide therapy planning and quantitative follow-up of disease evolution.</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 6","pages":"343-353"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138300855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies. 人工智能在放射药物治疗剂量测定中的应用。
Pub Date : 2023-12-01 Epub Date: 2023-10-12 DOI: 10.1055/a-2179-6872
Julia Franziska Brosch-Lenz, Astrid Delker, Fabian Schmidt, Johannes Tran-Gia

Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.

常规临床剂量测定和放射药物治疗是未来个性化治疗的关键。然而,剂量测定被认为是复杂和耗时的,在剂量测定工作流程中所需的步骤中存在各种挑战。基于图像的剂量测定的一般工作流程包括定量成像、器官和肿瘤的分割、时间活动曲线的拟合以及转换为吸收剂量。这项工作回顾了使用人工智能来提高剂量测定工作流程每一步的速度和准确性的潜力和优势。
{"title":"On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies.","authors":"Julia Franziska Brosch-Lenz, Astrid Delker, Fabian Schmidt, Johannes Tran-Gia","doi":"10.1055/a-2179-6872","DOIUrl":"10.1055/a-2179-6872","url":null,"abstract":"<p><p>Routine clinical dosimetry along with radiopharmaceutical therapies is key for future treatment personalization. However, dosimetry is considered complex and time-consuming with various challenges amongst the required steps within the dosimetry workflow. The general workflow for image-based dosimetry consists of quantitative imaging, the segmentation of organs and tumors, fitting of the time-activity-curves, and the conversion to absorbed dose. This work reviews the potential and advantages of the use of artificial intelligence to improve speed and accuracy of every single step of the dosimetry workflow.</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":" ","pages":"379-388"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41224276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Clinical Applications of Radiomics in Nuclear Medicine. 放射组学在核医学中的临床应用。
Pub Date : 2023-12-01 Epub Date: 2023-11-07 DOI: 10.1055/a-2191-3271
Philipp Lohmann, Ralph Alexander Bundschuh, Isabelle Miederer, Felix M Mottaghy, Karl Josef Langen, Norbert Galldiks

Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine. This review article examines the current state of radiomics in nuclear medicine and shows its potential to improve patient care. Selected clinical applications for diseases such as cancer, neurodegenerative diseases, cardiovascular problems and thyroid diseases are examined. The article concludes with a brief classification in terms of future perspectives and strategies for linking research findings to clinical practice.

放射组学是人工智能的一个新兴领域,专注于从医学图像中提取和分析定量特征,如强度、形状、纹理和空间关系。这些特征通常是人眼无法察觉的,可以揭示复杂的模式和生物学见解。它们还可以与临床数据相结合,使用机器学习创建预测模型,以改善核医学中的疾病特征。这篇综述文章探讨了核医学中放射组学的现状,并展示了其改善患者护理的潜力。研究了癌症、神经退行性疾病、心血管问题和甲状腺疾病等疾病的选定临床应用。文章最后对未来的前景和将研究结果与临床实践联系起来的策略进行了简要的分类。
{"title":"Clinical Applications of Radiomics in Nuclear Medicine.","authors":"Philipp Lohmann, Ralph Alexander Bundschuh, Isabelle Miederer, Felix M Mottaghy, Karl Josef Langen, Norbert Galldiks","doi":"10.1055/a-2191-3271","DOIUrl":"10.1055/a-2191-3271","url":null,"abstract":"<p><p>Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine. This review article examines the current state of radiomics in nuclear medicine and shows its potential to improve patient care. Selected clinical applications for diseases such as cancer, neurodegenerative diseases, cardiovascular problems and thyroid diseases are examined. The article concludes with a brief classification in terms of future perspectives and strategies for linking research findings to clinical practice.</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":" ","pages":"354-360"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71490666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions. 推进PET图像重建的人工智能和深度学习:最新技术和未来方向。
Pub Date : 2023-12-01 Epub Date: 2023-11-23 DOI: 10.1055/a-2198-0358
Dirk Hellwig, Nils Constantin Hellwig, Steven Boehner, Timo Fuchs, Regina Fischer, Daniel Schmidt

Positron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like filtered backprojection and iterative algorithms are powerful but face limitations. PET IR can be seen as an image-to-image translation. Artificial intelligence (AI) and deep learning (DL) using multilayer neural networks enable a new approach to this computer vision task. This review aims to provide mutual understanding for nuclear medicine professionals and AI researchers. We outline fundamentals of PET imaging as well as state-of-the-art in AI-based PET IR with its typical algorithms and DL architectures. Advances improve resolution and contrast recovery, reduce noise, and remove artifacts via inferred attenuation and scatter correction, sinogram inpainting, denoising, and super-resolution refinement. Kernel-priors support list-mode reconstruction, motion correction, and parametric imaging. Hybrid approaches combine AI with conventional IR. Challenges of AI-assisted PET IR include availability of training data, cross-scanner compatibility, and the risk of hallucinated lesions. The need for rigorous evaluations, including quantitative phantom validation and visual comparison of diagnostic accuracy against conventional IR, is highlighted along with regulatory issues. First approved AI-based applications are clinically available, and its impact is foreseeable. Emerging trends, such as the integration of multimodal imaging and the use of data from previous imaging visits, highlight future potentials. Continued collaborative research promises significant improvements in image quality, quantitative accuracy, and diagnostic performance, ultimately leading to the integration of AI-based IR into routine PET imaging protocols.

正电子发射断层扫描(PET)是诊断疾病和监测治疗的重要手段。传统的图像重建技术,如滤波反向投影和迭代算法,功能强大,但面临局限性。PET IR可以看作是一种图像到图像的转换。人工智能(AI)和使用多层神经网络的深度学习(DL)为这一计算机视觉任务提供了一种新的方法。这篇综述旨在为核医学专业人员和人工智能研究人员提供相互理解。我们概述了PET成像的基本原理以及基于人工智能的PET IR及其典型算法和DL架构的最新技术。进步提高了分辨率和对比度恢复,降低了噪声,并通过推断衰减和散射校正、sinogram inpainting、去噪和超分辨率细化来去除伪影。核先验支持列表模式重建、运动校正和参数化成像。混合方法将人工智能与传统IR结合起来。人工智能辅助PET IR的挑战包括训练数据的可用性、交叉扫描仪的兼容性以及幻觉病变的风险。严格评估的需要,包括定量幻象验证和与传统红外诊断准确性的视觉比较,与监管问题一起被强调。第一个批准的基于人工智能的应用是临床可用的,其影响是可以预见的。新出现的趋势,如整合多模式成像和使用以前的成像访问数据,突出了未来的潜力。持续的合作研究有望显著改善图像质量、定量准确性和诊断性能,最终将基于人工智能的红外技术整合到常规PET成像方案中。
{"title":"Artificial Intelligence and Deep Learning for Advancing PET Image Reconstruction: State-of-the-Art and Future Directions.","authors":"Dirk Hellwig, Nils Constantin Hellwig, Steven Boehner, Timo Fuchs, Regina Fischer, Daniel Schmidt","doi":"10.1055/a-2198-0358","DOIUrl":"10.1055/a-2198-0358","url":null,"abstract":"<p><p>Positron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like filtered backprojection and iterative algorithms are powerful but face limitations. PET IR can be seen as an image-to-image translation. Artificial intelligence (AI) and deep learning (DL) using multilayer neural networks enable a new approach to this computer vision task. This review aims to provide mutual understanding for nuclear medicine professionals and AI researchers. We outline fundamentals of PET imaging as well as state-of-the-art in AI-based PET IR with its typical algorithms and DL architectures. Advances improve resolution and contrast recovery, reduce noise, and remove artifacts via inferred attenuation and scatter correction, sinogram inpainting, denoising, and super-resolution refinement. Kernel-priors support list-mode reconstruction, motion correction, and parametric imaging. Hybrid approaches combine AI with conventional IR. Challenges of AI-assisted PET IR include availability of training data, cross-scanner compatibility, and the risk of hallucinated lesions. The need for rigorous evaluations, including quantitative phantom validation and visual comparison of diagnostic accuracy against conventional IR, is highlighted along with regulatory issues. First approved AI-based applications are clinically available, and its impact is foreseeable. Emerging trends, such as the integration of multimodal imaging and the use of data from previous imaging visits, highlight future potentials. Continued collaborative research promises significant improvements in image quality, quantitative accuracy, and diagnostic performance, ultimately leading to the integration of AI-based IR into routine PET imaging protocols.</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 6","pages":"334-342"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138300854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities. 多参数肿瘤学混合成像:机器学习的挑战和机遇。
Pub Date : 2023-10-01 Epub Date: 2023-10-06 DOI: 10.1055/a-2157-6670
Thomas Küstner, Tobias Hepp, Ferdinand Seith

Background: Machine learning (ML) is considered an important technology for future data analysis in health care.

Methods: The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers.

Results and conclusion: In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future.

Key points: · ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET..

背景:机器学习(ML)被认为是未来医疗保健数据分析的重要技术。方法:诊断放射学和核医学这两个固有的技术驱动领域都将在图像采集和重建方面受益于ML。在未来几年内,这将加速图像采集,提高图像质量,减少运动伪影,并减少PET成像的辐射暴露和衰减校正的新方法。此外,ML有可能通过对来自不同模式的数据进行组合分析来支持决策,尤其是在肿瘤学中。在这种情况下,我们看到ML在多参数混合成像和成像生物标志物开发方面的巨大潜力。结果和结论:在这篇综述中,我们将描述ML的基础,介绍MRI、CT和PET混合成像的方法,并讨论与之相关的具体挑战,以及使ML成为未来诊断和临床工具的步骤。要点:·ML为MRI、CT和PET混合成像的重建、处理和分析提供了可行的临床解决方案。。
{"title":"Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities.","authors":"Thomas Küstner,&nbsp;Tobias Hepp,&nbsp;Ferdinand Seith","doi":"10.1055/a-2157-6670","DOIUrl":"10.1055/a-2157-6670","url":null,"abstract":"<p><strong>Background: </strong>Machine learning (ML) is considered an important technology for future data analysis in health care.</p><p><strong>Methods: </strong>The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers.</p><p><strong>Results and conclusion: </strong>In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future.</p><p><strong>Key points: </strong>· ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET..</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 5","pages":"306-313"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41144225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Ectopic Thyroid Tissue in the Uterus Identified by Iodine-131 SPECT/CT. 碘-131 SPECT/CT鉴别子宫异位甲状腺组织。
Pub Date : 2023-10-01 Epub Date: 2023-10-06 DOI: 10.1055/a-2127-8006
Hansol Moon, Eu Jeong Ku, Chulhan Kim
A 45-year-old woman underwent a total thyroidectomy and was subsequently treated with 1110 MBq of radioactive iodine-131 ablation therapy for papillary thyroid cancer. A post-therapy iodine-131 whole-body scan revealed a focal uptake in the mid-pelvic area. To identify the exact anatomical location, SPECT/CT images were taken. The SPECT/CT fusion images revealed that the uptake was in the uterus, which was considered as radioactive iodine-avid ectopic thyroid tissue in the uterus.
{"title":"Ectopic Thyroid Tissue in the Uterus Identified by Iodine-131 SPECT/CT.","authors":"Hansol Moon,&nbsp;Eu Jeong Ku,&nbsp;Chulhan Kim","doi":"10.1055/a-2127-8006","DOIUrl":"10.1055/a-2127-8006","url":null,"abstract":"A 45-year-old woman underwent a total thyroidectomy and was subsequently treated with 1110 MBq of radioactive iodine-131 ablation therapy for papillary thyroid cancer. A post-therapy iodine-131 whole-body scan revealed a focal uptake in the mid-pelvic area. To identify the exact anatomical location, SPECT/CT images were taken. The SPECT/CT fusion images revealed that the uptake was in the uterus, which was considered as radioactive iodine-avid ectopic thyroid tissue in the uterus.","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 5","pages":"294-295"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41173533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Perspectives of Evidence-Based Therapy Management. 循证治疗管理的观点。
Pub Date : 2023-10-01 Epub Date: 2023-10-06 DOI: 10.1055/a-2159-6949
Fabian Kiessling, Volkmar Schulz

Background: Therapeutics that specifically address biological processes often require a much finer selection of patients and subclassification of diseases. Thus, diagnostic procedures must describe the diseases in sufficient detail to allow selection of appropriate therapy and to sensitively track therapy response. Anatomical features are often not sufficient for this purpose and there is a need to image molecular and pathophysiological processes.

Method: Two imaging strategies can be pursued: molecular imaging attempts to image a few biomarkers that play key roles in pathological processes. Alternatively, patterns describing a biological process can be identified from the synopsis of multiple (non-specific) imaging markers, possibly in combination with omics and other clinical findings. Here, AI-based methods are increasingly being used.

Results: Both strategies of evidence-based therapy management are explained in this review article and examples and clinical successes are presented. In this context, reviews of clinically approved molecular diagnostics and decision support systems are listed. Furthermore, since reliable, representative, and sufficiently large datasets are further important prerequisites for AI-assisted multiparametric analyses, concepts are presented to make data available in a structured way, e. g., using Generative Adversarial Networks to complement databases with virtual cases and to build completely anonymous reference databases.

Conclusion: Molecular imaging and computer-assisted cluster analysis of diagnostic data are complementary methods to describe pathophysiological processes. Both methods have the potential to improve (evidence-based) the future management of therapies, partly on their own but also in combined approaches.

Key points: · Molecular imaging and radiomics provide valuable complementary disease biomarkers.. · Data-driven, model-based, and hybrid model-based integrated diagnostics advance precision medicine.. · Synthetic data generation may become essential in the development process of future AI methods..

背景:专门针对生物过程的治疗方法通常需要对患者进行更精细的选择和疾病的亚类化。因此,诊断程序必须足够详细地描述疾病,以便选择适当的治疗方法并敏感地跟踪治疗反应。解剖特征通常不足以达到这一目的,需要对分子和病理生理过程进行成像。方法:可以采用两种成像策略:分子成像尝试对在病理过程中起关键作用的一些生物标志物进行成像。或者,描述生物过程的模式可以从多种(非特异性)成像标记的概要中识别,可能与组学和其他临床发现相结合。在这里,基于人工智能的方法越来越多地被使用。结果:这篇综述文章解释了循证治疗管理的两种策略,并列举了实例和临床成功案例。在此背景下,列出了临床批准的分子诊断和决策支持系统的综述。此外,由于可靠、有代表性和足够大的数据集是人工智能辅助多参数分析的重要先决条件,因此提出了以结构化方式提供数据的概念。 g.使用生成对抗性网络用虚拟案例补充数据库,并建立完全匿名的参考数据库。结论:分子成像和诊断数据的计算机辅助聚类分析是描述病理生理过程的补充方法。这两种方法都有可能改善(循证)未来的治疗管理,部分是单独的,但也有联合的方法。要点:·分子成像和放射组学提供了有价值的互补疾病生物标志物。·数据驱动、基于模型和基于混合模型的集成诊断促进了精准医疗。·合成数据生成可能在未来人工智能方法的开发过程中变得至关重要。。
{"title":"Perspectives of Evidence-Based Therapy Management.","authors":"Fabian Kiessling,&nbsp;Volkmar Schulz","doi":"10.1055/a-2159-6949","DOIUrl":"https://doi.org/10.1055/a-2159-6949","url":null,"abstract":"<p><strong>Background: </strong>Therapeutics that specifically address biological processes often require a much finer selection of patients and subclassification of diseases. Thus, diagnostic procedures must describe the diseases in sufficient detail to allow selection of appropriate therapy and to sensitively track therapy response. Anatomical features are often not sufficient for this purpose and there is a need to image molecular and pathophysiological processes.</p><p><strong>Method: </strong>Two imaging strategies can be pursued: molecular imaging attempts to image a few biomarkers that play key roles in pathological processes. Alternatively, patterns describing a biological process can be identified from the synopsis of multiple (non-specific) imaging markers, possibly in combination with omics and other clinical findings. Here, AI-based methods are increasingly being used.</p><p><strong>Results: </strong>Both strategies of evidence-based therapy management are explained in this review article and examples and clinical successes are presented. In this context, reviews of clinically approved molecular diagnostics and decision support systems are listed. Furthermore, since reliable, representative, and sufficiently large datasets are further important prerequisites for AI-assisted multiparametric analyses, concepts are presented to make data available in a structured way, e. g., using Generative Adversarial Networks to complement databases with virtual cases and to build completely anonymous reference databases.</p><p><strong>Conclusion: </strong>Molecular imaging and computer-assisted cluster analysis of diagnostic data are complementary methods to describe pathophysiological processes. Both methods have the potential to improve (evidence-based) the future management of therapies, partly on their own but also in combined approaches.</p><p><strong>Key points: </strong>· Molecular imaging and radiomics provide valuable complementary disease biomarkers.. · Data-driven, model-based, and hybrid model-based integrated diagnostics advance precision medicine.. · Synthetic data generation may become essential in the development process of future AI methods..</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 5","pages":"314-322"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41161261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Artificial Intelligence in Oncological Hybrid Imaging. 肿瘤混合成像中的人工智能。
Pub Date : 2023-10-01 Epub Date: 2023-10-06 DOI: 10.1055/a-2157-6810
Benedikt Feuerecker, Maurice M Heimer, Thomas Geyer, Matthias P Fabritius, Sijing Gu, Balthasar Schachtner, Leonie Beyer, Jens Ricke, Sergios Gatidis, Michael Ingrisch, Clemens C Cyran

Background:  Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.

Methods and results:  The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.

Conclusion:  AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.

Key points:   · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..

背景: 人工智能(AI)应用在医学成像的广泛环境中变得越来越重要。由于肿瘤学混合成像中产生了大量的成像数据,AI应用于初级分期、治疗监测和复发检测中的病变检测和表征是可取的。鉴于机器学习(ML)和深度学习(DL)方法的快速发展,人工智能的作用将对成像工作流程产生重大影响,并最终改善临床决策和结果。方法和结果: 这篇叙述性综述的第一部分讨论了当前的研究,介绍了肿瘤学混合成像中的人工智能和数据科学中的关键概念。第二部分回顾了相关的例子,重点是肿瘤学中的应用,以及对挑战和当前局限性的讨论。结论: 人工智能应用程序有潜力以高效率和深度利用诊断数据流,促进自动病变检测、表征和治疗监测,最终提高整个医疗成像工作流程的质量和效率。目标是生成可重复的、结构化的、定量的诊断数据,用于肿瘤学的循证治疗指导。然而,在应用程序开发、基准测试和临床实施方面仍然存在重大挑战。要点:  · 混合成像生成大量复杂度和深度高的多模态医学成像数据。·需要先进的工具来实现整个放射学价值链上的快速且经济高效的处理。·人工智能应用有望促进肿瘤疾病的混合成像评估,具有高质量和高效的病变检测、表征和反应评估。目标是为循证肿瘤学治疗指南生成可重复、结构化、定量的诊断数据在三个肿瘤学实体(肺、前列腺和神经内分泌肿瘤)中的选定应用表明,人工智能算法如何影响混合成像中基于成像的任务,并可能指导临床决策。。
{"title":"Artificial Intelligence in Oncological Hybrid Imaging.","authors":"Benedikt Feuerecker,&nbsp;Maurice M Heimer,&nbsp;Thomas Geyer,&nbsp;Matthias P Fabritius,&nbsp;Sijing Gu,&nbsp;Balthasar Schachtner,&nbsp;Leonie Beyer,&nbsp;Jens Ricke,&nbsp;Sergios Gatidis,&nbsp;Michael Ingrisch,&nbsp;Clemens C Cyran","doi":"10.1055/a-2157-6810","DOIUrl":"https://doi.org/10.1055/a-2157-6810","url":null,"abstract":"<p><strong>Background: </strong> Artificial intelligence (AI) applications have become increasingly relevant across a broad spectrum of settings in medical imaging. Due to the large amount of imaging data that is generated in oncological hybrid imaging, AI applications are desirable for lesion detection and characterization in primary staging, therapy monitoring, and recurrence detection. Given the rapid developments in machine learning (ML) and deep learning (DL) methods, the role of AI will have significant impact on the imaging workflow and will eventually improve clinical decision making and outcomes.</p><p><strong>Methods and results: </strong> The first part of this narrative review discusses current research with an introduction to artificial intelligence in oncological hybrid imaging and key concepts in data science. The second part reviews relevant examples with a focus on applications in oncology as well as discussion of challenges and current limitations.</p><p><strong>Conclusion: </strong> AI applications have the potential to leverage the diagnostic data stream with high efficiency and depth to facilitate automated lesion detection, characterization, and therapy monitoring to ultimately improve quality and efficiency throughout the medical imaging workflow. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based therapy guidance in oncology. However, significant challenges remain regarding application development, benchmarking, and clinical implementation.</p><p><strong>Key points: </strong>  · Hybrid imaging generates a large amount of multimodality medical imaging data with high complexity and depth.. · Advanced tools are required to enable fast and cost-efficient processing along the whole radiology value chain.. · AI applications promise to facilitate the assessment of oncological disease in hybrid imaging with high quality and efficiency for lesion detection, characterization, and response assessment. The goal is to generate reproducible, structured, quantitative diagnostic data for evidence-based oncological therapy guidance.. · Selected applications in three oncological entities (lung, prostate, and neuroendocrine tumors) demonstrate how AI algorithms may impact imaging-based tasks in hybrid imaging and potentially guide clinical decision making..</p>","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"62 5","pages":"296-305"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41166719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Analyzing the potential of radiomics features and radiomics signature from pretherapeutic PSMA-PET-CT scans and clinical data for prediction of overall survival when treated with Lu[177]-PSMA 分析治疗前PSMA-PET-CT扫描和临床数据的放射组学特征和放射组学特征的潜力,以预测Lu[177]-PSMA治疗时的总生存期
Pub Date : 2021-04-01 DOI: 10.1055/S-0041-1726854
S. Moazemi, A. Erle, M. Essler, R. Bundschuh
{"title":"Analyzing the potential of radiomics features and radiomics signature from pretherapeutic PSMA-PET-CT scans and clinical data for prediction of overall survival when treated with Lu[177]-PSMA","authors":"S. Moazemi, A. Erle, M. Essler, R. Bundschuh","doi":"10.1055/S-0041-1726854","DOIUrl":"https://doi.org/10.1055/S-0041-1726854","url":null,"abstract":"","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"23 1","pages":"181"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73749702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Simultane Ganzkörper-PET/MRT bei pädiatrischen Patienten mit Hodgkin-Lymphom – eine evidenzbasierte Empfehlung für die MRT-Sequenzauswahl 由霍奇金淋巴瘤的儿科患者为根据证据进行了筛查
Pub Date : 2021-04-01 DOI: 10.1055/S-0041-1726759
T. Georgi, D. Stoevesandt, L. Kurch, J. Bartelt, D. Hasenclever, D. Körholz, C. Mauz-Körholz, O. Sabri, R. Kluge
{"title":"Simultane Ganzkörper-PET/MRT bei pädiatrischen Patienten mit Hodgkin-Lymphom – eine evidenzbasierte Empfehlung für die MRT-Sequenzauswahl","authors":"T. Georgi, D. Stoevesandt, L. Kurch, J. Bartelt, D. Hasenclever, D. Körholz, C. Mauz-Körholz, O. Sabri, R. Kluge","doi":"10.1055/S-0041-1726759","DOIUrl":"https://doi.org/10.1055/S-0041-1726759","url":null,"abstract":"","PeriodicalId":94161,"journal":{"name":"Nuklearmedizin. Nuclear medicine","volume":"13 1","pages":"153 - 154"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72751096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Nuklearmedizin. Nuclear medicine
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1