Pub Date : 2025-05-26DOI: 10.1053/j.semnuclmed.2025.05.002
Dirk Bender
The strength of Nuclear Medicine imaging are the compounds to be used as radioactive probes. Unfortunately there are many constraints both in relation to physiological parameters such as metabolism as well to compound related properties like lipophilicity or chemical stability. Due these constraints, many, otherwise promising, compounds could not be used in Nuclear Medicine imaging. Within this review a brief summary is given regarding possible limitations for imaging probes and approaches or techniques to overcome the constraints. Even so the review focuses on imaging with central active compounds, many of these problems appear likewise when targeting peripheral organs. Besides the established approaches to overcome limitations some new, so far not explored, directions are discussed. Finally, a potential new tool in imaging will be presented, a trojan horse approach for transportation of radioligands. Here, like in conventional drug development, lipid nanoparticles may have potential to be used as carrier systems in Nuclear Medicine as well. This, so far not explored, concept is briefly presented.
{"title":"New Radiopharmaceutical Tools in Imaging","authors":"Dirk Bender","doi":"10.1053/j.semnuclmed.2025.05.002","DOIUrl":"10.1053/j.semnuclmed.2025.05.002","url":null,"abstract":"<div><div>The strength of Nuclear Medicine imaging are the compounds to be used as radioactive probes. Unfortunately there are many constraints both in relation to physiological parameters such as metabolism as well to compound related properties like lipophilicity or chemical stability. Due these constraints, many, otherwise promising, compounds could not be used in Nuclear Medicine imaging. Within this review a brief summary is given regarding possible limitations for imaging probes and approaches or techniques to overcome the constraints. Even so the review focuses on imaging with central active compounds, many of these problems appear likewise when targeting peripheral organs. Besides the established approaches to overcome limitations some new, so far not explored, directions are discussed. Finally, a potential new tool in imaging will be presented, a trojan horse approach for transportation of radioligands. Here, like in conventional drug development, lipid nanoparticles may have potential to be used as carrier systems in Nuclear Medicine as well. This, so far not explored, concept is briefly presented.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 5","pages":"Pages 789-794"},"PeriodicalIF":5.9,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144161566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-21DOI: 10.1053/j.semnuclmed.2025.04.006
Zhaoguo Lin , Pawel Rasinski , Ted Nilsson , Maria Holstensson , Yangmeihui Song , August Blomgren , Warissara Jutidamrongphan , Kalyani Pandya , Jimin Hong , Axel Rominger , Kuangyu Shi , Rimma Axelsson , Xiaoli Lan , Robert Seifert
Fibroblast activation protein (FAP) is a type II transmembrane serine protease that is highly expressed in cancer-associated fibroblasts (CAFs) but absent in quiescent fibroblasts. Its overexpression is associated with poor prognosis in various cancers and contributes to treatment resistance. In recent years, radiolabeled FAP inhibitors (FAPI) for PET imaging have shown promising clinical value across a range of cancers. Gastrointestinal (GI) malignancies, which often exhibit a desmoplastic reaction with a high density of FAP-expressing CAFs, are particularly well-suited for FAPI PET. Given the limitations of [18F]FDG PET in GI cancers, such as low sensitivity in certain histological subtypes and high physiological background uptake, FAPI PET is expected to serve as a complementary method, potentially enhancing both diagnostic accuracy and treatment guidance. This review provides a comprehensive comparison of the clinical applications of FAPI PET and [18F]FDG PET in various GI cancers, including their value in diagnosis, staging, and treatment guidance. Additionally, this review summarizes studies on the expanding role of FAPI PET, including its use in assessing treatment response and predicting prognosis, aiming to provide insights into its potential contribution to the improved management of GI malignancies.
{"title":"FAPI PET Versus FDG PET/CT in Gastrointestinal Cancers: An Overview","authors":"Zhaoguo Lin , Pawel Rasinski , Ted Nilsson , Maria Holstensson , Yangmeihui Song , August Blomgren , Warissara Jutidamrongphan , Kalyani Pandya , Jimin Hong , Axel Rominger , Kuangyu Shi , Rimma Axelsson , Xiaoli Lan , Robert Seifert","doi":"10.1053/j.semnuclmed.2025.04.006","DOIUrl":"10.1053/j.semnuclmed.2025.04.006","url":null,"abstract":"<div><div>Fibroblast activation protein (FAP) is a type II transmembrane serine protease that is highly expressed in cancer-associated fibroblasts (CAFs) but absent in quiescent fibroblasts. Its overexpression is associated with poor prognosis in various cancers and contributes to treatment resistance. In recent years, radiolabeled FAP inhibitors (FAPI) for PET imaging have shown promising clinical value across a range of cancers. Gastrointestinal (GI) malignancies, which often exhibit a desmoplastic reaction with a high density of FAP-expressing CAFs, are particularly well-suited for FAPI PET. Given the limitations of [<sup>18</sup>F]FDG PET in GI cancers, such as low sensitivity in certain histological subtypes and high physiological background uptake, FAPI PET is expected to serve as a complementary method, potentially enhancing both diagnostic accuracy and treatment guidance. This review provides a comprehensive comparison of the clinical applications of FAPI PET and [<sup>18</sup>F]FDG PET in various GI cancers, including their value in diagnosis, staging, and treatment guidance. Additionally, this review summarizes studies on the expanding role of FAPI PET, including its use in assessing treatment response and predicting prognosis, aiming to provide insights into its potential contribution to the improved management of GI malignancies.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 5","pages":"Pages 710-723"},"PeriodicalIF":5.9,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144120739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-09DOI: 10.1053/j.semnuclmed.2025.04.005
Martina Di Franco , Giuseppe Lamberti , Davide Campana , Valentina Ambrosini
Assessing treatment response in neuroendocrine tumors (NET) remains a significant challenge due to their typically indolent growth and heterogenity, the frequent occurrence of disease stabilization rather than tumor shrinkage after therapy, and the inherent limitations of conventional imaging criteria. While molecular imaging—primarily somatostatin receptor (SST) PET/CT—has improved lesion detection, the absence of standardized response criteria limits its clinical utility and prevents its use as full replacement of conventional imaging. Emerging strategies, including revised thresholds for dimensional changes, criteria evaluating different features, such as lesions’ density and functional tumor volumes, offer potential improvements in response evaluation but require further validation for routine clinical implementation. This review examines the current challenges in assessing NET treatment response, evaluates the strengths and limitations of available imaging modalities, and discusses emerging approaches and future directions for optimizing therapeutic monitoring in the heterogeneous panorama of NET.
{"title":"Molecular Imaging for Response Assessment of Neuroendocrine Tumors (NET)","authors":"Martina Di Franco , Giuseppe Lamberti , Davide Campana , Valentina Ambrosini","doi":"10.1053/j.semnuclmed.2025.04.005","DOIUrl":"10.1053/j.semnuclmed.2025.04.005","url":null,"abstract":"<div><div>Assessing treatment response in neuroendocrine tumors (NET) remains a significant challenge due to their typically indolent growth and heterogenity, the frequent occurrence of disease stabilization rather than tumor shrinkage after therapy, and the inherent limitations of conventional imaging criteria. While molecular imaging—primarily somatostatin receptor (SST) PET/CT—has improved lesion detection, the absence of standardized response criteria limits its clinical utility and prevents its use as full replacement of conventional imaging. Emerging strategies, including revised thresholds for dimensional changes, criteria evaluating different features, such as lesions’ density and functional tumor volumes, offer potential improvements in response evaluation but require further validation for routine clinical implementation. This review examines the current challenges in assessing NET treatment response, evaluates the strengths and limitations of available imaging modalities, and discusses emerging approaches and future directions for optimizing therapeutic monitoring in the heterogeneous panorama of NET.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 5","pages":"Pages 754-765"},"PeriodicalIF":5.9,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144051835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-06DOI: 10.1053/j.semnuclmed.2025.04.004
Anita Brink , Diana Paez , Enrique Estrada Lobato , Roberto C. Delgado Bolton , Peter Knoll , Aruna Korde , Adriana K. Calapaquí Terán , Mohamad Haidar , Francesco Giammarile
Nuclear medicine is rapidly evolving with new molecular imaging targets and advanced computational tools that promise to enhance diagnostic precision and personalized therapy. Recent years have seen a surge in novel PET and SPECT tracers, such as those targeting prostate-specific membrane antigen (PSMA) in prostate cancer, fibroblast activation protein (FAP) in tumor stroma, and tau protein in neurodegenerative disease. These tracers enable more specific visualization of disease processes compared to traditional agents, fitting into a broader shift toward precision imaging in oncology and neurology. In parallel, artificial intelligence (AI) and machine learning techniques are being integrated into tracer development and image analysis. AI-driven methods can accelerate radiopharmaceutical discovery, optimize pharmacokinetic properties, and assist in interpreting complex imaging datasets. This editorial provides an expanded overview of emerging imaging targets and techniques, including theranostic applications that pair diagnosis with radionuclide therapy, and examines how AI is augmenting nuclear medicine. We discuss the implications of these advancements within the field’s historical trajectory and address the regulatory, manufacturing, and clinical challenges that must be navigated. Innovations in molecular targeting and AI are poised to transform nuclear medicine practice, enabling more personalized diagnostics and radiotheranostic strategies in the era of precision healthcare.
{"title":"New Targets for Imaging in Nuclear Medicine","authors":"Anita Brink , Diana Paez , Enrique Estrada Lobato , Roberto C. Delgado Bolton , Peter Knoll , Aruna Korde , Adriana K. Calapaquí Terán , Mohamad Haidar , Francesco Giammarile","doi":"10.1053/j.semnuclmed.2025.04.004","DOIUrl":"10.1053/j.semnuclmed.2025.04.004","url":null,"abstract":"<div><div>Nuclear medicine is rapidly evolving with new molecular imaging targets and advanced computational tools that promise to enhance diagnostic precision and personalized therapy. Recent years have seen a surge in novel PET and SPECT tracers, such as those targeting prostate-specific membrane antigen (PSMA) in prostate cancer, fibroblast activation protein (FAP) in tumor stroma, and tau protein in neurodegenerative disease. These tracers enable more specific visualization of disease processes compared to traditional agents, fitting into a broader shift toward precision imaging in oncology and neurology. In parallel, artificial intelligence (AI) and machine learning techniques are being integrated into tracer development and image analysis. AI-driven methods can accelerate radiopharmaceutical discovery, optimize pharmacokinetic properties, and assist in interpreting complex imaging datasets. This editorial provides an expanded overview of emerging imaging targets and techniques, including theranostic applications that pair diagnosis with radionuclide therapy, and examines how AI is augmenting nuclear medicine. We discuss the implications of these advancements within the field’s historical trajectory and address the regulatory, manufacturing, and clinical challenges that must be navigated. Innovations in molecular targeting and AI are poised to transform nuclear medicine practice, enabling more personalized diagnostics and radiotheranostic strategies in the era of precision healthcare.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 5","pages":"Pages 869-884"},"PeriodicalIF":5.9,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144013197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-22DOI: 10.1053/j.semnuclmed.2025.03.006
Simon K.B. Spohn , Anca-L. Grosu
Radiation therapy (RT) plays a critical role in managing prostate cancer (PCa) in various stages, from localized disease to metastatic settings. Recent advancements in molecular imaging using prostate-specific membrane antigen positron emission tomography (PSMA-PET) have revolutionized PCa diagnosis, significantly enhancing local, lymph node and distant stagingto conventional imaging methods. This narrative review explores the impact of PSMA-PET on RT planning, highlighting its diagnostic performance and implications for RT treatment management. PSMA-PET has shown superior sensitivity in detecting metastatic lesions and intraprostatic tumor volumes, leading to more accurate disease staging and treatment planning. The HypoFocal trials investigate the safety and efficacy of implementing PSMA-PET into definitive RT regimens. Additionalongoing clinical trials are investigating the potential of PSMA-PET-based RT recurrent and oligometastatic PCa. Despite these advancements, further research is necessary to optimize patient selection and define the best management strategies for PSMA-PET-guided RT.
{"title":"Impact of PSMA PET on Radiation Oncology Planning","authors":"Simon K.B. Spohn , Anca-L. Grosu","doi":"10.1053/j.semnuclmed.2025.03.006","DOIUrl":"10.1053/j.semnuclmed.2025.03.006","url":null,"abstract":"<div><div>Radiation therapy (RT) plays a critical role in managing prostate cancer (PCa) in various stages, from localized disease to metastatic settings. Recent advancements in molecular imaging using prostate-specific membrane antigen positron emission tomography (PSMA-PET) have revolutionized PCa diagnosis, significantly enhancing local, lymph node and distant stagingto conventional imaging methods. This narrative review explores the impact of PSMA-PET on RT planning, highlighting its diagnostic performance and implications for RT treatment management. PSMA-PET has shown superior sensitivity in detecting metastatic lesions and intraprostatic tumor volumes, leading to more accurate disease staging and treatment planning. The HypoFocal trials investigate the safety and efficacy of implementing PSMA-PET into definitive RT regimens. Additionalongoing clinical trials are investigating the potential of PSMA-PET-based RT recurrent and oligometastatic PCa. Despite these advancements, further research is necessary to optimize patient selection and define the best management strategies for PSMA-PET-guided RT.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 5","pages":"Pages 664-671"},"PeriodicalIF":5.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-15DOI: 10.1053/j.semnuclmed.2025.04.001
Kirsten Bouchelouche MD, DMSc, M. Michael Sathekge MD, PhD
{"title":"Letter from the Editors","authors":"Kirsten Bouchelouche MD, DMSc, M. Michael Sathekge MD, PhD","doi":"10.1053/j.semnuclmed.2025.04.001","DOIUrl":"10.1053/j.semnuclmed.2025.04.001","url":null,"abstract":"","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 3","pages":"Pages 291-293"},"PeriodicalIF":4.6,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
SPECT is a widely used imaging modality in nuclear medicine which provides essential functional insights into cardiovascular, neurological, and oncological diseases. However, SPECT imaging suffers from limited quantitative accuracy due to low spatial resolution and high noise levels, posing significant challenges for precise diagnosis, disease monitoring, and treatment planning. Recent advances in artificial intelligence (AI), in particular deep learning-based techniques such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformers, have led to substantial improvements in SPECT image reconstruction, enhancement, attenuation correction, segmentation, disease classification, and multimodal fusion. These AI approaches have enabled more accurate extraction of functional and anatomical information, improved quantitative analysis, and facilitated the integration of SPECT with other imaging modalities to enhance clinical decision-making. This review provides a comprehensive overview of AI-driven developments in SPECT imaging, highlighting progress in both supervised and unsupervised learning approaches, innovations in image synthesis and cross-modality learning, and the potential of self-supervised and contrastive learning strategies for improving model robustness. Additionally, we discuss key challenges, including data heterogeneity, model interpretability, and computational complexity, which continue to limit the clinical adoption of AI methods. The need for standardized evaluation metrics, large-scale multimodal datasets, and clinically validated AI models remains a crucial factor in ensuring the reliability and generalizability of AI approaches. Future research directions include the exploration of foundation models and large language models for knowledge-driven image analysis, as well as the development of more adaptive and personalized AI frameworks tailored for nuclear imaging applications.
{"title":"AI in SPECT Imaging: Opportunities and Challenges","authors":"Fan Yang, Bowen Lei, Ziyuan Zhou, Tzu-An Song, Vibha Balaji, Joyita Dutta","doi":"10.1053/j.semnuclmed.2025.03.005","DOIUrl":"10.1053/j.semnuclmed.2025.03.005","url":null,"abstract":"<div><div>SPECT is a widely used imaging modality in nuclear medicine which provides essential functional insights into cardiovascular, neurological, and oncological diseases. However, SPECT imaging suffers from limited quantitative accuracy due to low spatial resolution and high noise levels, posing significant challenges for precise diagnosis, disease monitoring, and treatment planning. Recent advances in artificial intelligence (AI), in particular deep learning-based techniques such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformers, have led to substantial improvements in SPECT image reconstruction, enhancement, attenuation correction, segmentation, disease classification, and multimodal fusion. These AI approaches have enabled more accurate extraction of functional and anatomical information, improved quantitative analysis, and facilitated the integration of SPECT with other imaging modalities to enhance clinical decision-making. This review provides a comprehensive overview of AI-driven developments in SPECT imaging, highlighting progress in both supervised and unsupervised learning approaches, innovations in image synthesis and cross-modality learning, and the potential of self-supervised and contrastive learning strategies for improving model robustness. Additionally, we discuss key challenges, including data heterogeneity, model interpretability, and computational complexity, which continue to limit the clinical adoption of AI methods. The need for standardized evaluation metrics, large-scale multimodal datasets, and clinically validated AI models remains a crucial factor in ensuring the reliability and generalizability of AI approaches. Future research directions include the exploration of foundation models and large language models for knowledge-driven image analysis, as well as the development of more adaptive and personalized AI frameworks tailored for nuclear imaging applications.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 3","pages":"Pages 294-312"},"PeriodicalIF":4.6,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-29DOI: 10.1053/j.semnuclmed.2025.03.003
Alireza Safarian MD , Seyed Ali Mirshahvalad MD, MPH, FEBNM, FANMB , Abolfazl Farbod MD , Hadi Nasrollahi MSc , Christian Pirich MD, PhD , Mohsen Beheshti MD, FEBNM, FASNC
The advent of sophisticated image analysis techniques has facilitated the extraction of increasingly complex data, such as radiomic features, from various imaging modalities, including [18F]FDG PET/CT, a well-established cornerstone of oncological imaging. Furthermore, the use of artificial intelligence (AI) algorithms has shown considerable promise in enhancing the interpretation of these quantitative parameters. Additionally, AI-driven models enable the integration of parameters from multiple imaging modalities along with clinical data, facilitating the development of comprehensive models with significant clinical impact.
However, challenges remain regarding standardization and validation of the AI-powered models, as well as their implementation in real-world clinical practice. The variability in imaging acquisition protocols, segmentation methods, and feature extraction approaches across different institutions necessitates robust harmonization efforts to ensure reproducibility and clinical utility. Moreover, the successful translation of AI models into clinical practice requires prospective validation in large cohorts, as well as seamless integration into existing workflows to assess their ability to enhance clinicians’ performance.
This review aims to provide an overview of the literature and highlight three key applications: diagnostic impact, prediction of treatment response, and long-term patient prognostication. In the first part, we will focus on head and neck, lung, breast, gastroesophageal, colorectal, and gynecological malignancies.
{"title":"Artificial intelligence for tumor [18F]FDG-PET imaging: Advancement and future trends—part I","authors":"Alireza Safarian MD , Seyed Ali Mirshahvalad MD, MPH, FEBNM, FANMB , Abolfazl Farbod MD , Hadi Nasrollahi MSc , Christian Pirich MD, PhD , Mohsen Beheshti MD, FEBNM, FASNC","doi":"10.1053/j.semnuclmed.2025.03.003","DOIUrl":"10.1053/j.semnuclmed.2025.03.003","url":null,"abstract":"<div><div>The advent of sophisticated image analysis techniques has facilitated the extraction of increasingly complex data, such as radiomic features, from various imaging modalities, including [<sup>18</sup>F]FDG PET/CT, a well-established cornerstone of oncological imaging. Furthermore, the use of artificial intelligence (AI) algorithms has shown considerable promise in enhancing the interpretation of these quantitative parameters. Additionally, AI-driven models enable the integration of parameters from multiple imaging modalities along with clinical data, facilitating the development of comprehensive models with significant clinical impact.</div><div>However, challenges remain regarding standardization and validation of the AI-powered models, as well as their implementation in real-world clinical practice. The variability in imaging acquisition protocols, segmentation methods, and feature extraction approaches across different institutions necessitates robust harmonization efforts to ensure reproducibility and clinical utility. Moreover, the successful translation of AI models into clinical practice requires prospective validation in large cohorts, as well as seamless integration into existing workflows to assess their ability to enhance clinicians’ performance.</div><div>This review aims to provide an overview of the literature and highlight three key applications: diagnostic impact, prediction of treatment response, and long-term patient prognostication. In the first part, we will focus on head and neck, lung, breast, gastroesophageal, colorectal, and gynecological malignancies.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 3","pages":"Pages 328-344"},"PeriodicalIF":4.6,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-22DOI: 10.1053/j.semnuclmed.2025.02.012
Margarita Kirienko , Lara Cavinato , Martina Sollini
Infectious and inflammatory diseases represent a global challenge. Delayed diagnosis and treatment lead to death, disabilities and impairment of the quality of life. The detection of low-grade inflammation and occult infections remains challenging. Nuclear medicine techniques are well established in the assessment of the severity and extent of the disease. However, high-level expertise is required to process and interpret the images. Additionally, the workflows are frequently time consuming. Artificial intelligence (AI)-based techniques can be efficiently applied in this setting. We reviewed the literature to assess the state of the application of AI in nuclear medicine imaging in infectious and inflammatory diseases. We included 22 studies, which applied AI-based methods for any of the steps of their workflow. In this review we report and critically discuss the state-of-the-art knowledge on the application of AI models in Infection and Inflammation nuclear medicine imaging.
{"title":"Infection and Inflammation in Nuclear Medicine Imaging: The Role of Artificial Intelligence","authors":"Margarita Kirienko , Lara Cavinato , Martina Sollini","doi":"10.1053/j.semnuclmed.2025.02.012","DOIUrl":"10.1053/j.semnuclmed.2025.02.012","url":null,"abstract":"<div><div>Infectious and inflammatory diseases represent a global challenge. Delayed diagnosis and treatment lead to death, disabilities and impairment of the quality of life. The detection of low-grade inflammation and occult infections remains challenging. Nuclear medicine techniques are well established in the assessment of the severity and extent of the disease. However, high-level expertise is required to process and interpret the images. Additionally, the workflows are frequently time consuming. Artificial intelligence (AI)-based techniques can be efficiently applied in this setting. We reviewed the literature to assess the state of the application of AI in nuclear medicine imaging in infectious and inflammatory diseases. We included 22 studies, which applied AI-based methods for any of the steps of their workflow. In this review we report and critically discuss the state-of-the-art knowledge on the application of AI models in Infection and Inflammation nuclear medicine imaging.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 3","pages":"Pages 396-405"},"PeriodicalIF":4.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143693178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-11DOI: 10.1053/j.semnuclmed.2025.02.007
James Cairns BMBS, MSc, FRCR , Russell Frood MBChB, PhD, FRCR , Chirag Patel MBBS, FRCR , Andrew Scarsbrook BMBS, PhD, FRCR
Malignant lymphomas encompass a range of malignancies with incidence rising globally, particularly with age. In younger populations, Hodgkin and Burkitt lymphomas predominate, while older populations more commonly experience subtypes such as diffuse large B-cell, follicular, marginal zone, and mantle cell lymphomas. Positron emission tomography/computed tomography (PET/CT) using [18F] fluorodeoxyglucose (FDG) is the gold standard for staging, treatment response assessment, and prognostication in lymphoma. However, interpretation of PET/CT is complex, time-consuming, and reliant on expert imaging specialists, exacerbating challenges associated with workforce shortages worldwide. Artificial intelligence (AI) offers transformative potential across multiple aspects of PET/CT imaging in this setting.
AI applications in appointment planning have demonstrated utility in reducing nonattendance rates and improving departmental efficiency. Advanced reconstruction techniques leveraging convolutional neural networks (CNNs) enable reduced injected activities of radiopharmaceutical and patient dose whilst maintaining diagnostic accuracy, particularly benefiting younger patients requiring multiple scans. Automated segmentation tools, predominantly using 3D U-Net architectures, have improved quantification of metrics such as total metabolic tumour volume (TMTV) and total lesion glycolysis (TLG), facilitating prognostication and treatment stratification. Despite these advancements, challenges remain, including variability in segmentation performance, impact on Deauville Score interpretation, and standardization of TMTV/TLG measurements. Emerging large language models (LLMs) also show promise in enhancing PET/CT reporting, converting free-text reports into structured formats, and improving patient communication.
Further research is required to address limitations such as AI-induced errors, physiological uptake differentiation, and the integration of AI models into clinical workflows. With robust validation and harmonization, AI integration could significantly enhance lymphoma care, improving diagnostic precision, workflow efficiency, and patient outcomes.
{"title":"The Role of AI in Lymphoma: An Update","authors":"James Cairns BMBS, MSc, FRCR , Russell Frood MBChB, PhD, FRCR , Chirag Patel MBBS, FRCR , Andrew Scarsbrook BMBS, PhD, FRCR","doi":"10.1053/j.semnuclmed.2025.02.007","DOIUrl":"10.1053/j.semnuclmed.2025.02.007","url":null,"abstract":"<div><div>Malignant lymphomas encompass a range of malignancies with incidence rising globally, particularly with age. In younger populations, Hodgkin and Burkitt lymphomas predominate, while older populations more commonly experience subtypes such as diffuse large B-cell, follicular, marginal zone, and mantle cell lymphomas. Positron emission tomography/computed tomography (PET/CT) using [<sup>18</sup>F] fluorodeoxyglucose (FDG) is the gold standard for staging, treatment response assessment, and prognostication in lymphoma. However, interpretation of PET/CT is complex, time-consuming, and reliant on expert imaging specialists, exacerbating challenges associated with workforce shortages worldwide. Artificial intelligence (AI) offers transformative potential across multiple aspects of PET/CT imaging in this setting.</div><div>AI applications in appointment planning have demonstrated utility in reducing nonattendance rates and improving departmental efficiency. Advanced reconstruction techniques leveraging convolutional neural networks (CNNs) enable reduced injected activities of radiopharmaceutical and patient dose whilst maintaining diagnostic accuracy, particularly benefiting younger patients requiring multiple scans. Automated segmentation tools, predominantly using 3D U-Net architectures, have improved quantification of metrics such as total metabolic tumour volume (TMTV) and total lesion glycolysis (TLG), facilitating prognostication and treatment stratification. Despite these advancements, challenges remain, including variability in segmentation performance, impact on Deauville Score interpretation, and standardization of TMTV/TLG measurements. Emerging large language models (LLMs) also show promise in enhancing PET/CT reporting, converting free-text reports into structured formats, and improving patient communication.</div><div>Further research is required to address limitations such as AI-induced errors, physiological uptake differentiation, and the integration of AI models into clinical workflows. With robust validation and harmonization, AI integration could significantly enhance lymphoma care, improving diagnostic precision, workflow efficiency, and patient outcomes.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 3","pages":"Pages 377-386"},"PeriodicalIF":4.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143606405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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