Seminars in nuclear medicine最新文献

英文中文

Letter from the Editors 编辑来信

IF 4.6 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Seminars in nuclear medicine

Pub Date : 2025-04-15 DOI: 10.1053/j.semnuclmed.2025.04.001

Kirsten Bouchelouche MD, DMSc, M. Michael Sathekge MD, PhD

引用次数: 0

AI in SPECT Imaging: Opportunities and Challenges SPECT 成像中的人工智能：机遇与挑战。

IF 4.6 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Seminars in nuclear medicine

Pub Date : 2025-04-03 DOI: 10.1053/j.semnuclmed.2025.03.005

Fan Yang, Bowen Lei, Ziyuan Zhou, Tzu-An Song, Vibha Balaji, Joyita Dutta

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}

引用次数: 0

Artificial intelligence for tumor [18F]FDG-PET imaging: Advancement and future trends—part I

IF 4.6 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Seminars in nuclear medicine

Pub Date : 2025-03-29 DOI: 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}

引用次数: 0

Infection and Inflammation in Nuclear Medicine Imaging: The Role of Artificial Intelligence

IF 4.6 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Seminars in nuclear medicine

Pub Date : 2025-03-22 DOI: 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.

引用次数: 0

The Role of AI in Lymphoma: An Update

IF 4.6 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Seminars in nuclear medicine

Pub Date : 2025-03-11 DOI: 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}

引用次数: 0

Optimizing CT Imaging Parameters: Implications for Diagnostic Accuracy in Nuclear Medicine

IF 4.6 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Seminars in nuclear medicine

Pub Date : 2025-03-06 DOI: 10.1053/j.semnuclmed.2025.02.008

Anders F.S. Mikkelsen , Jesper Thygesen , Joan Fledelius

X-ray computed tomography (CT) is an important companion modality in molecular imaging, offering attenuation correction (AC) of single-photon emission computed tomography (SPECT) - and positron emission tomography (PET)-data, topographic information in scans as well as changes in morphology in serial follow-up studies. Image quality plays a critical role in delivering an acceptable diagnosis and in medical treatment planning. Variability in protocols can present a considerable challenge in achieving consistent image quality within departments. The differences in CT scanning protocol metrics established by various manufacturers and across different generations of scanners can contribute to this issue, making the standardization of image quality a complex task. This review aims to present relevant literature herein and provide an introduction of the CT imaging parameters, including acquisition factors, reconstruction algorithms, and relevant image quality metrics, and discuss possible ways to implement a robust CT protocol review process in a nuclear medicine department. We also evaluate the potential of iterative reconstruction (IR) and deep learning (DL) for enhancing image quality and minimizing exposure doses. This article points to the need for periodic audit of image quality to guarantee that CT protocols are suited for the intended purpose. Through the creation of local diagnostic reference levels and monitoring performance through protocol management, physicians may aim at delivering high quality imaging services consistently adhering to the principles of ALARA and reduction of dose for both patients and workers.

{"title":"Optimizing CT Imaging Parameters: Implications for Diagnostic Accuracy in Nuclear Medicine","authors":"Anders F.S. Mikkelsen , Jesper Thygesen , Joan Fledelius","doi":"10.1053/j.semnuclmed.2025.02.008","DOIUrl":"10.1053/j.semnuclmed.2025.02.008","url":null,"abstract":"<div><div>X-ray computed tomography (CT) is an important companion modality in molecular imaging, offering attenuation correction (AC) of single-photon emission computed tomography (SPECT) - and positron emission tomography (PET)-data, topographic information in scans as well as changes in morphology in serial follow-up studies. Image quality plays a critical role in delivering an acceptable diagnosis and in medical treatment planning. Variability in protocols can present a considerable challenge in achieving consistent image quality within departments. The differences in CT scanning protocol metrics established by various manufacturers and across different generations of scanners can contribute to this issue, making the standardization of image quality a complex task. This review aims to present relevant literature herein and provide an introduction of the CT imaging parameters, including acquisition factors, reconstruction algorithms, and relevant image quality metrics, and discuss possible ways to implement a robust CT protocol review process in a nuclear medicine department. We also evaluate the potential of iterative reconstruction (IR) and deep learning (DL) for enhancing image quality and minimizing exposure doses. This article points to the need for periodic audit of image quality to guarantee that CT protocols are suited for the intended purpose. Through the creation of local diagnostic reference levels and monitoring performance through protocol management, physicians may aim at delivering high quality imaging services consistently adhering to the principles of ALARA and reduction of dose for both patients and workers.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 3","pages":"Pages 450-459"},"PeriodicalIF":4.6,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586784","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}

引用次数: 0

Current Status of Staging and Restaging Malignant Pleural Mesothelioma

IF 4.6 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Seminars in nuclear medicine

Pub Date : 2025-03-01 DOI: 10.1053/j.semnuclmed.2025.01.003

Egesta Lopci MD, PhD

Malignant pleural mesothelioma (MPM) is the most frequent aggressive tumor affecting the pleura, accounting for over 38,000 deaths worldwide. It originates from the mesothelial cells and is mostly associated to asbestos exposure. Depending on the extent of the disease, the management of MPM varies from surgical intervention to a combination of systemic chemotherapy, immunotherapy, and radiation therapy. Major International scientific societies provide continuous updates on proper management of the disease, including recommendations on the optimal imaging algorithms, which are crucial for determining effective treatment options and optimizing clinical outcomes. However, despite the continuous efforts to improve patients’ prognosis, median overall survival remains poor, ranging from 8 to 14 months. And even in case of initial response to treatment, local or distant recurrences represent almost a certainty, requiring appropriate imaging for the assessment of tumor sites. The aim of the present article is to illustrate the current status of imaging for staging and restaging of MPM, not forgetting most recent novelties in the diagnostic work-up of the disease.

{"title":"Current Status of Staging and Restaging Malignant Pleural Mesothelioma","authors":"Egesta Lopci MD, PhD","doi":"10.1053/j.semnuclmed.2025.01.003","DOIUrl":"10.1053/j.semnuclmed.2025.01.003","url":null,"abstract":"<div><div>Malignant pleural mesothelioma (MPM) is the most frequent aggressive tumor affecting the pleura, accounting for over 38,000 deaths worldwide. It originates from the mesothelial cells and is mostly associated to asbestos exposure. Depending on the extent of the disease, the management of MPM varies from surgical intervention to a combination of systemic chemotherapy, immunotherapy, and radiation therapy. Major International scientific societies provide continuous updates on proper management of the disease, including recommendations on the optimal imaging algorithms, which are crucial for determining effective treatment options and optimizing clinical outcomes. However, despite the continuous efforts to improve patients’ prognosis, median overall survival remains poor, ranging from 8 to 14 months. And even in case of initial response to treatment, local or distant recurrences represent almost a certainty, requiring appropriate imaging for the assessment of tumor sites. The aim of the present article is to illustrate the current status of imaging for staging and restaging of MPM, not forgetting most recent novelties in the diagnostic work-up of the disease.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 2","pages":"Pages 240-251"},"PeriodicalIF":4.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400012","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}

引用次数: 0

The Role of [18F]FDG PET/CT in Monitoring of Therapy Response in Lung Cancer

IF 4.6 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Seminars in nuclear medicine

Pub Date : 2025-03-01 DOI: 10.1053/j.semnuclmed.2025.02.002

Akinwale Ayeni MBChB, MMed , Osayande Evbuomwan MBBS, PhD , Mboyo-Di-Tamba Willy Vangu MD, PhD

Lung cancer remains a leading cause of cancer deaths worldwide, with an all stage 5-year relative survival rate of less than 30%. Multiple treatment strategies are available and continue to evolve, with therapy primarily tailored to the type and stage of the disease. Accurate monitoring of therapy response is crucial for optimizing treatment outcomes. PET/CT imaging with [¹⁸F]FDG has become the standard of care across various phases of lung cancer management due to its ability to assess metabolic activity. This review underscores the pivotal role of [¹⁸F]FDG PET/CT in evaluating therapy response in lung cancer, particularly in non-small cell lung cancer (NSCLC). It examines conventional response criteria and their adaptations in the era of immunotherapy, highlighting the value of integrating metabolic imaging with established criteria to improve treatment assessment and guide clinical decisions. The potential of non-[¹⁸F]FDG PET tracers targeting diverse biological pathways to provide deeper insights into tumor biology, therapy response and predictive outcomes is also explored. Additionally, the emerging role of radiomics in enhancing treatment efficacy assessment and improving patient management is briefly highlighted. Despite the challenges in the routine clinical application of various metabolic response criteria, [¹⁸F]FDG PET/CT remains a crucial tool in monitoring therapy response in lung cancer. Ongoing advancements in therapeutic strategies, radiopharmaceuticals, and imaging techniques continue to drive progress in lung cancer management, promising improved patient outcomes.

肺癌仍然是全球癌症死亡的主要原因，所有阶段的 5 年相对生存率不到 30%。目前有多种治疗策略可供选择，而且还在不断发展，主要是根据疾病的类型和分期进行治疗。准确监测治疗反应对于优化治疗效果至关重要。由于[18F]FDG PET/CT 成像能够评估代谢活动，因此已成为肺癌各期治疗的标准方法。本综述强调了[18F]FDG PET/CT 在评估肺癌，尤其是非小细胞肺癌（NSCLC）治疗反应中的关键作用。它探讨了传统的反应标准及其在免疫疗法时代的适应性，强调了将代谢成像与既定标准相结合以改进治疗评估和指导临床决策的价值。此外，还探讨了针对不同生物通路的非[18F]FDG PET 示踪剂的潜力，以便深入了解肿瘤生物学、治疗反应和预测结果。此外，还简要介绍了放射组学在加强疗效评估和改善患者管理方面的新兴作用。尽管各种代谢反应标准的常规临床应用面临挑战，但[18F]FDG PET/CT 仍是监测肺癌治疗反应的重要工具。治疗策略、放射性药物和成像技术的不断进步将继续推动肺癌治疗的进步，有望改善患者的预后。

{"title":"The Role of [18F]FDG PET/CT in Monitoring of Therapy Response in Lung Cancer","authors":"Akinwale Ayeni MBChB, MMed , Osayande Evbuomwan MBBS, PhD , Mboyo-Di-Tamba Willy Vangu MD, PhD","doi":"10.1053/j.semnuclmed.2025.02.002","DOIUrl":"10.1053/j.semnuclmed.2025.02.002","url":null,"abstract":"<div><div>Lung cancer remains a leading cause of cancer deaths worldwide, with an all stage 5-year relative survival rate of less than 30%. Multiple treatment strategies are available and continue to evolve, with therapy primarily tailored to the type and stage of the disease. Accurate monitoring of therapy response is crucial for optimizing treatment outcomes. PET/CT imaging with [<sup>18</sup>F]FDG has become the standard of care across various phases of lung cancer management due to its ability to assess metabolic activity. This review underscores the pivotal role of [<sup>18</sup>F]FDG PET/CT in evaluating therapy response in lung cancer, particularly in non-small cell lung cancer (NSCLC). It examines conventional response criteria and their adaptations in the era of immunotherapy, highlighting the value of integrating metabolic imaging with established criteria to improve treatment assessment and guide clinical decisions. The potential of non-[<sup>18</sup>F]FDG PET tracers targeting diverse biological pathways to provide deeper insights into tumor biology, therapy response and predictive outcomes is also explored. Additionally, the emerging role of radiomics in enhancing treatment efficacy assessment and improving patient management is briefly highlighted. Despite the challenges in the routine clinical application of various metabolic response criteria, [<sup>18</sup>F]FDG PET/CT remains a crucial tool in monitoring therapy response in lung cancer. Ongoing advancements in therapeutic strategies, radiopharmaceuticals, and imaging techniques continue to drive progress in lung cancer management, promising improved patient outcomes.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 2","pages":"Pages 175-189"},"PeriodicalIF":4.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143531685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Letter From the Editors

IF 4.6 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Seminars in nuclear medicine

Pub Date : 2025-03-01 DOI: 10.1053/j.semnuclmed.2025.03.001

M Michael Sathekge MD, PhD, Kirsten Bouchelouche MD, DMSc

引用次数: 0

Impact of [18F]FDG PET/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role

IF 4.6 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Seminars in nuclear medicine

Pub Date : 2025-03-01 DOI: 10.1053/j.semnuclmed.2025.02.006

Alireza Safarian MD , Seyed Ali Mirshahvalad MD, MPH, FEBNM, FANMB , Hadi Nasrollahi MSc , Theresa Jung MD , Christian Pirich MD, PhD , Hossein Arabi PhD , Mohsen Beheshti MD, FEBNM, FASNC

Lung cancer remains one of the most prevalent cancers globally and the leading cause of cancer-related deaths, accounting for nearly one-fifth of all cancer fatalities. Fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography ([¹⁸F]FDG PET/CT) plays a vital role in assessing lung cancer and managing disease progression. While traditional PET/CT imaging relies on qualitative analysis and basic quantitative parameters, radiomics offers a more advanced approach to analyzing tumor phenotypes.

Recently, radiomics has gained attention for its potential to enhance the prognostic and diagnostic capabilities of [¹⁸F]FDG PET/CT in various cancers. This review explores the expanding role of [¹⁸F]FDG PET/CT-based radiomics, particularly when integrated with artificial intelligence (AI), in managing lung cancer, especially non-small cell lung cancer (NSCLC).

We review how radiomics and AI improve diagnostics, staging, tumor subtype identification, and molecular marker detection, which influence treatment decisions. Additionally, we address challenges in clinical integration, such as imaging protocol standardization, feature reproducibility, and the need for extensive prospective studies. Ultimately, radiomics and AI hold great promise for enabling more personalized and effective lung cancer treatments, potentially transforming disease management.

{"title":"Impact of [18F]FDG PET/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role","authors":"Alireza Safarian MD , Seyed Ali Mirshahvalad MD, MPH, FEBNM, FANMB , Hadi Nasrollahi MSc , Theresa Jung MD , Christian Pirich MD, PhD , Hossein Arabi PhD , Mohsen Beheshti MD, FEBNM, FASNC","doi":"10.1053/j.semnuclmed.2025.02.006","DOIUrl":"10.1053/j.semnuclmed.2025.02.006","url":null,"abstract":"<div><div>Lung cancer remains one of the most prevalent cancers globally and the leading cause of cancer-related deaths, accounting for nearly one-fifth of all cancer fatalities. Fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography ([<sup>18</sup>F]FDG PET/CT) plays a vital role in assessing lung cancer and managing disease progression. While traditional PET/CT imaging relies on qualitative analysis and basic quantitative parameters, radiomics offers a more advanced approach to analyzing tumor phenotypes.</div><div>Recently, radiomics has gained attention for its potential to enhance the prognostic and diagnostic capabilities of [<sup>18</sup>F]FDG PET/CT in various cancers. This review explores the expanding role of [<sup>18</sup>F]FDG PET/CT-based radiomics, particularly when integrated with artificial intelligence (AI), in managing lung cancer, especially non-small cell lung cancer (NSCLC).</div><div>We review how radiomics and AI improve diagnostics, staging, tumor subtype identification, and molecular marker detection, which influence treatment decisions. Additionally, we address challenges in clinical integration, such as imaging protocol standardization, feature reproducibility, and the need for extensive prospective studies. Ultimately, radiomics and AI hold great promise for enabling more personalized and effective lung cancer treatments, potentially transforming disease management.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 2","pages":"Pages 156-166"},"PeriodicalIF":4.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143573727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Seminars in 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.

﹀