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}
Pub Date : 2025-03-06DOI: 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}
Pub Date : 2025-03-01DOI: 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}
Pub Date : 2025-03-01DOI: 10.1053/j.semnuclmed.2025.03.001
M Michael Sathekge MD, PhD, Kirsten Bouchelouche MD, DMSc
{"title":"Letter From the Editors","authors":"M Michael Sathekge MD, PhD, Kirsten Bouchelouche MD, DMSc","doi":"10.1053/j.semnuclmed.2025.03.001","DOIUrl":"10.1053/j.semnuclmed.2025.03.001","url":null,"abstract":"","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 2","pages":"Pages 153-155"},"PeriodicalIF":4.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586783","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}
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.
{"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}
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