Seminars in nuclear medicine最新文献_第5页

Evolving Paradigms in Lung Cancer: Latest Trends in Diagnosis, Management, and Radiopharmaceuticals 肺癌的发展模式：诊断、管理和放射性药物的最新趋势。

IF 4.6 2区医学

Seminars in nuclear medicine Pub Date : 2025-03-01 DOI: 10.1053/j.semnuclmed.2025.02.005

Busra Cangut MD, MS, Rahman Akinlusi MD, Ava Mohseny MD, Nasrin Ghesani MD, Munir Ghesani MD

{"title":"Evolving Paradigms in Lung Cancer: Latest Trends in Diagnosis, Management, and Radiopharmaceuticals","authors":"Busra Cangut MD, MS, Rahman Akinlusi MD, Ava Mohseny MD, Nasrin Ghesani MD, Munir Ghesani MD","doi":"10.1053/j.semnuclmed.2025.02.005","DOIUrl":"10.1053/j.semnuclmed.2025.02.005","url":null,"abstract":"<div><div>Lung cancer is one of the most common and deadliest forms of cancer worldwide. Over the past two decades, significant changes have occurred in the classification of lung cancer, involving multidisciplinary input and emphasizing the growing contribution of immunohistochemistry and molecular techniques to morphology in the classification scheme. This comprehensive review will cover the background and epidemiology of lung cancer as well as advancements in its staging and management, including discussions of new surgical techniques, targeted therapies, and immunotherapy. The review will detail the role of 18F-FDG-PET-CT in lung cancer, highlighting its importance in staging, treatment response assessment, and recurrence detection. While immunotherapy has transformed lung cancer management and improved patient outcomes, it presents major challenges and opportunities for optimal assessment of treatment response in lung cancer patients using 18F-FDG-PET-CT. This review will also explore future directions, including a discussion of promising new targeted diagnostic radiopharmaceuticals for PET/CT imaging. Additionally, there will be a brief discussion of evolving and exciting treatment options for lung cancer using targeted therapeutic radiopharmaceuticals. Several case-based illustrations are included to exemplify the role of 18F-FDG-PET-CT in various clinical scenarios.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 2","pages":"Pages 264-276"},"PeriodicalIF":4.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586781","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 AI in the Evaluation of Neuroendocrine Tumors: Current State of the Art 人工智能在神经内分泌肿瘤评估中的作用：最新进展。

IF 4.6 2区医学

Seminars in nuclear medicine Pub Date : 2025-02-28 DOI: 10.1053/j.semnuclmed.2025.02.003

Felipe Lopez-Ramirez MD, Mohammad Yasrab MD, Florent Tixier PhD, Satomi Kawamoto MD, Elliot K. Fishman MD, Linda C. Chu MD

引用次数: 0

AI in Breast Cancer Imaging: An Update and Future Trends 人工智能在乳腺癌成像中的应用：最新进展和未来趋势。

IF 4.6 2区医学

Seminars in nuclear medicine Pub Date : 2025-02-25 DOI: 10.1053/j.semnuclmed.2025.01.008

Yizhou Chen MA, M. Eng. , Xiaoliang Shao MD , Kuangyu Shi PhD , Axel Rominger MD, PhD , Federico Caobelli PD, MD, FEBNM

{"title":"AI in Breast Cancer Imaging: An Update and Future Trends","authors":"Yizhou Chen MA, M. Eng. , Xiaoliang Shao MD , Kuangyu Shi PhD , Axel Rominger MD, PhD , Federico Caobelli PD, MD, FEBNM","doi":"10.1053/j.semnuclmed.2025.01.008","DOIUrl":"10.1053/j.semnuclmed.2025.01.008","url":null,"abstract":"<div><div>Breast cancer is one of the most common types of cancer affecting women worldwide. Artificial intelligence (AI) is transforming breast cancer imaging by enhancing diagnostic capabilities across multiple imaging modalities including mammography, digital breast tomosynthesis, ultrasound, magnetic resonance imaging, and nuclear medicines techniques. AI is being applied to diverse tasks such as breast lesion detection and classification, risk stratification, molecular subtyping, gene mutation status prediction, and treatment response assessment, with emerging research demonstrating performance levels comparable to or potentially exceeding those of radiologists. The large foundation models are showing remarkable potential in different breast cancer imaging tasks. Self-supervised learning gives an insight into data inherent correlation, and federated learning is an alternative way to maintain data privacy. While promising results have been obtained so far, data standardization from source, large-scale annotated multimodal datasets, and extensive prospective clinical trials are still needed to fully explore and validate deep learning's clinical utility and address the legal and ethical considerations, which will ultimately determine its widespread adoption in breast cancer care. We hereby provide a review of the most up-to-date knowledge on AI in breast cancer imaging.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 3","pages":"Pages 358-370"},"PeriodicalIF":4.6,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143516707","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

Potential of Technetium and Rhenium Theranostics. 锝和铼的治疗潜力。

IF 4.6 2区医学

Seminars in nuclear medicine Pub Date : 2025-02-24 DOI: 10.1053/j.semnuclmed.2025.01.005

Geoffrey M Currie, Eric M Rohren

{"title":"Potential of Technetium and Rhenium Theranostics.","authors":"Geoffrey M Currie, Eric M Rohren","doi":"10.1053/j.semnuclmed.2025.01.005","DOIUrl":"https://doi.org/10.1053/j.semnuclmed.2025.01.005","url":null,"abstract":"<p><p>While theranostics has transformed the precision medicine landscape over the last decade, there is scope for the development of true theranostic pairs, e.g. diagnostic and therapeutic partners in which any physical, chemical, and biological differences are negligible to in vivo application. Although simple to state in theory, there are, in fact, limited options exhibiting optimal physical characteristics and wholly shared elements. Further compounding real-world application of the traditional theranostic method are additional barriers. The use of PET/CT as the cornerstone of the diagnostic pair in theranostics creates inequity of access and opportunity based on socioeconomic and geographic factors, and the growing demand for both <sup>68</sup>Ga and <sup>177</sup>Lu is straining production capabilities globally. Improving access to theranostics globally will require novel thinking and infrastructure investment to ensure that patients of all economic and social backgrounds have access to this transformative technology. An approach which is underdeveloped, but which may address gaps in health inequities and improve outcomes, is the application of the widely available generator-produced <sup>99m</sup>Tc for imaging and <sup>188</sup>Re for therapy. Despite favourable and near identical radiochemistry, the search for the next generation of theranostic radionuclide pairs seldom references technetium or rhenium radionuclides. Advances in SPECT/CT instrumentation and radiochemistry provide an opportunity to deliver theranostics to communities not serviced by PET-based theranostics. The <sup>188</sup>Re and <sup>99m</sup>Tc supply by daily elution of a generator affords significant convenience, flexibility and delayed biomolecule imaging. Low abundance gamma emissions of <sup>188</sup>Re allow serial imaging and dosimetry calculations. <sup>99m</sup>Tc / <sup>188</sup>Re theranostics could address inequity in access and opportunity to cutting edge theranostics.</p>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143503570","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

Sharpening the Blade of Precision Theranostics. 磨砺精准治疗之刃。

IF 4.6 2区医学

Seminars in nuclear medicine Pub Date : 2025-02-24 DOI: 10.1053/j.semnuclmed.2025.01.007

Geoffrey M Currie, Eric M Rohren

{"title":"Sharpening the Blade of Precision Theranostics.","authors":"Geoffrey M Currie, Eric M Rohren","doi":"10.1053/j.semnuclmed.2025.01.007","DOIUrl":"https://doi.org/10.1053/j.semnuclmed.2025.01.007","url":null,"abstract":"<p><p>While theranostics is a new term for long-standing principles in nuclear medicine, recent advances have facilitated more personalized healthcare and precision medicine. Despite the widespread enthusiasm for theranostics and well established and standardized procedures, there are a number of opportunities to enhance practice and sharpen the blade of precision theranostics. A clear understanding of the requisites of an authentic theranostic pair reveals limitations in current approaches. Indeed, standardized dosing regimes based on activity dose as opposed to absorbed dose highlight the potential enhancements to outcomes and precision medicine that predictive dosimetry could bring. Such advances increase the demand for closer matching of biological and chemical properties of theranostic pairs. In turn, the need for more authentic or true theranostic pairs is revealed. While theranostics has provided a revolutionary toolkit for cancer management, advances in instrumentation, radiochemistry or clinical domains requires similar advances in the remaining domains. This discussion explores key considerations for an evolving theranostics landscape, recognising current best practice may fall short of precision medicine over coming years.</p>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143503572","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 Drug Discovery: An Update and Future Prospects 人工智能用于药物发现：最新进展和未来展望。

IF 4.6 2区医学

Seminars in nuclear medicine Pub Date : 2025-02-17 DOI: 10.1053/j.semnuclmed.2025.01.004

Harrison J. Howell BS , Jeremy P. McGale MA , Aurélie Choucair MD , Dorsa Shirini MD, MBA , Nicolas Aide MD, PhD , Michael A. Postow MD , Lucy Wang BA , Mickael Tordjman MD , Egesta Lopci MD, PhD , Augustin Lecler MD, PhD , Stéphane Champiat MD, PhD , Delphine L. Chen MD , Désirée Deandreis MD , Laurent Dercle MD, PhD

{"title":"Artificial Intelligence for Drug Discovery: An Update and Future Prospects","authors":"Harrison J. Howell BS , Jeremy P. McGale MA , Aurélie Choucair MD , Dorsa Shirini MD, MBA , Nicolas Aide MD, PhD , Michael A. Postow MD , Lucy Wang BA , Mickael Tordjman MD , Egesta Lopci MD, PhD , Augustin Lecler MD, PhD , Stéphane Champiat MD, PhD , Delphine L. Chen MD , Désirée Deandreis MD , Laurent Dercle MD, PhD","doi":"10.1053/j.semnuclmed.2025.01.004","DOIUrl":"10.1053/j.semnuclmed.2025.01.004","url":null,"abstract":"<div><div>Artificial intelligence (AI) has become a pivotal tool for medical image analysis, significantly enhancing drug discovery through improved diagnostics, staging, prognostication, and response assessment. At a high level, AI-driven image analysis enables the quantification and synthesis of previously qualitative imaging characteristics, facilitating the identification of novel disease-specific biomarkers, patient risk stratification, prognostication, and adverse event prediction. In addition, AI can assist in response assessment by capturing changes in imaging “phenotype” over time, allowing for optimized treatment plans based on real-time analysis. Integrating this emerging technology into drug discovery pipelines has the potential to accelerate the identification and development of new pharmaceuticals by assisting in target identification and patient selection, as well as reducing the incidence, and therefore cost, of failed trials through high-throughput, reproducible, and data-driven insights. Continued progress in AI applications will shape the future of medical imaging, ultimately fostering more efficient, accurate, and tailored drug discovery processes. Herein, we offer a comprehensive overview of how AI enhances medical imaging to inform drug development and therapeutic strategies.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 3","pages":"Pages 406-422"},"PeriodicalIF":4.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143449799","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 Evolution of Artificial Intelligence in Nuclear Medicine 人工智能在核医学中的发展。

IF 4.6 2区医学

Seminars in nuclear medicine Pub Date : 2025-02-10 DOI: 10.1053/j.semnuclmed.2025.01.006

Leonor Lopes MD , Alejandro Lopez-Montes PhD , Yizhou Chen MSc , Pia Koller MSc , Narendra Rathod PhD , August Blomgren MSc , Federico Caobelli MD , Axel Rominger PhD , Kuangyu Shi PhD , Robert Seifert MD

{"title":"The Evolution of Artificial Intelligence in Nuclear Medicine","authors":"Leonor Lopes MD , Alejandro Lopez-Montes PhD , Yizhou Chen MSc , Pia Koller MSc , Narendra Rathod PhD , August Blomgren MSc , Federico Caobelli MD , Axel Rominger PhD , Kuangyu Shi PhD , Robert Seifert MD","doi":"10.1053/j.semnuclmed.2025.01.006","DOIUrl":"10.1053/j.semnuclmed.2025.01.006","url":null,"abstract":"<div><div>Nuclear medicine has continuously evolved since its beginnings, constantly improving the diagnosis and treatment of various diseases. The integration of artificial intelligence (AI) is one of the latest revolutionizing chapters, promising significant advancements in diagnosis, prognosis, segmentation, image quality enhancement, and theranostics. Early AI applications in nuclear medicine focused on improving diagnostic accuracy, leveraging machine learning algorithms for disease classification and outcome prediction. Advances in deep learning, including convolutional and more recently transformer-based neural networks, have further enabled more precise diagnosis and image segmentation as well as low-dose imaging, and patient-specific dosimetry for personalized treatment. Generative AI, driven by large language models and diffusion techniques, is now allowing the process, interpretation, and generation of complex medical language and images. Despite these achievements, challenges such as data scarcity, heterogeneity, and ethical concerns remain barriers to clinical translation. Addressing these issues through interdisciplinary collaboration will pave the way for a broader adoption of AI in nuclear medicine, potentially enhancing patient care and optimizing diagnosis and therapeutic outcomes.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 3","pages":"Pages 313-327"},"PeriodicalIF":4.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143400013","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 Value of Artificial Intelligence in Prostate-Specific Membrane Antigen Positron Emission Tomography: An Update 人工智能在前列腺特异性膜抗原正电子发射断层扫描中的价值：最新进展。

IF 4.6 2区医学

Seminars in nuclear medicine Pub Date : 2025-01-31 DOI: 10.1053/j.semnuclmed.2024.12.001

Jianliang Liu MPhil , Kieran Sandhu MD , Dixon T.S. Woon DMedSc , Marlon Perera PhD , Nathan Lawrentschuk PhD

{"title":"The Value of Artificial Intelligence in Prostate-Specific Membrane Antigen Positron Emission Tomography: An Update","authors":"Jianliang Liu MPhil , Kieran Sandhu MD , Dixon T.S. Woon DMedSc , Marlon Perera PhD , Nathan Lawrentschuk PhD","doi":"10.1053/j.semnuclmed.2024.12.001","DOIUrl":"10.1053/j.semnuclmed.2024.12.001","url":null,"abstract":"<div><div>This review aims to provide an up-to-date overview of the utility of artificial intelligence (AI) in evaluating prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scans for prostate cancer (PCa). A literature review was conducted on the Medline, Embase, Web of Science, and IEEE Xplore databases. The search focused on studies that utilizes AI to evaluate PSMA PET scans. Original English language studies published from inception to October 2024 were included, while case reports, series, commentaries, and conference proceedings were excluded. AI applications show promise in automating the detection of metastatic disease and anatomical segmentation in PSMA PET scans. AI was also able to predict response to PSMA-based theragnostic and aids in tumor burden segmentation, improving radiotherapy planning. AI could also differentiate intraprostatic PCa with higher histological grade and predict extra-prostatic extension. AI has potential in evaluating PSMA PET scans for PCa, particularly in detecting metastasis, measuring tumor burden, detecting high grade intraprostatic cancer, and predicting treatment outcomes. Larger multicenter prospective studies are necessary to validate and enhance the generalizability of these AI models.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 3","pages":"Pages 371-376"},"PeriodicalIF":4.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143075346","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

Diagnostic Value of Gastrin-Releasing Peptide Receptor-Targeted PET Imaging in Oncology: A Systematic Review. 胃泌素释放肽受体靶向PET成像在肿瘤诊断中的价值：系统综述。

IF 4.6 2区医学

Seminars in nuclear medicine Pub Date : 2025-01-23 DOI: 10.1053/j.semnuclmed.2025.01.001

Nasibeh Mohseninia, Roya Eisazadeh, Seyed Ali Mirshahvalad, Nazanin Zamani-Siahkali, Anton Amadeus Hörmann, Christian Pirich, Andrei Iagaru, Mohsen Beheshti

{"title":"Diagnostic Value of Gastrin-Releasing Peptide Receptor-Targeted PET Imaging in Oncology: A Systematic Review.","authors":"Nasibeh Mohseninia, Roya Eisazadeh, Seyed Ali Mirshahvalad, Nazanin Zamani-Siahkali, Anton Amadeus Hörmann, Christian Pirich, Andrei Iagaru, Mohsen Beheshti","doi":"10.1053/j.semnuclmed.2025.01.001","DOIUrl":"https://doi.org/10.1053/j.semnuclmed.2025.01.001","url":null,"abstract":"<p><p>Gastrin-releasing peptide receptor (GRPR), overexpressed in various cancers, is a promising target for positron emission tomography (PET). This systematic review investigated the diagnostic value of GRPR-targeted PET imaging in oncology. A systematic search was conducted on major medical databases until May 23, 2024. Keywords were modified to include clinical original studies on GRPR-targeted PET in cancer patients. Out of 1624 searched studies initially, 107 were eligible for the full-text review. Overall, data from 38 studies met inclusion criteria, investigating GRPR-targeting radiotracers in breast cancer, prostate cancer, gastrointestinal stromal tumours (GIST) and gliomas (including optic pathway glioma and glioblastoma multiforme). In breast cancer, GRPR-targeted PET effectively detected primary tumours and metastases, particularly in estrogen receptor (ER)-positive patients, and predicted treatment response. In prostate cancer, high sensitivity (up to 88%) and specificity (up to 90%) for detecting primary tumours were observed, providing added value when combined with magnetic resonance imaging (MRI). In biochemical recurrence, sites of prostate cancer were identified even at PSA levels below 0.5ng/dL. Compared with PSMA PET, GRPR-targeted PET showed comparable or superior detection rates. Considering GIST, GRPR-targeted PET imaging proved to be a valuable diagnostic tool, particularly when [<sup>18</sup>F] FDG PET results were inconclusive. Regarding gliomas, GRPR-targeted PET achieved a 100% detection rate (MRI reference), aiding localization, preoperative planning, and differentiation between recurrence and malignant transformation. GRPR-targeted PET shows promise in improving cancer diagnostics, particularly in ER-positive breast cancer, prostate cancer, and gliomas, and may enhance clinical decision-making.</p>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143041582","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

Total Body PET-CT Protocols in Oncology 肿瘤学中的全身 PET-CT 方案。

IF 4.6 2区医学

Seminars in nuclear medicine Pub Date : 2025-01-01 DOI: 10.1053/j.semnuclmed.2024.05.008

Antonia Dimitrakopoulou-Strauss MD, Leyun Pan PhD, Christos Sachpekidis MD

{"title":"Total Body PET-CT Protocols in Oncology","authors":"Antonia Dimitrakopoulou-Strauss MD, Leyun Pan PhD, Christos Sachpekidis MD","doi":"10.1053/j.semnuclmed.2024.05.008","DOIUrl":"10.1053/j.semnuclmed.2024.05.008","url":null,"abstract":"<div><div>Recently developed long axial field of view (LAFOV) PET-CT scanners, including total body scanners, are already in use in a few centers worldwide. These systems have some major advantages over standard axial field of view (SAFOV) PET-CT scanners, mainly due to up to 20 times higher sensitivity and therefore improved lesion detectability. Other advantages are the reduction of the PET acquisition time for a static whole-body measurement, the reduction of the administered radiotracer dose, and the ability to perform delayed scans with good image quality, which is important for imaging radionuclides with long half-lives and pharmaceuticals with long biodistribution times, such as <sup>89</sup>Zr-labeled antibodies. The reduction of the applied tracer dose leads to less radiation exposure and may facilitate longitudinal studies, especially in oncological patients, for the evaluation of therapy. The reduction in acquisition time for a static whole body (WB) study allows a markedly higher patient throughput. Furthermore, LAFOV PET-CT scanners enable for the first-time WB dynamic PET scanning and WB parametric imaging with an improved image quality due to increased sensitivity and time resolution. WB tracer kinetics is of particular interest for the characterization of novel radiopharmaceuticals and for a better biological characterization of cancer diseases, as well as for a more accurate assessment of the response to new targeted therapies. Further technological developments based on artificial intelligence (AI) approaches are underway and may in the future allow CT-less attenuation correction or ultralow dose CT for attenuation correction as well as segmentation algorithms for the evaluation of total metabolic tumor volume. The aim of this review is to present dedicated PET acquisition protocols for oncological studies with LAFOV scanners, including static and dynamic acquisition as well as parametric scans, and to present literature data to date on this topic.</div></div>","PeriodicalId":21643,"journal":{"name":"Seminars in nuclear medicine","volume":"55 1","pages":"Pages 3-10"},"PeriodicalIF":4.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141293684","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