Seminars in nuclear medicine最新文献

Fibroblast activation protein (FAP), selectively expressed on activated fibroblasts in proliferating tissues, is emerging as a promising target in oncology. In lung cancer, the leading cause of cancer-related deaths worldwide, [¹⁸F]FDG PET/CT has set the bar high and earned widespread recognition in clinical guidelines for its essential role in staging and follow-up. Yet, FAP-targeted imaging agents like FAPI PET/CT have demonstrated significant potential due to their high tumor specificity, rapid tracer uptake, and low background activity. This review focuses on the role of FAPI PET/CT in lung cancer, highlighting its applications in staging, biomarker evaluation, and clinical management. FAP expression correlates with cancer associated fibroblast-driven tumorigenesis in lung cancer, showing higher expression in nonsmall cell lung cancer (NSCLC) than in small cell lung cancer (SCLC) subtypes. Studies reveal that FAPI PET/CT provides comparable or superior detection rates for primary tumors and metastases compared to [¹⁸F]FDG PET/CT, particularly in brain, pleural, and bone lesions. It also enhances accuracy in lymph node staging, influencing disease management by enabling surgical resection in cases misclassified by [¹⁸F]FDG PET/CT. Despite these advantages, several challenges remain, such as differentiating benign from malignant lesions, assessing FAPI's prognostic implications or its role in treatment response monitoring. Future directions include exploring FAPI-based theranostics, standardizing radiopharmaceuticals, and conducting well-designed, adequately powered prospective trials. FAPI PET/CT represents a transformative diagnostic tool, complementing or potentially surpassing [¹⁸F]FDG PET/CT in precision lung cancer care.

Advancements in Artificial Intelligence (AI) are driving a paradigm shift in the field of medical diagnostics, integrating new developments into various aspects of the clinical workflow. Neuroendocrine neoplasms are a diverse and heterogeneous group of tumors that pose significant diagnostic and management challenges due to their variable clinical presentations and biological behavior. Innovative approaches are essential to overcome these challenges and improve the current standard of care. AI-driven applications, particularly in imaging workflows, hold promise for enhancing tumor detection, classification, and grading by leveraging advanced radiomics and deep learning techniques. This article reviews the current and emerging applications of AI computer vision in the care of neuroendocrine neoplasms, focusing on its integration into imaging workflows, diagnostics, prognostic modeling, and therapeutic planning.

In non-small cell lung carcinoma (NSCLC) carcinoma, the CT-part of the FDG PET/CT examination is of primary importance for T (tumor)-status assessment, while information derived from the primary tumor on the FDG-part of the examination may provide additional information on N- (lymph node) status. FDG PET/CT imaging was shown to have an overall sensitivity of 85% and a specificity of 84% for identifying LN involvement in NSCLC. Parameters that may predict the presence and quantify the risk of LN-involvement in NSCLC missed on FDG PET/CT imaging are tumor size and its increase over time, tumor differentiation degree, the number of days elapsed from the time of initial diagnosis, an adenocarcinoma subtype, a central versus peripheral location of the primary tumor and a solid versus mixed solid-ground glass radiologic character. Nomograms incorporating several of these variables have been published and made available for clinical usage. Furthermore, FDG PET/CT imaging was shown to have an overall higher sensitivity for identifying extra-thoracic metastases than convential morphological imaging and this especially for bone and adrenal lesions. In small cell lung carcinoma (SCLC), limited available data have shown FDG PET/CT imaging to be systematically more accurate for staging purposes when compared to conventional staging and to lead to a change in disease stage (limited versus extensive disease) in up to 15% of SCLC-patients.

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.

Malignant pleural mesothelioma (MPM) is a rare but aggressive cancer characterized by its unique growth patterns, presenting substantial diagnostic challenges. With the shift toward immunotherapy for MPM treatment, assessing therapeutic responses has become increasingly complex. Recent studies indicate that FDG PET/CT may provide more effective response criteria compared to traditional CT-based methods. This review emphasizes the important role of PET/CT in offering deep insights into the disease state and monitoring treatment responses. It also addresses the challenges associated with current imaging criteria, particularly the nonspecificity of FDG uptake that may represent inflammatory responses following treatments or procedures rather than tumor activity. Furthermore, the review discusses the potential of emerging radiopharmaceuticals and advanced volumetric assessments, discussing their implications for improving diagnostic accuracy and treatment evaluation in MPM.

The management of Lung cancer, especially non-small cell lung cancer has undergone a paradigm shift recently with the advent of new treatment approaches including focused radiotherapy as well as evolution of a newer class of immunotherapy agents. Treatment efficacy and survival rates have improved and it is now even more important that patients are selected for appropriate interventions on the basis of a comprehensive assessment including a range of imaging as well as in-vitro tests such as immunohistochemistry. A new class of tracers targeting programmed cell death such as PD1 and PDL1 (broadly classed as Immuno PET) are being increasingly used in the molecular characterisation of patients deemed resistant to standard treatment approaches and being considered for additional interventions such as immunotherapy. In this review, we review the latest evidence in the field and propose a summary of clinical usefulness and provide a review of the research trends in this exciting and evolving field.

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.

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 ⁶⁸Ga and ¹⁷⁷Lu 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 ^99mTc for imaging and ¹⁸⁸Re 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 ¹⁸⁸Re and ^99mTc supply by daily elution of a generator affords significant convenience, flexibility and delayed biomolecule imaging. Low abundance gamma emissions of ¹⁸⁸Re allow serial imaging and dosimetry calculations. ^99mTc / ¹⁸⁸Re theranostics could address inequity in access and opportunity to cutting edge theranostics.

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.

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.