Seminars in nuclear medicine最新文献

Background/aim: Fibroblast activation protein inhibitor (FAPI) PET has shown promising diagnostic performance across various cancers. However, uptake in nonmalignant conditions has also been reported, as highlighted in the authors' previous systematic review from 2022. This updated systematic review summarizes the accumulated evidence on nonmalignant FAPI PET findings, both pitfalls in cancer diagnostic and emerging novel FAPI PET scan indications.

Materials and methods: A systematic search of PubMed, Embase, and Web of Science was conducted on May 2, 2025. Peer-reviewed English-language studies involving human subjects and using FAPI tracers, specifically targeting FAP, were included. Studies published between April 2022 and May 2025 reporting nonmalignant FAPI PET/CT findings were added to those from the previous 2022 review. Findings were analyzed on a per-lesion basis and grouped by anatomical region.

Results: In total, 380 studies reporting 8,230 nonmalignant FAPI PET findings were included. Most studies originated from China (70%), and 63% were case reports or case series. Although 69% of subjects were scanned due to "cancer", a clear increase in nonmalignant scan indications was observed. The search identified common pitfalls for cancer diagnostics, including FAPI uptake in infectious and inflammatory diseases, fibrosis, and benign neoplasms, but also emerging nonmalignant FAPI PET indications, including interstitial lung disease, cardiac conditions, and arthritis.

Conclusion: This review provides, to the best of our knowledge, the most comprehensive summary of nonmalignant FAPI PET findings to date. It may serve as a valuable reference for researchers and clinicians interpreting FAPI PET.

[¹⁸F] Fluorodeoxyglucose positron emission tomography/computed tomography ([¹⁸F] FDG-PET/CT) has traditionally been considered suboptimal for the evaluation of renal tumors due to intense physiological tracer accumulation in the urinary tract. However, incidental detection of renal masses can occur during [¹⁸F] FDG-PET examinations, and recognizing that FDG uptake varies according to tumor type can aid differential diagnosis. In patients with renal failure or those undergoing dialysis, reduced urinary excretion lowers background activity, paradoxically improving the visualization of renal tumors. Recent studies have demonstrated that high-grade and sarcomatoid renal cell carcinomas (RCCs) exhibit intense [¹⁸F] FDG uptake associated with poor prognosis, whereas benign and low-grade lesions show relatively low uptake. Although [¹⁸F] FDG-PET/CT has limited sensitivity for nodal staging, it provides high diagnostic accuracy for distant metastases, including osteolytic bone lesions. During postoperative surveillance and restaging, PET/CT contributes to early detection of recurrence and assists in therapeutic decision-making, particularly in high-risk or dialysis patients. In therapeutic monitoring, changes in metabolic parameters-such as SUV_max, metabolic tumor volume, and total lesion glycolysis-correlate with progression-free and overall survival during tyrosine kinase inhibitor or immune checkpoint inhibitor therapy. These parameters complement Response Evaluation Criteria in Solid Tumors (RECIST) and support metabolic response criteria such as PET Response Criteria in Solid Tumors (PERCIST). Moreover, novel tracers developed to overcome the intrinsic limitations of [¹⁸F] FDG, including [¹¹C] Acetate, [¹⁸F] Fluoromisonidazole, and prostate-specific membrane antigen ligands, have shown promise in differentiating fat-poor angiomyolipoma, evaluating tumor hypoxia, and detecting FDG-negative metastases. This review discusses the current role, limitations, and future perspectives of [¹⁸F] FDG-PET in the management of renal tumors, with particular focus on RCC.

For certain clinical scenarios accurate and precise GFR measurement is required. In the majority of situations this is calculated from the plasma clearance of an exogenous marker that undergoes glomerular filtration, using the slope-intercept (SI) or single-sample (SS) methods. This paper is intended to complement a previous Seminars paper with a specific focus on practical ways to avoid potential pitfalls and quality control measures that can be implemented to minimize the risk of GFR measurement errors. The underlying causes of GFR errors can be understood in terms of measurement errors or procedural errors. All measurements are accompanied by inherent errors that are related to the finite precision of the measuring instruments used. Kept within an acceptable range, measurement errors make a small contribution to GFR error when compared to expected biological variation. Procedural errors consist of a diverse group of deviations from correct procedure ranging from selection of an inappropriate methodology through to missteps at many points in the investigation procedure. Procedural errors can significantly affect the accuracy of GFR measurements. Errors are primarily avoided by regular equipment quality control and careful adherence to clear standard operating procedures. Quality control parameters integrated into a calculation spreadsheet can play a useful secondary role to flag potential measurement or procedural errors.

The contemporary scope of nuclear nephrology extends from non-imaging techniques for glomerular filtration rate calculation through dynamic renal scintigraphy and cortical imaging with planar or single photon emission computed tomography (SPECT) approaches to emerging applications in positron emission tomography (PET) renography and theranostics-based renal toxicity risk assessment. Artificial intelligence (AI) shares a long history with nuclear nephrology that started with expert systems and statistical machine learning (ML) approaches, transitioned through feed forward neural networks (FFNN), landed with convolutional neural networks (CNNs) and deep learning (DL), and has emerging opportunities across the gamut of generative AI like large language models (LLMs), diffusion models, generative adversarial networks (GANs) and multimodal models like vision language models (VLMs). A range of AI tools across the nuclear nephrology ecosystem describe bespoke AI algorithms, commercial AI products, embedded AI tools from vendors, general-purpose and cross-domain AI frameworks. Applications in clinical workflow, research and development, and imaging are explored, highlighting the potential of AI in detection, classification, segmentation, prediction, data analysis and image enhancement. Emerging AI opportunities from generative AI, LLMs, VLMs, and segmentation foundation models such as the SAM, offer exciting multi-modal, few-shot learning that may re-imagine nuclear nephrology. There remains the need for considerable development and validation for widespread clinical utility of AI opportunities, and the need for consideration of ethical limitations and social justice.

Renal cell carcinoma (RCC) is a clinically heterogeneous malignancy with rising global incidence. Conventional imaging modalities such as CT and MRI provide primarily anatomical information but are limited in their ability to characterize tumors at the molecular level. Positron emission tomography/ computed tomography (PET/CT) imaging offers a promising alternative by enabling non-invasive molecular diagnostics. This review summarizes recent advances in PET-based imaging of RCC and highlights tracers with potential for future clinical application. Fluorodeoxyglucose (2-[¹⁸F]FDG) PET/CT, although well established in oncology, demonstrates limited sensitivity for primary RCC but may be useful for detecting distant metastases and local recurrence. Consequently, increasing attention has shifted toward more specific molecular tracers that may improve diagnostic performance. Among these, fibroblast activation protein inhibitor (FAPI)-based PET imaging has shown higher sensitivity than 2-[¹⁸F]FDG PET across several RCC subtypes, although current evidence remains restricted to small or early-phase studies. Sodium-18F-fluoride ([¹⁸F]NaF) PET/CT has demonstrated excellent detection rates for bone metastases in RCC, yet evidence is currently sparse. Furthermore, CD70-targeted immunoPET/CT-using tracers such as [⁶⁸Ga]Ga-NOTA-RCCB6-has shown high specificity for clear cell RCC (ccRCC) and superior performance compared with 2-[¹⁸F]FDG PET/CT in the identification of metastatic disease; however, broader clinical validation is still required. Carbonic anhydrase IX (CAIX)-targeted PET/CT provides high specificity for ccRCC, particularly in the detection of small lesions, staging, and post-immunotherapy follow-up. Emerging theranostic approaches employing [⁶⁸Ga]/[¹⁷⁷Lu]-labeled CAIX ligands may further enable integrated diagnostic and radionuclide therapeutic strategies. In conclusion, molecular imaging is increasingly recognized as a valuable tool in the diagnosis and management of RCC. Larger, multicenter studies are essential to define its role in routine clinical practice and to fully explore its potential in future theranostic applications.