記述言語：英語   出版者・発行元：Japanese Journal of Radiology  

Objectives: To evaluate the distribution patterns of sarcoidosis involvement on FDG-PET/CT in patients with known or suspected cardiac sarcoidosis (CS), with a particular focus on upper abdominal lymph nodes (LN) (periportal LN [PLN], anterior pancreaticoduodenal LN [APDLN], and posterior pancreaticoduodenal LN [PPDLN]) and the association of them with other lesions and myocardium. Methods: We identified 861 FDG-PET/CT scans performed between July 2016 and August 2024 in patients with known or suspected CS, and included 178 cases for analysis of FDG uptake patterns suggestive of sarcoid involvement. FDG-positive LNs or regions were classified as sarcoidosis-related based on treatment response, characteristic uptake patterns, or histological confirmation. The occurrence ratio of FDG-positive lymph nodes or regions was also assessed in relation to myocardial FDG uptake patterns. Results: FDG uptake was observed most frequently in hilar and mediastinal LNs (79% and 76%, respectively). Upper abdominal LN uptake was observed in 49.4% of patients, most commonly in the PLN (31.5%), APDLN (38.2%), and PPDLN (37.1%). Heatmap analyses revealed strong co-occurrence between thoracic and upper abdominal LNs, suggesting a lymphatic dissemination pattern. Peripheral LNs such as axillary, subclavian, and inguinal/pelvic stations demonstrated low uptake and minimal co-occurrence. Conclusions: FDG-PET/CT provides valuable insight into the structured lymphatic dissemination of sarcoidosis. Frequent FDG uptake in upper abdominal lymph nodes, particularly when accompanied by thoracic involvement, represents a characteristic finding in sarcoidosis. Recognition of this pattern can improve diagnostic accuracy and help differentiate sarcoidosis from other systemic diseases. Secondary abstract: This study assessed lymph node involvement on FDG-PET/CT in patients with suspected or known cardiac sarcoidosis, revealing distinct dissemination patterns into the upper abdomen. These findings enhance understanding of disease pathophysiology and may improve diagnostic evaluation.

DOI： 10.1007/s11604-025-01935-x

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PubMed

記述言語：英語   出版者・発行元：Nagoya Journal of Medical Science  

Neuroendocrine tumors (NETs) are a heterogenous group of neoplasms originating from neuroendocrine cells, most frequently found in the gastrointestinal tract and pancreas. A defining feature of NETs is the overexpression of somatostatin receptors (SSTRs), particularly subtype 2 (SSTR2), which is the primary target for both diagnostic imaging and therapeutic interventions. Peptide receptor radionuclide therapy (PRRT) using¹⁷⁷Lu-DOTATATE, a radiolabeled somatostatin analog, has emerged as a transformative alternative for patients with advanced or progressive well-differentiated NETs. Considering that SSTR expression is also present in various other tumors—including pheochromocytomas, paragangliomas, meningiomas, and medullary thyroid carcinomas—there is increasing interest in expanding the use of PRRT to other SSTR-positive malignancies. This review aimed to present evidence, explore ongoing clinical research, and highlight emerging directions for¹⁷⁷Lu-DOTATATE therapy beyond gastroenteropancreatic NETs.

DOI： 10.18999/nagjms.87.4.607

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PubMed

記述言語：英語   出版者・発行元：Annals of Nuclear Medicine  

Objective: Vision language models (VLMs) allow visual input to Large Language Models. VLMs have been developing rapidly, and their accuracy is improving rapidly. Their performance in nuclear medicine compared to state-of-the-art models, including reasoning models, is not yet clear. We evaluated state-of-the-art VLMs using problems from the past Japan Nuclear Medicine Board Examination (JNMBE) and assessed their strengths and limitations. Methods: We collected 180 multiple-choice questions from JNMBE (2022–2024). About one-third included diagnostic images. We used eight latest VLMs. ChatGPT o1 pro, ChatGPT o1, ChatGPT o3-mini, ChatGPT-4.5, Claude 3.7, Gemini 2.0 Flash thinking, Llama 3.2, and Gemma 3 were tested. Each model answered every question three times in a deterministic setting, and the final answer was set by majority vote. Two board-certified nuclear medicine physicians independently provided reference answers, with a third expert resolving disagreements. We calculated overall accuracy with 95% confidence intervals and performed subgroup analyses by question type, content, and exam year. Results: Overall accuracies ranged from 36.1% (Gemma 3) to 83.3% (ChatGPT o1 pro). ChatGPT o1 pro achieved the highest score (150/180, 83.3% [95% CI: 77.1–88.5%]), followed by ChatGPT o3-mini (82.8%) and ChatGPTo1 (78.9%). All models performed better on text-only questions than on image-based ones; ChatGPT o1 pro correctly answered 89.5% of text questions versus 66.0% of image questions. VLMs demonstrated limitations in handling with questions on Japanese regulations. ChatGPT 4.5 excelled in neurology-related image-based questions (76.9%). Accuracy was slightly lower from 2022 to 2024 for most models. Conclusions: VLMs demonstrated high accuracy on the JNMBE, especially on text-based questions, but exhibited limitations with image recognition questions. These findings show that VLMs can be a good assistant for text-based questions in medical domains but have limitations when it comes to comprehensive questions that include images. Currently, VLMs cannot replace comprehensive training and expert interpretation. Because VLMs evolve rapidly and exam difficulty varies annually, these findings should be interpreted in that context.

DOI： 10.1007/s12149-025-02084-x

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PubMed

記述言語：英語   出版者・発行元：Japanese Journal of Radiology  

In pediatric oncology, ¹⁸F-fluorodeoxyglucose (FDG)-positron emission tomography (PET)/computed tomography (CT) is valuable as a tool for noninvasive imaging and monitoring. While many reports have reviewed the use of PET and PET/CT in pediatrics, considerable variations in age, body size, and metabolism are seen during different stages of childhood development. Neonates (from birth to one month old) and infants (from 1 month to 1 year) present unique challenges for FDG-PET/CT examination due to their small body size, the immaturity of organs, the need for specialized patient preparation, and support requirements during scanning. In addition, differences in metabolic activity can lead to distinct differences in patterns of physiological FDG uptake on PET/CT imaging between neonates and infants. These factors differ significantly from those encountered in older children, who may be treated similarly to adults during imaging procedures. This review, based on both the literature and clinical experience, explores the specific characteristics, challenges, and considerations for FDG-PET/CT imaging from neonates to infants, with a focus on optimizing imaging protocols and interpreting physiological variations in this growth period.

DOI： 10.1007/s11604-025-01763-z

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記述言語：英語   出版者・発行元：Ejnmmi Reports  

Purpose: This study evaluates the performance of four large language models (LLMs) in classifying malignant lymphoma stages using the Lugano classification from free-text FDG-PET reports in Japanese Specifically, we assess GPT-4o, Claude 3.5 Sonnet, Llama 3 70B, and Gemma 2 27B in their ability interpret unstructured radiology texts. Materials and methods: In a retrospective single-center study, 80 patients who underwent staging FDG-PET/CT for malignant lymphoma were included. The “Findings” sections of their reports were analyzed without pre-processing. Each LLM assigned Lugano stages based on these reports. Performance was compared to reference standard stages determined by expert radiologists. Statistical analyses involved overall accuracy, weighted kappa for agreement. Results: GPT-4o achieved the highest accuracy at 75% (60/80 cases) with substantial agreement (weighted kappa κ = 0.801). Claude 3.5 Sonnet had 61.3% accuracy (49/80, κ = 0.763). Gemma 2 27B and Llama 3 70B showed accuracies of 58.8% and 57.5%, respectively, all indicating substantial agreement. Conclusion: GPT-4o outperformed other LLMs in assigning Lugano classification from Japanese FDG-PET free-text reports. This demonstrated the potential of advanced LLMs to interpret clinical texts. While the immediate clinical utility of automatically predicting a Lugano stage from an existing report may be limited, these results highlight the value of LLMs for understanding and standardizing free-text radiology data.

DOI： 10.1186/s41824-025-00246-8

Open Access

Web of Science

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PubMed

開催年月日： 2026年2月

記述言語：日本語   会議種別：口頭発表（一般）  

開催年月日： 2025年11月

会議種別：ポスター発表