European Journal of Nuclear Medicine and Molecular Imaging最新文献

Purpose

To systematically review the literature and perform a meta-analysis of PET radiomics for histologic subtype classification in non-small cell lung cancer (NSCLC).

Methods

PubMed, Embase, Scopus, and Web of Science databases were systematically searched in English on human subjects for studies on distinguishing adenocarcinoma (ADC) from squamous cell carcinoma (SCC) using PET radiomics published from inception until November 2024. The Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and the Radiomics Quality Score (RQS) were utilized to assess the methodological quality of the included studies. The area under the receiver operating characteristic curves (AUC) was pooled to estimate predictive performance. An overall effect size was estimated using a random-effects model. Statistical heterogeneity was evaluated by the I² value. Subgroup analyses were conducted to explore sources of heterogeneity.

Results

Twelve studies were included in the analysis, yielding a pooled AUC of 0.92 (95% confidence interval [CI]: 0.89–0.94). Despite this promising result, the studies showed limitations in both study design and methodological quality, as evidenced by a median RQS of 11/36. A significant degree of heterogeneity was observed among the studies, with an I² of 92.20% (95% CI: 89.01–95.39) for sensitivity and 89.29% (95% CI: 84.48–94.10) for specificity.

Conclusions

This meta-analysis highlights the potential utility of PET radiomics in distinguishing ADC from SCC. However, the observed high heterogeneity indicates substantial methodological variability across the included studies. Future research should focus on standardization, transparency, and multicenter collaborations to improve the reliability and clinical applicability of PET radiomics for histologic subtype classification in NSCLC.

Purpose

Temporal lobe epilepsy (TLE) is a brain network disorder closely associated with synaptic loss and has a genetic basis. However, the in vivo whole-brain synaptic changes at the network-level and the underlying gene expression patterns in patients with TLE remain unclear.

Methods

In this study, we utilized a positron emission tomography with the synaptic vesicle glycoprotein 2 A radioligand [¹⁸F]SynVesT-1 cohort and two independent transcriptome datasets to investigate the topological properties of the synaptic density similarity network (SDSN) in TLE and its correlation with significantly dysregulated risk genes.

Results

We observed an overall decrease in strength, reduced clustering coefficient, and increased path length of SDSN in TLE, suggesting a loss of connectivity that is accompanied by network reorganization. These changes were predominantly distributed in the temporo-limbic circuit and fronto-parietal networks. Moreover, connectivity changes in SDSN were found to be spatially correlated with the brain-wide expression of TLE risk genes, and the transcriptional correlate of SDSN changes showed a significant relationship with gene dysregulation. In particular, we identified a total of 183 downregulated genes that were functionally enriched for synaptic transmission pathways, forming a highly connected genetic interaction network. Within this set of genes, GABAergic genes such as RBFOX1 play a central role.

Discussion

Our study provides the first evidence that the spatial expression patterns of downregulated risk genes underlie in vivo synaptic density network dysfunction in TLE. These imaging-transcriptomic findings have the potential to guide the development of molecular and genetic network-based therapeutic approaches for TLE.

This joint practice guideline/procedure standard was collaboratively developed by the European Association of Nuclear Medicine (EANM), the Society of Nuclear Medicine and Molecular Imaging (SNMMI), the European Association of Neuro-Oncology (EANO), and the PET task force of the Response Assessment in Neurooncology Working Group (PET/RANO). Brain metastases are the most common malignant central nervous system (CNS) tumors. PET imaging with radiolabeled amino acids and to lesser extent [¹⁸F]FDG has gained considerable importance in the assessment of brain metastases, especially for the differential diagnosis between recurrent metastases and treatment-related changes which remains a limitation using conventional MRI. The aim of this guideline is to assist nuclear medicine physicians in recommending, performing, interpreting and reporting the results of brain PET imaging in patients with brain metastases. This practice guideline will define procedure standards for the application of PET imaging in patients with brain metastases in routine practice and clinical trials and will help to harmonize data acquisition and interpretation across centers.

Purpose

To explore the dynamic and parametric characteristics of [¹⁸F]F-FAPI-42 PET/CT in lung cancers.

Methods

Nineteen participants with newly diagnosed lung cancer underwent 60-min dynamic [¹⁸F]F-FAPI-42 PET/CT. Time-activity curves (TAC) were generated for tumors and normal organs, with kinetic parameters (K₁, K₂, K₃, K₄, K_i) calculated. A new parameter, the K ratio (K₁ + K₃)/(K₂ + K₄), was introduced to measure net uptake efficiency.

Results

In primary tumor (PT), [¹⁸F]F-FAPI-42 uptake showed a gradual increase followed by a plateau, contrasting with organs like the thyroid and pancreas, which showed rapid uptake and continuous washout. Compared to non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) lesions reached the plateau earlier (11 min vs. 14 min) but had a lower uptake. During the plateau phase, [¹⁸F]F-FAPI-42 demonstrated slight washout in SCLC, whereas its uptake increased slightly in NSCLC. Lymph node and distant metastases exhibited similar TAC profiles to primary tumors. Kinetic modeling revealed that an irreversible two-compartment model (irre-2TCM) best represented the pharmacokinetics of [¹⁸F]F-FAPI-42 in lung cancer, whereas re-2TCM was better suited for the pancreas and thyroid. Lower K₁, K₂, K₃ and K₄ were observed in PT compared to those in the pancreas and thyroid (P < 0.05), however, the K ratio in PT was found to be 2–3 times higher. SCLC had lower K_i and SUVmean than NSCLC (P < 0.05). Kinetic parameter differences were also observed between PT and metastatic lesions. Larger metastatic lymph nodes exhibited higher K₁, K_i, and K ratio than smaller ones.

Conclusion

Lung cancers exhibit distinct [¹⁸F]F-FAPI-42 dynamic and kinetic characteristics compared to the thyroid gland and pancreas. Differences were also observed between SCLC and NSCLC, primary and metastatic lesions, as well as larger versus smaller lesions. These findings provide valuable insights into the in vivo pharmacokinetics of [¹⁸F]F-FAPI-42, potentially improving the diagnosis of lung cancer.

Trial registration

ChiCTR2100045757. Registered April 24, 2021 retrospectively registered, http//www.chictr.org.cn.

Objective

This study aims to conduct a bibliometric analysis to explore research trends, collaboration patterns, and emerging themes in the PET/MR field based on published literature from 2010 to 2024.

Methods

A detailed literature search was performed using the Web of Science Core Collection (WoSCC) database with keywords related to PET/MR. A total of 4,349 publications were retrieved and analyzed using various bibliometric tools, including VOSviewer and CiteSpace.

Results

The analysis revealed an initial increase in PET/MR publications, peaking at 495 in 2021, followed by a slight decline. The USA, Germany, and China were the most prolific countries, with the USA demonstrating strong collaborative networks. Key institutions included the Stanford University, Technical University of Munich and University of Duisburg-Essen. Prominent authors were primarily from Germany, with significant contributions from University Hospital Essen. Major journals in the field included the European Journal of Nuclear Medicine, Journal of Nuclear Medicine, and Physics in Medicine and Biology. Emerging research areas focused on oncology, neurological disorders, and cardiovascular diseases, with keywords such as “prostate cancer,” “Alzheimer’s disease,” and “breast cancer” showing high research activity. Recent trends also highlight the growing integration of AI, particularly deep learning, to improve imaging reconstruction and diagnostic accuracy.

Conclusion

The findings emphasize the need for continuous investment, strategic planning, and technological innovations to expand PET/MR’s clinical applications. Future research should focus on optimizing imaging techniques, fostering international collaborations, and integrating emerging technologies like artificial intelligence to enhance PET/MR’s diagnostic and therapeutic potential in precision medicine.