European Journal of Nuclear Medicine and Molecular Imaging最新文献

Purpose

Clinical staging in individuals with Alzheimer’s disease (AD) typically relies on neuropsychological testing. Recognizing the imperative for an objective measure of clinical AD staging, regional perfusion in early-phase β-amyloid-PET may aid as a cost-efficient index for the assessment of neurodegeneration severity in patients with Alzheimer’s disease.

Methods

Regional perfusion deficits in early-phase β-amyloid-PET as well as neuropsychological testing (max. 90 days delay) were evaluated in 82 patients with biologically defined AD according to the ATN classification. In reference to the Braak staging system patients were classified into the groups stage⁰, stage^I−II+, stage^I−IV+, stage^I−VI+, and stage^atypical+ according to regional perfusion deficits in regions of interest (ROIs) published by the Alzheimer’s Disease Neuroimaging Initiative. Multiple regression analysis controlling for age, gender, and education was used to evaluate the association of regional z-scores on perfusion-phase PET with clinical scores for all patients and with annual decline of cognitive performance in 23 patients with follow-up data.

Results

Patients classified as stage⁰ and stage^I−II+ demonstrated significantly superior neuropsychological performance compared to those classified as stage^I−IV+ and stage^I−VI+. Lower cognitive performance was associated with decreased perfusion in early-phase β-amyloid-PET globally and regionally, with the most pronounced association identified in the left temporal lobe. Mean z-scores on early-phase PET in temporal and parietal regions offered a robust prediction of future annual decline in MMSE and sum scores of the CERAD-Plus (Consortium to Establish a Registry for Alzheimer’s Disease) test battery.

Conclusion

Regional and global perfusion deficits in early-phase β-amyloid-PET can serve as an objective index of neurodegeneration severity and may act as prognostic markers of future cognitive decline in AD.

Purpose

Gallium-68-labeled fibroblast activation protein inhibitor ([⁶⁸Ga]Ga-FAPI) positron emission tomography (PET) has demonstrated excellent diagnostic performance in various malignancies, including gastric carcinoma. However, its prognostic utility is unclear. This study evaluates the prognostic value of [⁶⁸Ga]Ga-FAPI-04 PET/MRI(CT) in gastric carcinoma.

Methods

We retrospectively analyzed patients with gastric cancer who underwent [⁶⁸Ga]Ga-FAPI-04 PET/MRI(CT) between June 2020 and June 2023. Semi-quantitative parameters, including maximum and mean standard uptake value (SUVmax, SUVmean), FAPI-avid tumor volume (FTV), total lesion FAP expression (TLF), tumor to background ratio (TBR), heterogeneity factor (HF) and coefficient of variation (CV) of the primary tumor were measured or calculated. Overall survival (OS) and progression-free survival (PFS) were obtained through follow-up. The relationships between disease prognosis and potential predictors were analyzed， and predictive models were established.

Results

Eighty-six patients (median age 59 years) were included. Thirty-five patients experienced disease progression, and 26 of them died. Univariable analysis revealed SUVmax, FTV, TLF, TBR, HF and CV were significant prognostic factors for both OS and PFS. In multivariate Cox regression analysis, a nomogram model for OS was established, incorporating body mass index (BMI) and CV as independent predictors. The time-dependent C-index of the nomogram model > 0.75 indicates good predictive performance. When predicting PFS, a stratified analysis was performed based on distant metastasis, FTV was an independent prognostic factor among patients without distant metastasis.

Conclusion

CV and FTV, derived from [⁶⁸Ga]Ga-FAPI-04 PET imaging, could serve as independent prognostic factor for OS and PFS in patients with gastric cancer, respectively.

Background

Cardiac amyloidosis (CA) is a severe condition characterized by amyloid fibril deposition in the myocardium, leading to restrictive cardiomyopathy and heart failure. Differentiating between amyloidosis subtypes is crucial due to distinct treatment strategies. The individual conventional diagnostic methods lack the accuracy needed for effective subtype identification. This study aimed to evaluate the efficacy of combining ¹¹C-PiB PET/CT and ^99mTc-DPD scintigraphy in detecting CA and distinguishing between its main subtypes, light chain (AL) and transthyretin (ATTR) amyloidosis while assessing the association of imaging findings with patient prognosis.

Methods

We retrospectively evaluated the diagnostic efficacy of combining ¹¹C-PiB PET/CT and ^99mTc-DPD scintigraphy in a cohort of 50 patients with clinical suspicion of CA. Semi-quantitative imaging markers were extracted from the images. Diagnostic performance was calculated against biopsy results or genetic testing. Both machine learning models and a rationale-based model were developed to detect CA and classify subtypes. Survival prediction over five years was assessed using a random survival forest model. Prognostic value was assessed using Kaplan-Meier estimators and Cox proportional hazards models.

Results

The combined imaging approach significantly improved diagnostic accuracy, with ¹¹C-PiB PET and ^99mTc-DPD scintigraphy showing complementary strengths in detecting AL and ATTR, respectively. The machine learning model achieved an AUC of 0.94 (95% CI 0.93–0.95) for CA subtype differentiation, while the rationale-based model demonstrated strong diagnostic ability with AUCs of 0.95 (95% CI 0.88-1.00) for ATTR and 0.88 (95% CI 0.770–0.961) for AL. Survival prediction models identified key prognostic markers, with significant stratification of overall mortality based on predicted survival (p value = 0.006; adj HR 2.43 [95% CI 1.03–5.71]).

Conclusion

The integration of ¹¹C-PiB PET/CT and ^99mTc-DPD scintigraphy, supported by both machine learning and rationale-based models, enhances the diagnostic accuracy and prognostic assessment of cardiac amyloidosis, with significant implications for clinical practice.

Graphical abstracts

Cancer remains a leading cause of mortality globally, driving ongoing research into innovative treatment strategies. Preclinical research forms the base for developing these novel treatments, using both in vitro and in vivo model systems that are, ideally, as clinically representative as possible. Emerging as a promising approach for cancer management, targeted radionuclide theranostics (TRT) uses radiotracers to deliver (cytotoxic) radionuclides specifically to cancer cells. Since the field is relatively new, more advanced preclinical models are not yet regularly applied in TRT research. This narrative review examines the currently applied in vitro, ex vivo and in vivo models for oncological research, discusses if and how these models are now applied for TRT studies, and whether not yet applied models can be of benefit for the field. A selection of different models is discussed, ranging from in vitro two-dimensional (2D) and three-dimensional (3D) cell models, including spheroids, organoids and tissue slice cultures, to in vivo mouse cancer models, such as cellline-derived models, patient-derived xenograft models and humanized models. Each of the models has advantages and limitations for studying human cancer biology, radiopharmaceutical assessment and treatment efficacy. Overall, there is a need to apply more advanced models in TRT research that better address specific TRT phenomena, such as crossfire and abscopal effects, to enhance the clinical relevance and effectiveness of preclinical TRT evaluations.

Purpose

PET/CT imaging data contains a wealth of quantitative information that can provide valuable contributions to characterising tumours. A growing body of work focuses on the use of deep-learning (DL) techniques for denoising PET data. These models are clinically evaluated prior to use, however, quantitative image assessment provides potential for further evaluation. This work uses radiomic features to compare two manufacturer deep-learning (DL) image enhancement algorithms, one of which has been commercialised, against ‘gold-standard’ image reconstruction techniques in phantom data and a sarcoma patient data set (N=20).

Methods

All studies in the retrospective sarcoma clinical [(^{18})F]FDG dataset were acquired on either a GE Discovery 690 or 710 PET/CT scanner with volumes segmented by an experienced nuclear medicine radiologist. The modular heterogeneous imaging phantom used in this work was filled with [(^{18})F]FDG, and five repeat acquisitions of the phantom were acquired on a GE Discovery 710 PET/CT scanner. The DL-enhanced images were compared to ‘gold-standard’ images the algorithms were trained to emulate and input images. The difference between image sets was tested for significance in 93 international biomarker standardisation initiative (IBSI) standardised radiomic features.

Results

Comparing DL-enhanced images to the ‘gold-standard’, 4.0% and 9.7% radiomic features measured significantly different (p_critical < 0.0005) in the phantom and patient data respectively (averaged over the two DL algorithms). Larger differences were observed comparing DL-enhanced images to algorithm input images with 29.8% and 43.0% of radiomic features measuring significantly different in the phantom and patient data respectively (averaged over the two DL algorithms).

Conclusion

DL-enhanced images were found to be similar to images generated using the ‘gold-standard’ target image reconstruction method with more than 80% of radiomic features not significantly different in all comparisons across unseen phantom and sarcoma patient data. This result offers insight into the performance of the DL algorithms, and demonstrate potential applications for DL algorithms in harmonisation for radiomics and for radiomic features in quantitative evaluation of DL algorithms.

The growing interest and investments in targeted radionuclide therapy (TRT) have expanded research efforts across preclinical and clinical domains. Researchers from diverse fields, including nuclear medicine, radiochemistry, radiopharmacy, radiotherapy, biology, and physics, are increasingly focusing on TRT. Despite this, a lack of standardization in preclinical radiobiological studies hinders the evaluation and comparison of therapeutic radiopharmaceuticals across laboratories. Recognizing this, participants at the second International Workshop on Radiobiology of Molecular Radiotherapy, held in London, UK, in March 2023, emphasized the need for a consensus on nomenclature and standardized reporting guidelines in this field. The recommendations outlined here aim to address this gap by promoting consistent reporting. By adopting these standards, we hope to enhance the reproducibility, inter-laboratory comparability and visibility of preclinical TRT research, ultimately accelerating progress and amplifying its impact on the field and clinic.

Purpose

This study explores the use of fibroblast activation protein inhibitors (FAPI) targeting radiopharmaceuticals as a new approach for pan-cancer treatment, focusing on key factors affecting their effectiveness. We hypothesized that adjusting the administered radiotracer dose one could enhance the tumor-to-background ratios.

Methods

In a dose-escalation study with PC3 xenografts, all radiotracers were administered at doses between 10 and 1500 pmol, followed by biodistribution and PET/CT imaging. Their selectivity towards FAP, PREP, and DDP4, along with their stability in vivo, was assessed by biodistribution and metabolite analysis, respectively. Organ FAP expression was quantified using qPCR, and circulating FAP (sFAP) levels were measured in mouse and human blood samples via ELISA. Proof-of-principle human studies were also conducted.

Results

Increasing the dose from 10 to 600 pmol significantly reduced blood uptake and enhanced tumor uptake, optimizing their in vivo performance. All radiotracers showed peak efficacy at 350–600 pmol, with altered pharmacokinetics beyond 600 pmol. Biodistribution studies validated the in vivo selectivity of all radiotracers towards FAP, even in the presence of PREP and DPP4 inhibitors, while they demonstrated remarkable stability in vivo. FAP expression was confirmed in various organs, with sFAP quantified in both healthy mice and humans. Human studies with [⁶⁸Ga]Ga-DOTA.SA.FAPI revealed reduced off-target uptake (e.g., pancreas, salivary glands, heart), aligning with the preclinical findings.

Conclusion

The study highlights the crucial need for precise FAPI-radiotracer dosing, optimizing PET imaging, reducing radiation exposure, and enhancing treatment by accounting for FAP biology and sFAP’s influence on pharmacokinetics.

Purpose

Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion artifacts, but the shortened scanning time increases statistical noise and may affect diagnostic quality. This study aims to denoise the U2BH PET images using a deep learning (DL)-based method.

Methods

The study was conducted on two datasets collected from five scanners where the first dataset included 1272 retrospectively collected full-time PET data while the second dataset contained 46 prospectively collected U2BH and the corresponding full-time PET/CT images. A robust and data-efficient DL method called mask vision transformer (Mask-ViT) was proposed which, after fine-tuned on a limited number of training data from a target scanner, was directly applied to unseen testing data from new scanners. The performance of Mask-ViT was compared with state-of-the-art DL methods including U-Net and C-Gan taking the full-time PET images as the reference. Statistical analysis on image quality metrics were carried out with Wilcoxon signed-rank test. For clinical evaluation, two readers scored image quality on a 5-point scale (5 = excellent) and provided a binary assessment for diagnostic quality evaluation.

Results

The U2BH PET images denoised by Mask-ViT showed statistically significant improvement over U-Net and C-Gan on image quality metrics (p < 0.05). For clinical evaluation, Mask-ViT exhibited a lesion detection accuracy of 91.3%, 90.4% and 91.7%, when it was evaluated on three different scanners.

Conclusion

Mask-ViT can effectively enhance the quality of the U2BH PET images in a data-efficient generalization setup. The denoised images meet clinical diagnostic requirements of lesion detectability.