European Journal of Nuclear Medicine and Molecular Imaging最新文献

Purpose

Approximately 10% of prostate cancer (PCa) are prostate-specific membrane antigen (PSMA)-negative, leading to blind spots in PSMA-based diagnosis. This study aimed to identify a potential target for PSMA-negative PCa and preliminarily evaluate the feasibility of using radionuclide probe targeting the identified target for PCa diagnosis.

Methods

Quantitative protein analysis was performed on eight PSMA-negative PCa and eleven controls to identify a potential molecular target, followed by validation with an expanded cohort using immunohistochemistry. Sixteen participants underwent [¹⁸F]AlF-CD13-L1 PET/CT scanning, with the PCa pathological tissues used as references to interpret the imaging results.

Results

Quantitative protein analysis revealed CD13 as the most significantly upregulated membrane protein in PSMA-negative PCa. Expanded validation results indicated that CD13 positivity rates were 92.9% (13/14), 82.7% (105/127), 91.7% (11/12), and 70% (14/20) in PSMA-negative PCa, PSMA-positive PCa, ductal adenocarcinoma of the prostate (DAC), and intraductal carcinoma of the prostate (IDC-P), respectively. In PCa participants, the median [¹⁸F]AlF-CD13-L1 PET/CT maximum standardized uptake value (SUVmax) of tumors and tumor-to-muscle ratio were 4.3 (1.5–5.8) and 4.6 (1.7–6.1), respectively. The SUVmax value of the PCa lesions and the tumor-to-muscle ratio showed a positive correlation with the immunohistochemical score of CD13 of the PCa lesions (r_spearman = 0.6249, p = 0.025; r_spearman = 0.6714, p = 0.015, respectively), with CD13-positive tumors showing significant radiotracer accumulation.

Conclusion

CD13 was a potential target for PSMA-negative PCa and also showed high positivity rates in PSMA-positive PCa, DAC, and IDC-P. [¹⁸F]AlF-CD13-L1 selectively accumulated in CD13-positive PCa, enabling visualization. (Trial registration: ChiCTR2300077817. Registered November 21, 2023).

Purpose

In this trial we explore potential indications of an intra-operative mobile PET/CT camera. The tested device is designed to acquire high quality images of resected tissue specimens from patients who were administered a PET-tracer, shortly before resection. Besides clinical experiences, we will also comment on the practical aspects of the implementation of a safe workflow for intra-operative PET/CT-imaging.

Methods

This investigator driven study involved a 12-month evaluation of the AURA 10 PET/CT camera (XEOS Medical, Belgium). Depending on the tumour type, [¹⁸F]FDG, [18F]JK-PSMA-7, [^18F]PSMA-1007 or [¹⁸F]Choline was injected intravenously 60–90 min prior to the removal of the tumour. The tissue was scanned in the mobile PET/CT-device and 12 min later the surgeon could review the images. Specimen PET/CT-images were confronted with pathology findings. Dose rates were monitored around the patient throughout the procedure.

Results

The technique was tested in 32 surgeries for thyroid carcinomas (n = 5), transitional cell carcinomas (n = 2,) renal cell carcinoma (n = 1), prostate cancer (n = 5), breast carcinoma (n = 7), skin cancer (n = 3), nodal or bone biopsy for oncology work up (n = 6) and parathyroid adenoma (n = 3). Normalized to an injected activity of 1 MBq/kg the estimated median absorbed doses per procedure were 15.6 µSv (range 0,7-140,8) and 14,1 µSv (0,5–46,2) for respectively the surgeons and instrumenting nurses.

Conclusion

The overall experience of intra-operative PET/CT-imaging of surgical specimens was promising in our hospital, with particular added value in case of (para)thyroid, urological surgeries, and oncological work-ups. High quality images were obtained with low activity of tracers, enabling a safe implementation.

Trial registration number (Belgium)

BUN: B0172022000009 (NCT retrospectively submitted).

Purpose

Tumor-associated neovasculature and energy metabolism reprogramming serve as critical indicators of tumor proliferation, progression, invasion, and metastasis. This study conducted a head-to-head clinical investigation and comparison of [¹⁸F]FDG and [⁶⁸Ga]Ga-HX01 PET by reflecting neovasculature and glucose metabolism in sarcoma patients, respectively.

Methods

We reviewed the imaging data of sarcoma patients who underwent [⁶⁸Ga]Ga-HX01 PET/MR and [¹⁸F]FDG PET/CT from June 29, 2022, to December 21, 2023. The two imaging modalities were performed on two separate days within one week of each other. A cohort of 21 patients with an average age of 45.81 ± 19.99 years were enrolled. The location, number and PET characteristics of all lesions were collected. The relationships between the two tracers were evaluated.

Results

Among the 21 patients, 4 underwent imaging for initial disease staging, while the remaining 17 were imaged to detect recurrences. Patient-based analysis revealed that [⁶⁸Ga]Ga-HX01 PET/MR diagnostic performance was equivalent to [¹⁸F]FDG PET/CT in lesion detection (P = 1.0). The SUVmax value of [⁶⁸Ga]Ga-HX01 (4.64 ± 1.90) was significantly lower than that of [¹⁸F]FDG (9.43 ± 6.17, P = 0.002) across all patients. In terms of lesion-based analysis, [⁶⁸Ga]Ga-HX01 identified two additional lesions compared to [¹⁸F]FDG, though this difference was not statistically significant (94 vs. 92, P = 0.678). The SUVmax value for all lesions with [⁶⁸Ga]Ga-HX01 (3.47 ± 1.68) was also lower than that with [¹⁸F]FDG (5.82 ± 4.81, P = 0.003). Notably, [⁶⁸Ga]Ga-HX01 was preferred in patients receiving hematopoietic cytokines.

Conclusion

[⁶⁸Ga]Ga-HX01 PET offers comparable diagnostic efficacy to [¹⁸F]FDG PET/CT in sarcoma, with potential advantages in specific clinical scenarios. Larger cohorts are needed to validate these findings.

Clinical Trial Registration

NCT05490849 and NCT06416774.

Purpose

The impact of myocardial scar on coronary microcirculation is not well understood. This study aims to evaluate the association between microvascular resistance reserve (MRR) and scar tissue.

Methods

In this post-hoc analysis of the PACIFIC 2 trial, symptomatic patients with prior myocardial infarction (MI) and/or percutaneous coronary intervention (PCI) underwent [¹⁵O]H₂O positron emission tomography (PET), cardiac magnetic resonance (CMR) imaging, and fractional flow reserve (FFR). MRR was assessed utilizing PET-derived coronary flow reserve and FFR measurements. Scar quantification was assessed by CMR late gadolinium enhancement (LGE). Vessel LGE burden was defined as the scar tissue proportion in each myocardial territory. Total LGE burden was defined as the proportion of overall scar.

Results

The study included 154 patients with 397 vessels with a mean MRR of 3.56 ± 1.24. Patients with any scar tissues (LGE > 0%) exhibited a lower MRR in every myocardial territory than those without scar tissues. After adjusting for cardiovascular risk factors, either vessel LGE burden (β =-0.013, P = 0.006) or total LGE burden (β =-0.039, P = 0.002) independently predicted a reduced MRR. Compared to myocardial territories without scar tissues (LGE burdens = 0%), MRR was significantly lower in myocardial territories with vessel LGE burden = 0% + total LGE burden > 0%, and in myocardial territories with both LGE burdens > 0%.

Conclusion

Scar burden was negatively associated with MRR in patients with prior MI and/or PCI. Our findings indicate that both the proportion of myocardial scar in the vascular territory and the overall myocardial scar affect the microcirculation of individual vascular territories.

Clinical trial number

Not applicable.

Purpose

In order to limit climate changes, we need to reduce the carbon footprint of human activities, including those due to health systems. We performed an estimation of the carbon footprint of our nuclear medicine department using a methodology developed with the help of a specialized consulting firm.

Methods

The estimate of greenhouse gas (GHG) emissions comprises direct and indirect emissions. Direct emissions are due to fuels consumption (by the hospital and by hospital’s vehicles), refrigerant leaks and impact of buildings on biomass (land use change). Indirect emissions include upstream and downstream emissions. Upstream emissions are linked to electricity and heating consumption, transport of merchandises, transport of patients and employees, business travels, purchases, and fixed assets. Downstream emissions are due to usage and disposal of manufactured products created by the hospital. Different GHGs (CO₂, CH₄, N₂O…) each have a different global warming potential. To aggregate all GHG emissions, the results were expressed in carbon dioxide equivalent (CO2e).

Results

In 2022, 13,303 diagnostic and therapeutic procedures were performed in our department, for an estimated carbon footprint reaching 772 tons of CO2 equivalent. Transport of people accounts for 67% of total emissions. Purchases are responsible for 14% of total emissions, of which 11.8% are due to radiotracers supply. Energy consumption accounts for 6.9% of total emissions. Imaging devices (2 PET/CT, 2 SPECT/CT and 1 cardiac imaging dedicated CZT camera) account for 5.5% of emissions.

Conclusion

Our emissions are mainly due to indirect emission which is a common result in tertiary sector.

Background

Although the combined treatment with radiopharmaceutical therapy (RPT) and poly (ADP-ribose) polymerase inhibitors (PARPi) shows promise, a critical challenge remains in the limited quantitative understanding needed to optimize treatment protocols. This study introduces a mathematical model that quantitatively represents homologous recombination deficiency (HRD) and facilitates patient-specific customization of therapeutic schedules.

Methods

The model predicts therapeutic outcomes based on the absorbed dose by DNA and the resulting radiobiological responses, with DNA double-strand breaks (DSBs) being the critical determinant of cancer cell fate. The effect of PARPi is modeled by the accelerated conversion of single-strand breaks (SSBs) to DSBs due to PARP-trapping in the S phase, while HRD is represented by defects in DSB repair in replicated DNA. In vitro experiments are used to calibrate the model parameters and validate the model. In silico tests are designed to extensively investigate various combination protocols including the LuPARP trial.

Results

Model calibration was performed using data from the treatment of NCI-H69 cells with [¹⁷⁷Lu]Lu-DOTA-TOC and PARPi. Previously published in vivo studies were integrated into the presented model. Model validation using in vitro data showed deviations within the experimental error margins, with average deviations of 5.3 ± 3.2% without PARPi, 6.1 ± 4.4% with Olaparib, and 12 ± 18% with Rucaparib. Rucaparib radiosensitization reduces number of tumor cells during lutetium therapy by 99.2% and 99.99% (HRD). The highest radiosensitizing effect in vivo and in vitro was observed with Talazoparib (IC50: 4.8 nM), followed by Rucaparib (IC50: 1.4 µM). The model predicts relative tumor shrinkage after 14 days of combination treatment with Olaparib (250 mg) based on patient body weight (e.g. 60 kg: 99.6%; 90 kg: 98.0%).

Conclusion

Results demonstrate the potential of this computational model as a step toward the development of the digital twin for systematic exploration and optimization of clinical protocols.

Purpose

Positron Emission Tomography (PET) is a powerful molecular imaging tool that visualizes radiotracer distribution to reveal physiological processes. Recent advances in total-body PET have enabled low-dose, CT-free imaging; however, accurate organ segmentation using PET-only data remains challenging. This study develops and validates a deep learning model for multi-organ PET segmentation across varied imaging conditions and tracers, addressing critical needs for fully PET-based quantitative analysis.

Materials and methods

This retrospective study employed a 3D deep learning-based model for automated multi-organ segmentation on PET images acquired under diverse conditions, including low-dose and non-attenuation-corrected scans. Using a dataset of 798 patients from multiple centers with varied tracers, model robustness and generalizability were evaluated via multi-center and cross-tracer tests. Ground-truth labels for 23 organs were generated from CT images, and segmentation accuracy was assessed using the Dice similarity coefficient (DSC).

Results

In the multi-center dataset from four different institutions, our model achieved average DSC values of 0.834, 0.825, 0.819, and 0.816 across varying dose reduction factors and correction conditions for FDG PET images. In the cross-tracer dataset, the model reached average DSC values of 0.737, 0.573, 0.830, 0.661, and 0.708 for DOTATATE, FAPI, FDG, Grazytracer, and PSMA, respectively.

Conclusion

The proposed model demonstrated effective, fully PET-based multi-organ segmentation across a range of imaging conditions, centers, and tracers, achieving high robustness and generalizability. These findings underscore the model’s potential to enhance clinical diagnostic workflows by supporting ultra-low dose PET imaging.

Clinical trial number

Not applicable. This is a retrospective study based on collected data, which has been approved by the Research Ethics Committee of Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine.