European Journal of Nuclear Medicine and Molecular Imaging最新文献

Purpose

The aim of this study was to validate simplified methods for quantifying [⁶⁸Ga]Ga-FAPI-46 uptake against full pharmacokinetic modeling.

Methods

Ten patients with pancreatobiliary cancer underwent a 90-min dynamic PET/CT scan using a long axial field of view system. Arterial blood samples were used to establish calibrated plasma-input function from both continuous arterial sampling and image-derived input function (IDIF). Lesional [⁶⁸Ga]Ga-FAPI-46 kinetics were described using conventional non-linear plasma-input tissue-compartment models. Logan plots using 30–90 min and 30–60 min post-injection (p.i), image-based target-to-whole blood ratio (TBR), mean standardized uptake values (SUVmean) normalized to body weight, lean body mass, and body surface area, at 20–30 min, 60–70 min and 80–90 min p.i were assessed.

Results

One patient was excluded due to discontinued scan acquisition and missing arterial sampling. Thirteen tumoral lesions and 11 non-tumoral lesions were included. A reversible 2-tissue-compartment model showed most preferrable fits for all types of [⁶⁸Ga]Ga-FAPI-46 positive lesions. The distribution volume (V_T) results obtained using arterial sampling plasma-input function and those using plasma-IDIF (V_{T_plasma_IDIF}) showed an excellent correlation (Spearman rank correlation coefficient (r_s) = 0.949). Logan V_T using both time intervals were highly correlated with V_{T_plasma_IDIF} (r_s ≥ 0.938). The correlation values with V_{T_plasma_IDIF} for image-based TBR and SUVmean parameters were higher at 80–90 min (r_s ≥ 0.839) and at 60–70 min (r_s ≥ 0.835) p.i than at 20–30 min p.i (r_s ≤ 0.774).

Conclusion

Image-based TBR and SUVmean at 60–70 min p.i are suitable for quantifying [⁶⁸Ga]Ga-FAPI-46 uptake.

Trial registration

EudraCT, EudraCT 2022-001867-29. Registered 02 November 2022.

Purpose

Long axial field-of-view (LAFOV) positron emission tomography/computed tomography (PET/CT) scanners enable high sensitivity and wide anatomical coverage. Therefore, they seem ideal to perform post-selective internal radiation therapy (SIRT) ⁹⁰Y scans, which are needed, to confirm that the dose is delivered to the tumors and that healthy organs are spared. However, it is unclear to what extent the use of LAFOV PET is feasible and which dosimetry approaches results in accurate measurements.

Methods

In this retrospective analysis, a total number of 32 patients was included (median age 71, IQR 14), which had hepatocellular carcinoma, cholangiocarcinoma, or liver metastases. All patients underwent SIRT, and the post-therapy scan was acquired on a single photon emission computed tomography/computed tomography (SPECT/CT) and a LAFOV Biograph Quadra PET/CT with a 20-minute acquisition time. Post-treatment dosimetry, regarding the tumor, whole-liver and lung (LMD) absorbed dose was done using an organ-wise (Simplicit90Y) and a voxel-wise approach (HERMIA Dosimetry) which used a semi-Monte Carlo algorithm. The lung shunt fraction (LSF) was also measured using the voxel-wise approach and compared to the planned.

Results

The planning, post-treatment SPECT and PET (SPECT_pre, SPECT_post, PET_post) median tumor doses based on the organ-wise dosimetry were 276.0 Gy (200.0–330.0 Gy), 232.0 Gy (158.5–303.5 Gy) and 267.5 Gy (182.5–370.8 Gy). In contrast, the median voxel-wise PET_post dose was significantly smaller than the planned SPECT_pre (152.5 Gy (94.8–223.8 Gy); p < 0.00001). Moreover, the median tumor absorbed dose at 90% (D90) of the tumor volume was significantly higher in SPECT_post compared with PET_post (123.5 Gy; 81.5–180.0 vs. 30.5 Gy; 11.3-106.3; p < 0.00001). The PET_post measured LSF was significantly lower compared to the planned SPECT_pre (0.89%; 0.4–1.3% vs. 2.3%; 1.5–3.6%; p < 0.0001). Similarly, the measured PET_post median LMD was considerably lower to the planned SPECT_pre (1.2 Gy; 0.6–2.3 vs. 2.5 Gy; 1.4–4.7; p < 0.0001).

Conclusion

LAFOV PET enabled the direct measurement of post therapy lung dose and tumor doses that correlated well with the planned treatment doses. However, current voxel-wise-based tumor dosimetry seems to be inaccurate for LAFOV PET. In addition, dose volume histogram-based metrics also significantly underestimate the delivered dose. Therefore, improved dosimetry tools are needed for reliable voxel-wise ⁹⁰Y dosimetry to leverage the sensitivity and spatial resolution of LAFOV PET scanners.

Purpose

Nanoparticles are highly efficient vectors for ferrying contrast agents across cell membranes, enabling ultra-sensitive in vivo tracking of single cells with positron emission tomography (PET). However, this approach must be fully characterized and understood before it can be reliably implemented for routine applications.

Methods

We developed a Langmuir adsorption model that accurately describes the process of labeling mesoporous silica nanoparticles (MSNP) with ⁶⁸Ga. We compared the binding efficiency of three different nanoparticle systems by fitting the model to experimental data. We then chose the MSNP with the highest affinity for ⁶⁸Ga to study uptake and efflux kinetics in cancer cells. After intracardiac injection of 50–100 cells in mice, PET imaging was performed to test the effectiveness of cellular radiolabeling.

Results

We found that highly porous mesoporous nanoparticles (d = 100 nm) with MCM-41 pore structures can achieve radiolabeling efficiency > 30 GBq/mg using ⁶⁸Ga, without the need for any chelator. These ⁶⁸Ga conjugated particles showed strong serum stability in vitro. In mice, the ⁶⁸Ga-MSNPs predominantly accumulated in the liver with a high signal-to-background ratio and no bladder signal, indicating excellent stability of the labeled nanoparticles in vivo. Additionally, these MSNPs were efficiently taken up by B16F10 and MDA-MB-231 cancer cells, as confirmed by confocal imaging, flow cytometry analysis, and gamma counting. Finally, cardiac injection of < 100 ⁶⁸Ga-MSNP-labeled cells allowed PET/CT tracking of these cells in various organs in mice.

Conclusion

We characterized the critical parameters of MSNP-mediated direct cellular radiolabeling to improve the use of these nanoparticles as cellular labels for highly sensitive preclinical PET imaging.

Purpose

Fluorine-18 prostate-specific membrane antigen-1007 positron emission tomography/computed tomography (¹⁸F-PSMA-1007 PET/CT) has been shown to be superior to multiparametric magnetic resonance imaging (MRI) for the locoregional staging of intermediate-risk and high-risk prostate tumors. This study aims to evaluate whether it is also superior in estimating tumor parameters, such as three-dimensional spatial localization and volume.

Methods

134 participants underwent ¹⁸F-PSMA-1007 PET/CT and MRI prior to radical prostatectomy as part of the validating paired-cohort Next Generation Trial (NCT05141760). MRI, ¹⁸F-PSMA-1007 PET/CT, and final pathology were independently assessed by blinded radiologists, nuclear medicine physicians, and pathologists, respectively. Individual tumor nodules were measured in three dimensions and cognitively registered to 38 segment prostate diagrams as per PI-RADSv2.1. Correct spatial localization was compared using McNemar test and estimation of tumor volumes were compared using linear regression and partial F-test.

Results

286 tumor nodules were identified by final histopathology. ¹⁸F-PSMA-1007 PET/CT was superior to MRI for correct localization (186 [65.0%] vs 134 [46.9%], p < 0.001) and tumor volume estimation (R² = 0.545 vs 0.431, p < 0.001). Larger tumors and higher Gleason Grade Group (GGG) were associated with correct localization by ¹⁸F-PSMA-1007 PET/CT (OR = 2.05, p < 0.001 for tumor volume and OR = 4.92, p < 0.01 for ≥ GGG3) and MRI (OR = 1.81, p < 0.001 for tumor volume and OR = 11.67, p < 0.001 for ≥ GGG3).

Conclusion

¹⁸F-PSMA-1007 PET/CT outperforms MRI for determination of three-dimensional spatial localization and volume of prostate tumors. These findings support the use of ¹⁸F-PSMA-1007 PET/CT prior to definitive treatment of localized prostate cancers.

Background

The design of smart, photoactivated nanomaterials for targeted drug delivery systems (DDS) has garnered significant research interest due in part to the ability of light to precisely control drug release in specific cells or tissues with high spatial and temporal resolution. The development of effective light-triggered DDS involves mechanisms including photocleavage, photoisomerization, photopolymerization, photosensitization, photothermal phenomena, and photorearrangement, which permit response to ultraviolet (UV), visible (Vis), and/or Near Infrared (NIR) light. This review explores recent advancements in light-responsive small molecules, polymers, and nanocarriers, detailing their underlying mechanisms and utility for drug delivery and/or imaging. Furthermore, it highlights key challenges and future perspectives in the development of light-triggered DDS, emphasizing the potential of these systems to revolutionize targeted therapies.

Method

A systematic literature search was performed using Google Scholar as the primary database and information source. We searched the recently published literature (within 15 years) with the following keywords individually and in relevant combinations: light responsive, nanoparticle, drug release, mechanism, photothermal, photosensitization, photopolymerization, photocleavage, and photoisomerization.

Results

We selected 117 scientific articles to assess the strength of evidence after screening titles and abstracts. We found that six mechanisms (photocleavage, photoisomerization, photopolymerization, photosensitization, photothermal phenomena, and photorearrangement) have primarily been used for light-triggered drug release and categorized our review accordingly. Azobenzene/spiropyran-based derivatives and o-nitrobenzyl/Coumarin derivatives are often used for photoisomerization and photocleavage-enabled drug delivery, while free radical polymerization and cationic polymerization comprise two main mechanisms of photopolymerization. One hundred two is the primary active radical oxygen species employed for photosensitization, which is a key factor that impacts the therapeutic effects in Photodynamic therapy, but not in photothermal therapy.

Conclusion

The comprehensive review serves as a guiding compass for light-triggered DDS for biomedical applications. This rapidly advancing field is poised to generate breakthroughs for disease diagnosis and treatment.

Purpose

Extranodal natural killer/T-cell lymphoma (ENKTCL) is an hematologic malignancy with prognostic heterogeneity. We aimed to develop and validate DeepENKTCL, an interpretable deep learning prediction system for prognosis risk stratification in ENKTCL.

Methods

A total of 562 patients from four centers were divided into the training cohort, validation cohort and test cohort. DeepENKTCL combined a tumor segmentation model, a PET/CT fusion model, and prognostic prediction models. RadScore and TopoScore were constructed using radiomics and topological features derived from fused images, with SHapley Additive exPlanations (SHAP) analysis enhancing interpretability. The final prognostic models, termed FusionScore, were developed for predicting progression-free survival (PFS) and overall survival (OS). Performance was assessed using area under the receiver operator characteristic curve (AUC), time-dependent C-index, clinical decision curves (DCA), and Kaplan-Meier (KM) curves.

Results

The tumor segmentation model accurately delineated the tumor lesions. RadScore (AUC: 0.908 for PFS, 0.922 for OS in validation; 0.822 for PFS, 0.867 for OS in test) and TopoScore (AUC: 0.756 for PFS, 0.805 for OS in validation; 0.689 for PFS, 0.769 for OS in test) both exhibited potential prognostic capability. The time-dependent C-index (0.897 for PFS, 0.928 for OS in validation; 0.894 for PFS, 0.868 for OS in test) and DCA indicated that FusionScore offers significant prognostic performance and superior net clinical benefits compared to existing models. KM survival analysis showed that higher FusionScores correlated with poorer PFS and OS across all cohorts.

Conclusion

DeepENKTCL provided a robust and interpretable framework for ENKTCL prognosis, with the potential to improve patient outcomes and guide personalized treatment.