European Journal of Nuclear Medicine and Molecular Imaging最新文献

Purpose

Precise tumor excision is important but challenging in breast-conserving surgery (BCS). Tumor-specific fluorescence imaging may be used for intraoperative tumor detection and, therefore, to guide precise tumor excision. The aims of this study are to develop a glucose transporter 1 (GLUT1)-targeted near-infrared fluorescence tracer and evaluate its accuracy for breast cancer detection using fresh surgical breast specimens.

Methods

Bioinformatic analysis was performed to compare GLUT1 expression between breast cancer and normal breast tissues. A GLUT1-targeted fluorescence imaging tracer WZB117-CY7.5 was developed. In combination with fluorescence imaging (FMI), its binding specificity to GLUT1 was examined in in vitro breast cancer cell experiments, in vivo 4T1 breast tumor-bearing mouse models, and 60 freshly resected human breast tumor tissues. The diagnostic accuracy of WZB117-CY7.5, was evaluated in fresh specimens derived from 60 patients diagnosed with breast cancer.

Results

GLUT1 expression is higher in breast cancer tissues compared with normal tissues. WZB117-CY7.5 specifically bound to breast cancer cells in in vitro cell experiments and accumulated in tumor areas in a 4T1 tumor-bearing mice after intravenous injection by FMI. Moreover, WZB117-CY7.5 specifically bound to freshly resected human breast cancer and demonstrated excellent diagnostic performance in discriminating breast cancer, irrespective of cancer subtype, from normal breast tissue on fresh surgically resected breast tissues.

Conclusions

WZB117-CY7.5 showed high accuracy in intraoperative breast cancer detection, irrespective of the cancer subtype. This highlights its potential for clinical applications as a generic tracer for fluorescence image-guided surgery (FIGS) in BCS and fluorescence image-guided pathology for tissue sampling.

Purpose

We aimed to explore the value of [⁶⁸Ga]Ga‑FAPI PET/CT for the evaluation of Langerhans cell histiocytosis (LCH) in comparison with [¹⁸F]FDG PET/CT.

Methods

Thirty-two patients pathologically diagnosed with LCH were enrolled in this study. [⁶⁸Ga]Ga‑FAPI and [¹⁸F]FDG PET/CT were performed within 1 week to identify disease extent and status. The detectability and intensity of the involved organs were compared between these two tracers.

Results

Thirty patients had active disease while two had non-active disease. In patients with active disease, the most commonly involved organ was bone (27/30), and [⁶⁸Ga]Ga-FAPI PET/CT detected more osseous lesions (106/106) than [¹⁸F]FDG PET/CT (52/106). [⁶⁸Ga]Ga-FAPI also identified liver, skin, and salivary gland involvement, which were often missed by [¹⁸F]FDG. Although both tracers detected thymus and pituitary gland involvement, [⁶⁸Ga]Ga-FAPI demonstrated higher image contrast and more diagnostic confidence. Lymph node involvement, however, was not visualized by [⁶⁸Ga]Ga-FAPI. Due to the superior sensitivity of [⁶⁸Ga]Ga-FAPI, approximately 30% (10/30) of patients experienced reclassification in disease status or subtype. Furthermore, [⁶⁸Ga]Ga‑FAPI appeared to be advantageous in response assessment.

Conclusion

[⁶⁸Ga]Ga-FAPI PET/CT outperforms [¹⁸F]FDG PET/CT in detecting osseous and extra-nodal lesions in LCH, providing a valuable tool for precise disease evaluation and treatment planning.

Purpose

This study evaluated the differences in amyloid-β (Aβ), tau deposition, and longitudinal tau deposition between subjective cognitive decline (SCD) and objective subtle cognitive difficulties (Obj-SCD).

Methods

Participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort (n = 234) and the Huashan cohort (n = 267) included individuals with Obj-SCD, SCD, subjective memory concern (SMC), and healthy controls (HC). General linear models (GLM) were used to compare baseline and longitudinal differences in Aβ and tau among the groups, and to examine the associations between these biomarkers.

Results

Obj-SCD participants had significantly higher Aβ and tau levels compared to HC, with increased annual accumulation of Aβ and tau, especially in Braak stages III-IV. In contrast, SCD/SMC participants did not show significant differences from HC in annual Aβ and tau changes. Baseline Aβ PET correlated with annual tau PET changes in Obj-SCD (Braak III-VI) and SCD/SMC (all Braak stages), and baseline Aβ levels strongly predicted early memory decline in both Obj-SCD and SCD/SMC groups.

Conclusion

This study indicates that Obj-SCD is more strongly associated with Aβ and tau biomarkers, with Aβ contributing to the progression of tau pathology and memory decline. Further research should include more longitudinal data to more robustly validate these findings and clarify the temporal relationship between Aβ and tau in preclinical Alzheimer’s disease.

Purpose

To investigate the efficacy of [⁶⁸Ga]Ga-FAPI-04 PET/CT for assessing viable tumours (VTs) after local regional treatment (LRT) in hepatocellular carcinoma (HCC) patients. The related imaging features of HCC after LRT are preliminarily discussed.

Methods

A cohort of 37 LRT patients with HCC (encompassing 51 lesions) was retrospectively included from a prospective parent study (ChiCTR2000039099), and sequential PET/CT using [¹⁸F]FDG and [⁶⁸Ga]Ga-FAPI-04 was performed. The diagnostic accuracies of [⁶⁸Ga]Ga-FAPI-04 and [¹⁸F]FDG PET/CT and multiphasic CT/MRI for detecting VTs after LRT were calculated and analysed. Pathological examination was considered the gold standard for VT diagnosis, and clinical follow-up was used as the reference standard. The SUVmax and tumour-to-background ratio (TBR) derived from [¹⁸F]FDG and [⁶⁸Ga]Ga-FAPI-04 PET/CT were calculated and compared. Moreover, the SUVmax, target-to-normal liver ratio (TNR) of VT, tumour necrosis (TN), benign rim (BR), and normal liver (NL) from different imaging modalities after LRT for HCC were compared.

Results

Both the sensitivity (96.0% [24/25] vs. 36.0% [9/25], p < 0.001) and accuracy (94.1%, [48/51] vs. 68.6% [35/51], p = 0.004) of [⁶⁸Ga]Ga-FAPI-04 PET/CT for detecting VTs after LRT were greater than those of [¹⁸F]FDG PET/CT, whereas their specificities were comparable (92.3% [24/26] vs. 100% [26/26]). Notably, [⁶⁸Ga]Ga-FAPI-04 PET/CT had a greater SUVmax (9.80 vs. 3.60) and TBR (9.93 vs. 1.57) than [¹⁸F]FDG PET/CT in VT patients (all p < 0.001). Furthermore, VT had a greater SUVmax and TNR than did TN, BR, and NL on [⁶⁸Ga]Ga-FAPI-04 PET/CT (all p < 0.001) and exhibited morphologic nodular, mass-like, or irregular tracer uptake. Although no significant differences were observed for VT detection (all p > 0.05), [⁶⁸Ga]Ga-FAPI-04 PET/CT and multiphasic CT/MRI complemented each other in some cases.

Conclusion

[⁶⁸Ga]Ga-FAPI-04 PET/CT not only presented higher sensitivity and accuracy than [¹⁸F]FDG PET/CT for diagnosing VTs after LRT for HCC but also showed comparable diagnostic accuracy and complementary roles with multiphasic CT/MRI. Overall, [⁶⁸Ga]Ga-FAPI-04 PET/CT may play an essential role in surveillance after LRT for HCC.

Trial registration

Chinese Clinical Trial Registry ChiCTR2000039099. Registered 17 October 2020.

Introduction

The intricate interplay between organs can give rise to a multitude of physiological conditions. Disruptions such as inflammation or tissue damage can precipitate the development of chronic diseases such as tumors or diabetes mellitus (DM). While both lung cancer and DM are the consequences of disruptions in homeostasis, the relationship between them is intricate. This study sought to investigate the potential influence of DM on lung cancer by employing total-body dynamic PET imaging.

Methods

The present study proposes a framework for metabolic network analysis using total-body dynamic PET imaging of 20 lung cancer patients with DM (DM group) and 20 lung cancer patients without DM (Non-DM group), with the residuals of a third-order polynomial fit serving as an indicator of Pearson correlation.

Results

The framework successfully captured the deviation of the DM group from the Non-DM group at both the edge and organ levels. At the edge level, there was a significant difference in the lesion- left ventricle (LV) between the DM and Non-DM groups (P < 0.05). Furthermore, we discovered a positive correlation between the absolute value of Z-score (ZCC) of lesion - LV and the duration of DM (R = 0.680, P < 0.001). At the organ level, there was a significant difference in the kidney, brain, and abdominal fat between the DM and Non-DM groups (P < 0.05).

Conclusion

This study demonstrated the feasibility of constructing metabolic networks to uncover complex alterations in lung cancer patients with DM. The findings contribute to understanding the systemic effects of DM on lung cancer metabolism and highlight the importance of personalized metabolic network analysis to comprehend the implications of concurrent diseases.

Purpose

Patients who develop metastatic melanoma have a very poor prognosis, and new treatments are needed to improve the response rates. Melanocortin-1 receptor (MC1R) is a promising target for radionuclide therapy of metastatic melanoma, and alpha-melanocyte stimulating hormone (α-MSH) peptide analogs show high affinities to MC1Rs. Because targeted alpha therapy (TAT) can be a desirable treatment for metastatic melanoma, this study aimed to develop an ²¹¹At-labeled α-MSH peptide analog for TAT of metastatic melanoma.

Methods

We designed an α-MSH analog labeled with ²¹¹At using a neopentyl glycol scaffold via a hydrophilic linker. Preliminary studies using ¹²⁵I-labeled α-MSH analogs were performed to identify suitable hydrophilic linkers. Then, [²¹¹At]NpG-GGN4c was prepared using a procedure similar to that of the ¹²⁵I-labeled counterpart, [¹²⁵I]NpG-GGN4b. The biodistribution profile of [²¹¹At]NpG-GGN4c in B16F10 tumor-bearing mice was compared with that of [¹²⁵I]NpG-GGN4b. B16F10 tumor-bearing mice were treated with a single dose of vehicle or [²¹¹At]NpG-GGN4c (1 or 0.4 MBq).

Results

The D-Glu-D-Arg linker was identified as the optimal hydrophilic linker because of its high affinity for MC1R and good biodistribution profile, especially with low accumulation in the liver and intestine. [²¹¹At]NpG-GGN4c showed tumor accumulation comparable to that of [¹²⁵I]NpG-GGN4b and maintained the tumor radioactivity retention from 1 to 3 h postinjection. [²¹¹At]NpG-GGN4c exhibited a dose-dependent inhibitory effect on B16F10 xenograft growth without apparent body weight loss.

Conclusion

[²¹¹At]NpG-GGN4c showed dose-dependent efficacy against B16F10 xenografts, suggesting that [²¹¹At]NpG-GGN4c is a promising TAT agent for treating metastatic melanoma.

Purpose

Dopamine transporter [¹¹C]CFT PET is highly effective for diagnosing Parkinson’s Disease (PD), whereas it is not widely available in most hospitals. To develop a deep learning framework to synthesize [¹¹C]CFT PET images from real [¹⁸F]FDG PET images and leverage their cross-modal correlation to distinguish PD from normal control (NC).

Methods

We developed a deep learning framework to synthesize [¹¹C]CFT PET images from real [¹⁸F]FDG PET images, and leveraged their cross-modal correlation to distinguish PD from NC. A total of 604 participants (274 with PD and 330 with NC) who underwent [¹¹C]CFT and [¹⁸F]FDG PET scans were included. The quality of the synthetic [¹¹C]CFT PET images was evaluated through quantitative comparison with the ground-truth images and radiologist visual assessment. The evaluations of PD diagnosis performance were conducted using biomarker-based quantitative analyses (using striatal binding ratios from synthetic [¹¹C]CFT PET images) and the proposed PD classifier (incorporating both real [¹⁸F]FDG and synthetic [¹¹C]CFT PET images).

Results

Visualization result shows that the synthetic [¹¹C]CFT PET images resemble the real ones with no significant differences visible in the error maps. Quantitative evaluation demonstrated that synthetic [¹¹C]CFT PET images exhibited a high peak signal-to-noise ratio (PSNR: 25.0–28.0) and structural similarity (SSIM: 0.87–0.96) across different unilateral striatal subregions. The radiologists achieved a diagnostic accuracy of 91.9% (± 2.02%) based on synthetic [¹¹C]CFT PET images, while biomarker-based quantitative analysis of the posterior putamen yielded an AUC of 0.912 (95% CI, 0.889–0.936), and the proposed PD Classifier achieved an AUC of 0.937 (95% CI, 0.916–0.957).

Conclusion

By bridging the gap between [¹⁸F]FDG and [¹¹C]CFT, our deep learning framework can significantly enhance PD diagnosis without the need for [¹¹C]CFT tracers, thereby expanding the reach of advanced diagnostic tools to clinical settings where [¹¹C]CFT PET imaging is inaccessible.

Purpose

To report real-world clinical experience with [¹⁷⁷Lu]Lu-PSMA-I&T targeted radionuclide therapy (TRT) in patients with metastatic castration-resistant prostate cancer (mCRPC) in a single tertiary referral university hospital.

Methods

Patients with mCRPC who were treated with [¹⁷⁷Lu]Lu-PSMA-I&T TRT as standard of care between February 2022 and August 2023 were included in this retrospective study. Patients were treated with a maximum of six cycles with a fixed activity of 7.4 GBq/100µg [¹⁷⁷Lu]Lu-PSMA-I&T per cycle.

Results

50 patients with mCRPC were included, of them 84% had prior therapy with two lines of taxane-based chemotherapy treated and at least one line of androgen receptor signaling inhibitor. A total of 126 cycles with a median of 2 cycles (IQR 1–6) [ ¹⁷⁷Lu]Lu-PSMA-I&T were administered per patient. PSA declines of ≥ 50% and ≥ 70% were achieved in 16% and 10% of the patients, respectively. Radiological response was achieved in 11% of the patients. In total, 68 treatment-related Adverse Events (TRAEs) were observed, mainly grade 1–2 in 88% of cases. Grade 3/4 TRAEs were observed in 12% of cases. No grade 3 or higher xerostomia was reported. Median progression-free survival was 7.7 months (95% CI 4.0-11.3) and median overall survival was 8.1 months (95% CI 5.0-11.3).

Conclusion

In heavily pretreated patients with mCRPC, treatment of [¹⁷⁷Lu]Lu-PSMA-I&T TRT is well tolerated and safe, but real-world efficacy of [¹⁷⁷Lu]Lu-PSMA appears lower compared to data from recent phase-3 clinical trials using a different radioligand [¹⁷⁷Lu]Lu-PSMA-617. Further studies may show whether patients with mCRPC benefit more from [¹⁷⁷Lu]Lu-PSMA when initiated at an earlier stage of treatment.

Purpose

Radiomics-based machine learning (ML) models of amino acid positron emission tomography (PET) images have shown efficiency in glioma prediction tasks. However, their clinical impact on physician interpretation remains limited. This study investigated whether an explainable radiomics model modifies nuclear physicians’ assessment of glioma aggressiveness at diagnosis.

Methods

Patients underwent dynamic 6-[¹⁸F]fluoro-L-DOPA PET acquisition. With a 75%/25% split for training (n = 63) and test sets (n = 22), an ensemble ML model was trained using radiomics features extracted from static/dynamic parametric PET images to classify lesion aggressiveness. Three explainable ML methods—Local Interpretable Model-agnostic Explanations (LIME), Anchor, and SHapley Additive exPlanations (SHAP)—generated patient-specific explanations. Eighteen physicians from eight institutions evaluated the test samples. During the first phase, physicians analyzed the 22 cases exclusively through magnetic resonance and static/dynamic PET images, acquired within a maximum interval of 30 days. In the second phase, the same physicians reevaluated the same cases (n = 22), using all available data, including the radiomics model predictions and explanations.

Results

Eighty-five patients (54[39–62] years old, 41 women) were selected. In the second phase, physicians demonstrated a significant improvement in diagnostic accuracy compared to the first phase (0.775 [0.750–0.802] vs. 0.717 [0.694–0.737], p = 0.007). The explainable radiomics model augmented physician agreement, with a 22.72% increase in Fleiss’s kappa, and significantly enhanced physician confidence (p < 0.001). Among all physicians, Anchor and SHAP showed efficacy in 75% and 72% of cases, respectively, outperforming LIME (p ≤ 0.001).

Conclusions

Our results highlight the potential of an explainable radiomics model using amino acid PET scans as a diagnostic support to assist physicians in identifying glioma aggressiveness.