European Journal of Nuclear Medicine and Molecular Imaging最新文献

{"title":"Estimation of carbon footprint in nuclear medicine: illustration of a french department","authors":"F. Godard, J. Oosthoek, A. Alexis, P. Léo, E. Fontaine, M. Dahmani, L. Houot, M. Quermonne, A. Cochet, Clément Drouet","doi":"10.1007/s00259-025-07129-x","DOIUrl":"https://doi.org/10.1007/s00259-025-07129-x","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>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).</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>Our emissions are mainly due to indirect emission which is a common result in tertiary sector.</p>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"31 1","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mathematical model for the investigation of combined treatment of radiopharmaceutical therapy and PARP inhibitors","authors":"Marc Ryhiner, Yangmeihui Song, Jimin Hong, Carlos Vinícius Gomes Ferreira, Axel Rominger, Susanne Kossatz, Gerhard Glatting, Wolfgang Weber, Kuangyu Shi","doi":"10.1007/s00259-025-07144-y","DOIUrl":"https://doi.org/10.1007/s00259-025-07144-y","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>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. <i>In silico</i> tests are designed to extensively investigate various combination protocols including the LuPARP trial.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>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%).</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>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.</p>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"51 1","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143495155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body PET imaging: a multi-center and cross-tracer study","authors":"Hanzhong Wang, Xiaoya Qiao, Wenxiang Ding, Gaoyu Chen, Ying Miao, Rui Guo, Xiaohua Zhu, Zhaoping Cheng, Jiehua Xu, Biao Li, Qiu Huang","doi":"10.1007/s00259-025-07156-8","DOIUrl":"https://doi.org/10.1007/s00259-025-07156-8","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Materials and methods</h3><p>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).</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Conclusion </h3><p>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.</p><h3 data-test=\"abstract-sub-heading\">Clinical trial number</h3><p>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.</p>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"1 1","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Standardized uptake value-based analysis of two-phase whole-body bone tomoscintigraphies recorded with a high-speed 360° CZT camera in patients with known or suspected inflammatory arthritis","authors":"Franklin Rajadhas, Laetitia Imbert, Mathilde Fiorino, Caroline Morizot, Victor Boucher, Zohra Lamiral, Véronique Roch, Pierre-Yves Marie, Damien Loeuille, Isabelle Chary-Valckenaere, Achraf Bahloul","doi":"10.1007/s00259-025-07150-0","DOIUrl":"https://doi.org/10.1007/s00259-025-07150-0","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>360° CZT-cameras provide whole-body bone SPECT/CT recordings at delayed (DEL) and blood-pool (BP) phases with short recording times but long visual analysis times. This study aims to determine whether a standardized uptake value (SUV)-based detection of inflammatory arthritis (IA) could facilitate this analysis.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We included 72 patients with known or suspected IA who underwent two-phase whole-body bone SPECT/CT after 550–650 MBq [^99mTc]Tc-HDP injection. Forty-eight patients also had ultrasound (US) for peripheral IA, and 42 had MRI for axial IA. The skeleton was segmented into 26 joint areas and analyzed by trained observers using a visual consensus methodology and SUVmax measurements.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>A total of 1836 joint areas were analyzed, including 1126 peripheral ones (limb joints excluding hips and shoulders). SUVmax was predictive of visually abnormal SPECT joints with high areas under receiver-operating-characteristic (ROC) curves for non-peripheral (BP-SPECT: 0.941 ± 0.017, DEL-SPECT: 0.910 ± 0.014) and especially peripheral (BP-SPECT: 0.980 ± 0.005, DEL-SPECT: 0.939 ± 0.012) joints. An SUVmax threshold-based prediction of visual SPECT abnormalities had high negative predictive values (BP-SPECT: 99.2% (1479/1491), DEL-SPECT: 97.2% (1333/1372)) but low positive predictive values (BP-SPECT: 35.1% (121/345), DEL-SPECT: 51.2% (237/463)). MRI- and US-defined IA were best predicted by a visually abnormal BP-SPECT due to higher specificities than SUVmax thresholds (all <i>p</i> &lt; 0.05).</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>On two-phase whole-body bone SPECT/CT, an SUVmax-based IA detection may not replace the conventional visual method. However, given the high negative predictive values provided by SUVmax thresholds, the time-consuming visual analysis of SPECT/CT slices could be confined to the small proportion of joints exceeding these thresholds.</p>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"11 1","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [18F]PSMA-1007 PET/CT and multiparametric MRI","authors":"Heng Lin, Fei Yao, Xinwen Yi, Yaping Yuan, Jian Xu, Lixuan Chen, Hongyan Wang, Yuandi Zhuang, Qi Lin, Yingnan Xue, Yunjun Yang, Zhifang Pan","doi":"10.1007/s00259-025-07134-0","DOIUrl":"https://doi.org/10.1007/s00259-025-07134-0","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. This study aims to develop and evaluate a multimodal deep learning model based on [¹⁸F]PSMA-1007 PET/CT and multiparametric MRI (mpMRI) to predict the presence of AP, and investigate whether the model that integrates [¹⁸F]PSMA-1007 PET/CT and mpMRI outperforms the individual PET/CT or mpMRI models in predicting AP.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>341 PCa patients who underwent radical prostatectomy (RP) with mpMRI and PET/CT scans were retrospectively analyzed. We generated deep learning signature from mpMRI and PET/CT with a multimodal deep learning model (MPC) based on convolutional neural networks and transformer, which was subsequently incorporated with clinical characteristics to construct an integrated model (MPCC). These models were compared with clinical models and single mpMRI or PET/CT models.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The MPCC model showed the best performance in predicting AP (AUC, 0.955 [95% CI: 0.932–0.975]), which is higher than MPC model (AUC, 0.930 [95% CI: 0.901–0.955]). The performance of the MPC model is better than that of single PET/CT (AUC, 0.813 [95% CI: 0.780–0.845]) or mpMRI (AUC, 0.865 [95% CI: 0.829–0.901]). Additionally, MPCC model is also effective in predicting single adverse pathological features.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>The deep learning model that integrates mpMRI and [¹⁸F]PSMA-1007 PET/CT enhances the predictive capabilities for the presence of AP in PCa patients. This improvement aids physicians in making informed preoperative decisions, ultimately enhancing patient prognosis.</p>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"49 1","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143443440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IRMA: Machine learning-based harmonization of $$^{18}$$ F-FDG PET brain scans in multi-center studies","authors":"S.S. Lövdal, R. van Veen, G. Carli, R. J. Renken, T. Shiner, N. Bregman, R. Orad, D. Arnaldi, B. Orso, S. Morbelli, P. Mattioli, K. L. Leenders, R. Dierckx, S. K. Meles, M. Biehl","doi":"10.1007/s00259-025-07114-4","DOIUrl":"https://doi.org/10.1007/s00259-025-07114-4","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>We demonstrate the use of the recently proposed machine learning method Iterated Relevance Matrix Analysis (IRMA) for harmonization of center-specific effects in brain <span>(^{18})</span>F-Fluorodeoxyglucose (<span>(^{18})</span>F-FDG) PET scans. The center difference is learned by applying IRMA on PCA-based feature vectors of healthy controls (HC), resulting in a subspace <span>(varvec{V})</span>, representing information not comparable between centers, and the remaining subspace <span>(varvec{U})</span>, where no center differences are present. In this proof-of-concept study, we demonstrate the properties of the method using data from four centers. After center-harmonization, a Generalized Matrix Learning Vector Quantization (GMLVQ) model was trained to discriminate between Parkinson’s disease, Alzheimer’s disease and Dementia with Lewy Bodies.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>At the initial IRMA iteration, the system was able to determine the center origin of the four HC cohorts almost perfectly. The method required six iterations, corresponding to a six-dimensional subspace <span>(varvec{V})</span>, to determine the entire center difference. An uncorrected disease classification model was highly biased to center-specific effects, creating a falsely inflated performance when applying internal (cross-) validation. The cross-validation performance of the center-harmonized model remained high, while it generalized significantly better to unseen test cohorts. Furthermore, the framework is highly transparent, providing analytic reconstructions of the correction and visualizations of the data in voxel space.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>IRMA can be used to learn and disregard center-specific information in features extracted from brain <span>(^{18})</span>F-FDG PET scans, while retaining disease-specific information.</p>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"25 1","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study","authors":"Daesung Kim, Kyobin Choo, Sangwon Lee, Seongjin Kang, Mijin Yun, Jaewon Yang","doi":"10.1007/s00259-025-07132-2","DOIUrl":"https://doi.org/10.1007/s00259-025-07132-2","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>Quantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ considerably. To address these problems, this study proposes a deep learning framework for translating CT of PET/CT to synthetic MR images (MR_SYN) and performing automated quantitative regional analysis using MR_SYN-derived segmentation.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>In this retrospective study, 139 subjects who underwent brain [¹⁸F]FBB PET/CT and T1-weighted MRI were included. A U-Net-like model was trained to translate CT images to MR_SYN; subsequently, a separate model was trained to segment MR_SYN into 95 regions. Regional and composite standardised uptake value ratio (SUVr) was calculated in [¹⁸F]FBB PET images using the acquired ROIs. For evaluation of MR_SYN, quantitative measurements including structural similarity index measure (SSIM) were employed, while for MR_SYN-based segmentation evaluation, Dice similarity coefficient (DSC) was calculated. Wilcoxon signed-rank test was performed for SUVrs computed using MR_SYN and ground-truth MR (MR_GT).</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>Compared to MR_GT, the mean SSIM of MR_SYN was 0.974 ± 0.005. The MR_SYN-based segmentation achieved a mean DSC of 0.733 across 95 regions. No statistical significance (<i>P</i> &gt; 0.05) was found for SUVr between the ROIs from MR_SYN and those from MR_GT, excluding the precuneus.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>We demonstrated a deep learning framework for automated regional brain analysis in PET/CT with MR_SYN. Our proposed framework can benefit patients who have difficulties in performing an MRI scan.</p>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"24 1","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reflections on Terbium-149: advancing preclinical research in targeted alpha therapy.","authors":"Serkan Kuyumcu, Yasemin Şanlı","doi":"10.1007/s00259-025-07151-z","DOIUrl":"https://doi.org/10.1007/s00259-025-07151-z","url":null,"abstract":"","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":" ","pages":""},"PeriodicalIF":8.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143440158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers","authors":"Abolfazl Mehranian, Scott D. Wollenweber, Kevin M. Bradley, Patrick A. Fielding, Martin Huellner, Andrei Iagaru, Meghi Dedja, Theodore Colwell, Fotis Kotasidis, Robert Johnsen, Floris P. Jansen, Daniel R. McGowan","doi":"10.1007/s00259-025-07119-z","DOIUrl":"https://doi.org/10.1007/s00259-025-07119-z","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Aim</h3><p>To evaluate a deep learning-based time-of-flight (DLToF) model trained to enhance the image quality of non-ToF PET images for different tracers, reconstructed using BSREM algorithm, towards ToF images.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>A 3D residual U-NET model was trained using 8 different tracers (FDG: 75% and non-FDG: 25%) from 11 sites from US, Europe and Asia. A total of 309 training and 33 validation datasets scanned on GE Discovery MI (DMI) ToF scanners were used for development of DLToF models of three strengths: low (L), medium (M) and high (H). The training and validation pairs consisted of target ToF and input non-ToF BSREM reconstructions using site-preferred regularisation parameters (beta values). The contrast and noise properties of each model were defined by adjusting the beta value of target ToF images. A total of 60 DMI datasets, consisting of a set of 4 tracers (¹⁸F-FDG, ¹⁸F-PSMA, ⁶⁸Ga-PSMA, ⁶⁸Ga-DOTATATE) and 15 exams each, were collected for testing and quantitative analysis of the models based on standardized uptake value (SUV) in regions of interest (ROI) placed in lesions, lungs and liver. Each dataset includes 5 image series: ToF and non-ToF BSREM and three DLToF images. The image series (300 in total) were blind scored on a 5-point Likert score by 4 readers based on lesion detectability, diagnostic confidence, and image noise/quality.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>In lesion SUV_max quantification with respect to ToF BSREM, DLToF-H achieved the best results among the three models by reducing the non-ToF BSREM errors from -39% to -6% for ¹⁸F-FDG (38 lesions); from -42% to -7% for ¹⁸F-PSMA (35 lesions); from -34% to -4% for ⁶⁸Ga-PSMA (23 lesions) and from -34% to -12% for ⁶⁸Ga-DOTATATE (32 lesions). Quantification results in liver and lung also showed ToF-like performance of DLToF models. Clinical reader resulted showed that DLToF-H results in an improved lesion detectability on average for all four radiotracers whereas DLToF-L achieved the highest scores for image quality (noise level). The results of DLToF-M however showed that this model results in the best trade-off between lesion detection and noise level and hence achieved the highest score for diagnostic confidence on average for all radiotracers.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>This study demonstrated that the DLToF models are suitable for both FDG and non-FDG tracers and could be utilized for digital BGO PET/CT scanners to provide an image quality and lesion detectability comparable and close to ToF.</p>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"35 1","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pathological and radiological features of false-positive lesions on [68Ga]Ga-PSMA-11 PET/CT in primary staging of prostate cancer: a radio-pathology matching analysis","authors":"Renjie Li, Yao Fu, Shan Peng, Fengjiao Yang, Shuyue Ai, Feng Wang, Shun Zhang, Hongqian Guo, Xuefeng Qiu","doi":"10.1007/s00259-025-07148-8","DOIUrl":"https://doi.org/10.1007/s00259-025-07148-8","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Purpose</h3><p>The aim of this study was to investigate the radiological and pathological characteristics of false-positive lesions on [⁶⁸Ga]Ga-PSMA-11 PET/CT during the primary staging of prostate cancer.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This study retrospectively analyzed 216 prostate cancer patients who had [⁶⁸Ga]Ga-PSMA-11 PET/CT before radical prostatectomy. False-positive lesion was defined as suspicious lesion with PRIMARY score ≥ 3 on PET/CT but benign on whole-mount pathology. To analyze the radiological and pathological features of false-positive lesions, no-uptake areas on PSMA PET/CT with benign pathology on the whole-mount specimen were randomly delineated and defined as true-negative lesions. The pathological features of false-positive and true-negative lesions were compared using Fisher’s exact test. The differences of SUVmax and SUVmean between false-positive and true-positive lesions were determined and compared using the Wilcoxon matched-pairs signed-ranks test. In addition, PSMA expression in false-positive lesions was assessed by immunohistochemistry.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>A total of 36 false-positive lesions were identified on [⁶⁸Ga]Ga-PSMA-11 PET/CT: 25 (69.44%) were simple atrophy with cyst formation, 7 (19.44%) were prostatic nodular hyperplasia, 3 (8.33%) were inflammation and 1 (2.78%) was normal glands. A comparable number of 36 true-negative lesions were delineated: 21 (58.33%) were normal glands, 8 (22.22%) were simple atrophy with cyst formation, 6 (16.67%) were prostatic nodular hyperplasia, and 1 (2.78%) were inflammation. The Fisher’s exact test revealed a statistically significant difference in the prevalence of simple atrophy with cyst formation between false-positive and true-negative lesions (69.44% vs. 22.22%; <i>P</i> &lt; 0.001). Differences in SUVmax and SUVmean between false-positive and true-positive lesions were also statistically significant (both <i>P</i> &lt; 0.001). PSMA expression in false-positive lesions was confirmed via immunohistochemistry.</p><h3 data-test=\"abstract-sub-heading\">Conclusion</h3><p>This study determined that simple atrophy with cyst formation is a distinctive pathological feature of false-positive lesions on [⁶⁸Ga]Ga-PSMA-11 PET/CT. This observation is likely attributable to the elevated PSMA expression in simple atrophy with cyst formation, as confirmed by histological analysis. Additionally, false-positive lesions were found to have significantly lower SUV compared to true-positive lesions.</p>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":"1 1","pages":""},"PeriodicalIF":9.1,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}