Revista espanola de medicina nuclear e imagen molecular最新文献

{"title":"Correlation between F-18 FDG PET/CT-derived metabolic parameters and PD-L1 expression in non-small cell lung cancer patients","authors":"S. Kesim,&nbsp;F. Ozulker,&nbsp;G. Gul Gecmen,&nbsp;N. Turan","doi":"10.1016/j.remnie.2025.500258","DOIUrl":"10.1016/j.remnie.2025.500258","url":null,"abstract":"<div><h3>Objectives</h3><div>Programmed death-ligand 1 (PD-L1) expression serves as a critical biomarker for selecting patients eligible for treatment with immune checkpoint inhibitors. Herein, we investigated the association between PD-L1 expression and various FDG PET/CT–derived metabolic parameters in patients with non-small cell lung cancer (NSCLC).</div></div><div><h3>Materials and methods</h3><div>This retrospective study included 81 NSCLC patients who underwent pre-treatment F-18 FDG PET/CT imaging and histopathological evaluation of PD-L1 expression. PD-L1 tumour proportion score (TPS) was determined using the SP263 immunohistochemical assay. PD-L1 positivity was defined as TPS ≥ 1%. Quantitative PET/CT parameters—SUVmax, SUVmean, SULpeak, SULmax, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity indices (coefficient of variation [COV] and SUV-based heterogeneity index [HI])—were analyzed in relation to PD-L1 TPS.</div></div><div><h3>Results</h3><div>PD-L1 positivity was identified in 30 patients (37%). Although SUVmax, SUVmean, SULpeak, and SULmax values tended to be higher in PD-L1–positive patients, these differences were not statistically significant. Conversely, MTV and TLG were higher in the PD-L1–negative group. Among all parameters, HI was significantly elevated in the PD-L1–positive group (<em>P</em> = .031), and remained significant across PD-L1 expression strata (<em>P</em> = .037). In metastatic patients, HI and COV showed significant positive correlation with PD-L1 expression (r = 0.34 and 0.33, respectively). ROC analysis identified a HI cut-off of 1.59 to predict PD-L1 positivity with 90% sensitivity and 50% specificity (AUC = 0.674).</div></div><div><h3>Conclusions</h3><div>Tumor heterogeneity indices, particularly HI and COV derived from FDG PET/CT, demonstrated stronger predictive value for PD-L1 expression than conventional metabolic parameters. These findings suggest that metabolic heterogeneity may serve as a useful noninvasive imaging biomarker for guiding immunotherapy in NSCLC.</div></div>","PeriodicalId":94197,"journal":{"name":"Revista espanola de medicina nuclear e imagen molecular","volume":"45 4","pages":"Article 500258"},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145644099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of PD-L1 expression in gastric cancer using ¹⁸F-FDG PET/CT radiomics.","authors":"Q Lyu, D Lin, S Li, J Zhang, W Chen, D Chen, X Pan, D Liu","doi":"10.1016/j.remnie.2026.500324","DOIUrl":"10.1016/j.remnie.2026.500324","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate the predictive performance of radiomics models based on ¹⁸F-FDG PET/CT for determining programmed cell death ligand-1 status in treatment-naïve gastric cancer.</p><p><strong>Methods: </strong>This retrospective study included 170 gastric cancer patients who underwent preoperative ¹⁸F-FDG PET/CT. Patients were randomly assigned to training (n = 119) and testing (n = 51) cohorts. PD-L1 positivity was defined as a combined positive score ≥5. Radiomic features were extracted from PET, CT, and fused PET/CT images and processed through a comprehensive pipeline with multiple methods employed for normalization, preprocessing, and feature selection. Each pipeline retained 20 radiomic features, which were subsequently evaluated with 10 different classifiers using feature subsets ranging from 1 to 20 to identify the optimal radiomics model. Model performance was assessed by AUC, accuracy, and related metrics, with 5-fold cross-validation on the training set.</p><p><strong>Results: </strong>The integrated PET/CT model achieved the best performance on the test set (AUC 0.750; accuracy 0.745). The CT-based model yielded a test AUC of 0.678 and accuracy of 0.762, while the PET-based model showed a test AUC of 0.653 and accuracy of 0.706. All three radiomics models outperformed the conventional metabolic-parameter model (AUC 0.578; accuracy 0.544). The optimal predictive pipeline varied across the different imaging modalities.</p><p><strong>Conclusion: </strong>Integrated ¹⁸F-FDG PET/CT radiomics demonstrated superior potential for noninvasive prediction of PD-L1 expression in gastric adenocarcinoma compared with single-modality imaging or metabolic parameters, supporting its potential utility for guiding patient selection for immunotherapy, with further validation warranted.</p>","PeriodicalId":94197,"journal":{"name":"Revista espanola de medicina nuclear e imagen molecular","volume":" ","pages":"500324"},"PeriodicalIF":0.0,"publicationDate":"2026-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of Nuclear Medicine in Multidisciplinary Committees: Involvement, perception, and areas for improvement. Results of a national survey.","authors":"J R Garcia, V Vallejos, A Niñerola, R Jover","doi":"10.1016/j.remnie.2026.500327","DOIUrl":"10.1016/j.remnie.2026.500327","url":null,"abstract":"<p><strong>Objective: </strong>To analyze the participation of Nuclear Medicine specialists in multidisciplinary committees (MCs) in Spain, assessing their level of involvement, perception of performance, main challenges, and areas for improvement.</p><p><strong>Method: </strong>A descriptive, cross-sectional observational study was conducted using a structured survey designed by the Management and Quality Working Group (GTGyC) of the Spanish Society of Nuclear Medicine and Molecular Imaging (SEMNIM). The questionnaire included closed and open-ended questions about professional profile, participation in MCs, their organization, available resources, perceived barriers, and satisfaction level. The survey was distributed through SEMNIM's institutional platform and via email to all members and registered services. Responses were collected between January and September 2025.</p><p><strong>Results: </strong>A total of 86 responses were analyzed: 81 specialists (94.2%) and 5 residents (5.8%). Most worked in the public healthcare sector (93%) and had more than five years of experience (70.9%). 94.2% participate in oncology committees, and 32.6% in more than three committees. 97.7% attend at least one weekly session, dedicating between 10% and 30% of their working time to preparation. Overall satisfaction with MC performance was high (87.2% scored >7). However, relevant areas for improvement were identified, mainly in case management (59.4%), available infrastructure (51.3%), and interprofessional coordination (43%). 91.9% consider the representation of specialties adequate, and 74.4% confirmed the existence of standardized protocols. Likewise, 87.7% perceived a positive impact of their participation on diagnostic quality and clinical outcomes.</p><p><strong>Conclusion: </strong>Nuclear Medicine plays an essential role in MCs, contributing significantly to multidisciplinary decision-making. Nevertheless, organizational challenges and resource limitations persist and must be addressed to optimize efficiency and ensure quality care.</p>","PeriodicalId":94197,"journal":{"name":"Revista espanola de medicina nuclear e imagen molecular","volume":" ","pages":"500327"},"PeriodicalIF":0.0,"publicationDate":"2026-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bilateral renoureteral plasmacytoma. An unusual presentation in a young patient with multiple myeloma.","authors":"S Ruiz, A Compte, L L Mont-Castro, L Pinilla, D Tovar, E Riera, J R Garcia","doi":"10.1016/j.remnie.2026.500308","DOIUrl":"10.1016/j.remnie.2026.500308","url":null,"abstract":"","PeriodicalId":94197,"journal":{"name":"Revista espanola de medicina nuclear e imagen molecular","volume":" ","pages":"500308"},"PeriodicalIF":0.0,"publicationDate":"2026-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148019867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The nuclear oncologist.","authors":"J R Garcia, P Paredes, E Riera, D Fuster","doi":"10.1016/j.remnie.2026.500326","DOIUrl":"10.1016/j.remnie.2026.500326","url":null,"abstract":"","PeriodicalId":94197,"journal":{"name":"Revista espanola de medicina nuclear e imagen molecular","volume":" ","pages":"500326"},"PeriodicalIF":0.0,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147960588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic value of radionics based on ¹⁸F-FDG PET/CT in Hodgkin lymphoma: A systematic review.","authors":"C Pradère, J Mucientes Rasilla, B Navarro Matilla, P Manuel Meneses Soares, K Velásquez Díaz, I Garrido Solesio, A Grajeda Gallardo, M Del Pilar Estrella Caballero, M Mitjavila Casanovas","doi":"10.1016/j.remnie.2026.500323","DOIUrl":"10.1016/j.remnie.2026.500323","url":null,"abstract":"<p><strong>Objective: </strong>To systematically evaluate the available evidence on the prognostic value of ¹⁸F-FDG PET/CT-based radiomics in Hodgkin lymphoma (HL), its methodological limitations, and potential clinical implications.</p><p><strong>Materials and methods: </strong>A systematic search was conducted in PubMed following PRISMA guidelines. Observational studies or radiomic analyses within clinical trials were included if they evaluated patients with HL and assessed the prognostic value of radiomic features extracted from PET/CT by analyzing their association with survival, treatment response, or treatment refractoriness. Reviews, meta-analyses, and studies without clinical outcomes were excluded. Study quality was assessed using the Radiomics Quality Score (RQS). Given the limited number of studies, a narrative synthesis was performed.</p><p><strong>Results: </strong>Of the 53 initially identified references, 9 retrospective studies met the inclusion criteria. In 7 studies, radiomic or multimodal models showed greater prognostic performance for progression-free survival and overall survival than clinical or volumetric models, with AUC values ranging from 0.86 to 0.92. Textural features derived from GLRLM, GLSZM, and NGTDM were independent predictors of adverse outcomes, with high hazard ratios. Two studies demonstrated a high ability to identify primary refractory disease (AUC > 0.95). Integrating multiple lesions increased predictive accuracy compared with the analysis of a single target lesion. Substantial variability persisted in feature segmentation, extraction, and standardization.</p><p><strong>Conclusions: </strong>Baseline ¹⁸F-FDG PET/CT-based radiomics provides relevant complementary prognostic information in HL, particularly for predicting survival and treatment refractoriness. However, methodological heterogeneity, small sample sizes, and the lack of external validation currently limit its clinical implementation. For radiomics to be realistically incorporated into clinical practice, further studies are needed that are externally validated in large, multicenter cohorts, methodologically reproducible, and widely available for use and validation.</p>","PeriodicalId":94197,"journal":{"name":"Revista espanola de medicina nuclear e imagen molecular","volume":" ","pages":"500323"},"PeriodicalIF":0.0,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147936916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiac amyloidosis: An update on nuclear cardiology in diagnosis and follow-up.","authors":"A Jiménez Heffernan, N Orta Tomás, V Pubul Nuñez, R Ruano Pérez","doi":"10.1016/j.remnie.2026.500322","DOIUrl":"10.1016/j.remnie.2026.500322","url":null,"abstract":"<p><p>Cardiac amyloidosis (CA) is an infiltrative myocardiopathy caused by extracellular deposits of misfolded proteins, with evolution to heart failure (HF) and high mortality. The main forms are light-chain amyloidosis (AL-CA) and transthyretin amyloidosis (ATTR-CA), including the wild type and hereditary. Cardiac involvement constitutes the main prognostic factor. Scintigraphy has transformed the diagnosis by identifying ATTR-CA without biopsy by cardiac imaging with [^99mTc]Tc-diphosphonates, especially with SPECT/CT, which also allows quantifying the amyloid load and thus, introducing prognostic value. Amyloid PET imaging with the new pan-amyloid Radiopharmaceuticals has expanded the diagnostic possibilities, further favoring quantification, early diagnosis and prognostic evaluation and therapeutic response. In parallel, the development of modifying therapies (TTR stabilizers, gene silencers and anti-amyloid agents) has changed the prognosis of patients with CA. This Continuing Education article describes the advances in molecular imaging and its integration with disease-modifying therapies.</p>","PeriodicalId":94197,"journal":{"name":"Revista espanola de medicina nuclear e imagen molecular","volume":" ","pages":"500322"},"PeriodicalIF":0.0,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147936927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Superior vena cava syndrome as a manifestation of primary atypical carcinoid tumor of mediastinum by [68Ga]Ga-DOTA-TATE PET/CT","authors":"M. Daşar,&nbsp;A. Akbulut,&nbsp;S. Özdemir,&nbsp;K. Demirel,&nbsp;G. Koca,&nbsp;M. Korkmaz","doi":"10.1016/j.remnie.2025.500278","DOIUrl":"10.1016/j.remnie.2025.500278","url":null,"abstract":"","PeriodicalId":94197,"journal":{"name":"Revista espanola de medicina nuclear e imagen molecular","volume":"45 3","pages":"Article 500278"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of transperineal prostate biopsy guided by fusion of [18F]F-PSMA-1007 PET/MRI with real-time transrectal ultrasound. Prostate cancer classification using imaging biomarkers and radiomics in PIRADS 3 lesions","authors":"J.R. Garcia ,&nbsp;N. Garrido ,&nbsp;P. Bassa ,&nbsp;A. Compte ,&nbsp;L. Pinilla ,&nbsp;J.R. Romero ,&nbsp;L. Mont-Castro ,&nbsp;J. Garcia-Magro ,&nbsp;S. Ruiz ,&nbsp;S. Mourelo ,&nbsp;J.M. Santabárbara ,&nbsp;E. Riera","doi":"10.1016/j.remnie.2025.500263","DOIUrl":"10.1016/j.remnie.2025.500263","url":null,"abstract":"<div><h3>Objective</h3><div>To assess the diagnostic feasibility of transperineal biopsy guided by fusion of PET/MRI with [18F]F-PSMA-1007 and real-time transrectal ultrasound (BP PET/MR PSMA + TRUS) in patients with PIRADS 3 lesions.</div><div>To analyze imaging biomarkers and radiomic features for differentiating between patients with negative biopsy, clinically non-significant prostate cancer (cnsPCa), and clinically significant prostate cancer (csPCa).</div></div><div><h3>Methods</h3><div>A prospective study was conducted in 20 patients with PIRADS 3 lesions and PSA ≥ 4 ng/mL, with no concurrent suspicious lesions.</div><div>All patients underwent a PET/MRI with [18F]F-PSMA-1007, followed by targeted biopsy using real-time ultrasound fusion.</div><div>Diagnostic accuracy for prostate cancer (PCa) and the proportion of csPCa and cnsPCa diagnoses were calculated.</div><div>Imaging biomarkers and machine learning models based on radiomic features were analyzed.</div></div><div><h3>Results</h3><div>The technique was feasible in 100% of cases.</div><div>The overall detection rate of PCa was 80%, with 100% sensitivity and 36% specificity for csPCa.</div><div>SUVpeak was the only biomarker that showed significant differences between patients with negative histology and PCa (AUC 0.81; sensitivity 75%; specificity 80%; cutoff value 3.5). No biomarker successfully differentiated between csPCa and cnsPCa.</div><div>Both supervised classification models demonstrated high diagnostic performance (AUC &gt; 0.95): Multimodal regression performed slightly better for binary classification (negative vs. PCa). Random Forest outperformed in three-class classification (negative vs. cnsPCa vs. csPCa).</div></div><div><h3>Conclusions</h3><div>BP PET/MRI PSMA + TRUS is a feasible, safe, and potentially superior technique compared to MRI-only targeted biopsy, especially in PIRADS 3 lesions.</div><div>Radiomic analysis improves discriminative ability over conventional imaging biomarkers, particularly for distinguishing between cnsPCa and csPCa.</div></div>","PeriodicalId":94197,"journal":{"name":"Revista espanola de medicina nuclear e imagen molecular","volume":"45 3","pages":"Article 500263"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145644257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preoperative prediction of lymph node metastasis and prognosis in gastric cancer patients: A machine learning model based on 18F-FDG metabolic parameters","authors":"D. Chen,&nbsp;W. Chen,&nbsp;X. Pan,&nbsp;Q. Lyu,&nbsp;J. Zhang,&nbsp;D. Lin,&nbsp;D. Liu","doi":"10.1016/j.remnie.2025.500277","DOIUrl":"10.1016/j.remnie.2025.500277","url":null,"abstract":"<div><h3>Objective</h3><div>To construct and validate a model for predicting lymph node metastasis (LNMs) of gastric cancer (GC) based on 18F-FDG PET/CT multi-parameter data and multiple machine learning (ML) algorithms.</div></div><div><h3>Methods</h3><div>We conducted a retrospective analysis of clinical records and 18F-FDG PET/CT images from 167 GC patients treated with radical gastrectomy. The dataset was randomly divided into two cohorts (training and validation cohort) with a ratio of 7:3. Seven machine learning algorithms were used to develop models. The model's performance was evaluated using metrics such as the receiver operating characteristic curve (ROC), sensitivity, specificity, accuracy, negative and positive predictive value, F1 score, calibration curve, and decision curve. Shapley Additive Explanations (SHAP) analysis was conducted to determine feature importance and enhance model interpretability. Additionally, Kaplan-Meier analysis was used to evaluate the prognosis of GC patients.</div></div><div><h3>Results</h3><div>The Artificial Neural Network algorithm(ANN)model outperformed other algorithms in the validation set, with an AUC of 0.814 (95% CI: 0.707−0.921), a sensitivity of 0.872, a specificity of 0.500 and an accuracy of 0.784. Both calibration and decision curve analyses demonstrated excellent calibrated ability and favorable clinical applicability. Through SHAP method, tumor invasion depth (clinical T stage), platelet count (PLT), and total lesion glycolysis (TLG) emerged as the most influential predictors in the ML model. Furthermore, the model exhibited robust risk stratification capability for overall survival (OS).</div></div><div><h3>Conclusion</h3><div>The ML model utilizing 18F-FDG PET/CT metabolic parameters showed good efficacy and reliability in predicting LNS preoperatively in GC patients.</div></div>","PeriodicalId":94197,"journal":{"name":"Revista espanola de medicina nuclear e imagen molecular","volume":"45 3","pages":"Article 500277"},"PeriodicalIF":0.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}