Annals of Nuclear Medicine最新文献

Objective: Early diagnosis of radioiodine-refractory differentiated thyroid cancer (RAIR-DTC) is crucial for timely treatment adjustment. Currently, diagnosis relies on clinical progression and assessment of iodine uptake in lesions, which is a time-consuming process. The objective of this study is to identify risk factors associated with RAIR-DTC and develop a visual predictive model to facilitate earlier identification.

Methods: Retrospectively collected data (including general information, biochemical indicators, pathological and imaging data) of DTC patients from August 2020 to September 2025. A total of 234 patients were included and divided into RAIR-DTC (n = 105) and non-RAIR-DTC (n = 129) according to guidelines. The dataset was divided into a model development cohort and an external validation cohort using January 2025 as the temporal cutoff. The model development cohort was randomly split into a training cohort and an internal validation cohort in a 7:3 ratio. Subsequently, univariate and multivariate logistic regression analysis were performed to determine the independent predictors of RAIR-DTC, which was then visualized using a nomogram. The performance of the nomogram was evaluated by the area under the receiver operating characteristic(AUC) in training, an internal validation and external validation cohorts. Calibration curve and decision curve analysis(DCA) were used to validate the nomogram's performance. Additionally, progression-free survival (PFS) analysis was conducted using the Kaplan-Meier method.

Results: Through multivariate logistic regression, treatment response evaluation, recurrent /persistent lesions, sTg-second, and the ratio of sTg-second/sTg-first were obtained to develop a nomogram model for predicting RAIR-DTC. In the training cohort, internal validation cohort and external validation, the AUC were 0.893, 0.920 and 0.743, respectively. The nomogram fit well in calibration curves (P > 0.05), and DCA further confirmed the clinical usefulness of the nomogram. Additionally, the RAIR-DTC group exhibited significantly shorter PFS compared to the non-RAIR group.

Conclusions: The nomogram model, constructed based on dynamic serological, imaging, and clinical evaluation, demonstrates good predictive performance and clinical utility. This model provides valuable guidance for individualized treatment decision-making in DTC patients.

Objective: Development and validation of a radiomics model based on pretreatment deoxy-2-[fluorine-18]-fluoro-D-glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) imaging for predicting lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC).

Methods: A retrospective analysis was performed on 145 patients with ESCC, using pretreatment ¹⁸F-FDG PET/CT imaging data and clinical information. Patients were randomly divided into training and validation cohorts in a 7:3 ratio. In the training cohort, independent risk factors for LNM in ESCC were identified through univariate and multivariate logistic regression analyses. Radiomic features were extracted from the PET images, and the least absolute shrinkage and selection operator (LASSO) regression was used for dimensionality reduction. Features highly correlated with LNM in ESCC were selected. The weighted radiomics score (Radscore) was then calculated based on these selected features. The diagnostic performance of each factor was evaluated using receiver operating characteristic (ROC) curves, and a prediction model nomogram was established. Decision curve analysis (DCA) was conducted to evaluate the clinical utility of the model. Finally, the model was validated using the validation cohort.

Results: Maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and gender were significantly associated with LNM in ESCC (all P < 0.05). SUVmax was found to be an independent risk factor for predicting LNM in ESCC. In the training and validation cohorts, the areas under the curve (AUC) for SUVmax combined with Radscore were 0.809 (95% CI: 0.723-0.894) and 0.801 (95% CI: 0.661-0.941), respectively, both of which were higher than those for SUVmax and Radscore alone. A nomogram, a comprehensive predictive model based on SUVmax and Radscore, may improve the net clinical benefit for patients.

Conclusion: The nomogram, a predictive model developed using ¹⁸F-FDG PET/CT-based radiomics, offers reliable predictive value for LNM in ESCC and is expected to serve as a reference tool for therapeutic decision making in patients with ESCC.

Objective: Next-generation imaging with prostate-specific membrane-antigen Positron Emission Tomography/Computed Tomography (PSMA PET/CT) has emerged as an imaging modality offering high diagnostic accuracy and prognostic biomarkers in the primary staging of prostate cancer (PCa). Among these, PSMA-positive tumor volume (PSMA-TV) may carry prognostic significance but has been poorly investigated in patients receiving radical-intent radiotherapy (RT).

Methods: Patients with biopsy-proven unfavorable intermediate-to-high-risk PCa staged as non-metastatic (T1-4 N0-1 M0) at [⁶⁸ Ga]PSMA-11 or [¹⁸F]PSMA-1007 PET/CT before definitive RT plus androgen deprivation therapy (ADT) at our Institution (2019-2024) were retrospectively recruited. Following RECIP criteria, semi-quantitative PET parameters extracted were: maximum and mean standardized uptake value (SUVmax and SUVmean), PSMA-TV, and total lesion PSMA uptake (PSMA-TL) [PSMA-TV*SUVmean]). We assessed the association between PET-derived semi-quantitative parameters and clinical outcomes, including time to treatment failure (TTF) and PSA response. Inverse probability of treatment weighting (IPTW) was adopted to address confounders, namely, initial PSA, ISUP score, T stage, and N stage.

Results: Among 145 patients recruited, median age was 76 years and median initial PSA 8.9 ng/mL. Most patients had ISUP grade ≥ 3 (39.3%), and 28.3% presented with nodal involvement at staging. Concurrent androgen deprivation therapy was administered in all patients, and the most common duration was 12-24 months (60.7%). The median follow-up was 20.5 months. While unadjusted analyses showed no significant association between PET parameters and treatment outcomes, IPTW-adjusted survival analysis revealed that high PSMA-TV was significantly associated with shorter TTF (p < 0.05). Other PET-derived metrics were not predictive of outcomes.

Conclusion: Our findings highlight PSMA-TV as an independent predictor of treatment failure following definitive RT in PCa, supporting its potential role as a risk-stratifying biomarker, paving the way for individualized therapeutic strategies. Prospective validation is warranted to confirm its clinical utility and guide future radiotherapy personalization.

Objective: To determine the role of whole-body dynamic (WBD)/Patlak parametric ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in patients with alveolar echinococcosis (AE). This technique allows separating metabolized from unmetabolized FDG in the blood pool and tissue, potentially providing complementary qualitative information and superior quantification to standard static PET/CT images.

Methods: We prospectively analyzed 20 PET/CT datasets performed for staging or therapy monitoring in patients with confirmed AE. Dynamic and standard static PET/CT datasets were acquired in all patients, and quantitative imaging parameters were measured in the lesion with the highest uptake (i.e., maximum standardized uptake value (SUVmax) and Patlak parameters) and compared to normal liver tissue (SUVratio and Patlak ratio).

Results: Mean SUVmax in AE manifestations was 5.7 ± 3.1 (3.2-13.9), compared to 3.2 ± 0.4 (2.5-4.2) in non-infected liver tissue, respective values for Patlak were 13.0 ± 8.6 (2.7-35.5) and 4.9 ± 2.8 (0.6-12.1). SUVratio (1.8 ± 1.1; 1.0-5.2) was significantly lower (P < 0.001) than Patlak ratio (3.2 ± 3.2; 1.1-15.6). Both ratios correlated significantly with E. granulosus hydatid fluid (EgHF) antibodies (SUVratio r = 0.73, P < 0.001; Patlak ratio r = 0.85, P < 0.001).

Conclusion: WBD PET/CT yields higher lesion-to-background contrast and may, therefore, have the potential to increase sensitivity in the assessment of hepatic AE.