Nuclear Medicine Communications最新文献

Objective: The primary treatments for Graves' disease include antithyroid drugs (ATD), thyroidectomy, and iodine-131 (I-131) therapy. This study aimed to identify factors predicting treatment outcomes and the treatment period required to achieve euthyroidism after I-131 administration.

Methods: This study included 109 patients with Graves' disease who underwent I-131 therapy. Patients achieving euthyroidism or hypothyroidism within 1 year were classified as the success group, whereas those with remaining hyperthyroidism were classified as the failure group. Thyroid volume, computed tomography (CT) values, 24-h radioiodine uptake, effective half-life, the levels of free triiodothyronine, free thyroxine, thyroid-stimulating hormone, and thyrotropin receptor antibody, history of I-131 therapy, history of ATD use, history of potassium iodide use, and thyroid absorbed dose were analysed.

Results: Larger thyroid volume [odds ratio = 0.982, 95% confidence interval (CI): 0.967-0.998, P  < 0.05] was identified as a predictive factor for treatment failure, as determined by multivariable logistic regression. In contrast, a shorter treatment period was associated with lower thyroid volume (hazard ratio = 0.990, 95% CI: 0.982-0.999, P  < 0.05), higher thyroid absorbed dose (hazard ratio = 1.007, 95% CI: 1.002-1.011, P  < 0.01), and lower thyroid CT values (hazard ratio = 0.963, 95% CI: 0.939-0.987, P  < 0.01), as identified by multivariable Cox regression.

Conclusion: Larger thyroid volume was associated with treatment failure. Smaller thyroid volume, higher thyroid absorbed dose, and lower thyroid CT values were significant predictors of the treatment period required to achieve euthyroidism after I-131 administration.

As a promising approach, in vivo pretargeting can leverage the unique tumor-targeting properties of antibodies for nuclear imaging and therapy while bypassing their pharmacokinetic limitations. The core premise of pretargeting is that targeted vectors and radioisotopes are administered separately, leading to a higher target background ratio than traditional imaging methods using long-lived radionuclides. This strategy directly relies on chemical reactions, namely bioorthogonal reactions. The inverse electron-demand Diels-Alder (IEDDA) cycloaddition between 1, 2, 4, 5-tetrazine and strained alkene dienophile is an emerging catalyst-free 'click' chemistry. The IEDDA reaction has been used in various chemical environments because of its selectivity, efficiency, cleanliness, biocompatibility, and bioorthogonality. In the present review, we briefly focused on the IEDDA reaction in pretargeted nuclear imaging and radioimmunotherapy and discussed the common bioorthogonal click reactions in vivo systems.

Background: In thallium-201 (Tl-201) stress myocardial perfusion imaging (MPI), elevated lung-to-heart ratio (LHR) can help to predict adverse cardiac events and identify coronary artery disease. However, few studies have evaluated the LHR values on Tl-201 MPI in patients with chronic obstructive pulmonary disease (COPD).

Objective: To examine whether LHR in COPD may be altered, considering the combined effects of hypoxia, inflammation, and capillary loss.

Methods: We retrospectively evaluated patients with normal Tl-201 pharmacologic stress MPI, no adverse cardiac events in the subsequent 2 years, and pulmonary function tests, coronary angiography, and echocardiography results obtained within 6 months. Patients with COPD (study group) were matched 1:1 by sex and age to controls with normal pulmonary function (control group). Subgroups within the study group were established based on COPD severity determined by spirometry. MPI images were interpreted using a 17-segment american heart association (AHA) model and a 0-4-point scale. LHR and right ventricle/left ventricle (RV/LV) ratios were also documented.

Results: Patients with severe COPD exhibited lower poststress LHR values than those with mild-to-moderate COPD. Compared with the control group, the moderate COPD group displayed higher stress LHR, stress RV/LV ratio, and tricuspid regurgitation maximum pressure gradient (TRmaxPG) values. Moreover, poststress LHR showed a positive correlation with the stress RV/LV ratio and TRmaxPG value. These findings were statistically significant ( P  < 0.05).

Conclusion: In Tl-201 pharmacologic stress MPI, our study suggests a nuanced relationship between COPD severity and LHR, emphasizing the need to reconsider normal LHR thresholds in COPD. Larger studies are warranted to validate and expand upon these findings.

Background: Cardiac resynchronization therapy (CRT) is an effective treatment for heart failure when left ventricular mechanical dyssynchrony (LVdys) is present, yet approximately 30-40% of patients do not respond to therapy. The purpose of this study is to use unsupervised learning to identify phenotypes of patients with a better response rate.

Methods: Unsupervised learning integrating gated single-photon emission computed tomography (SPECT) was used to identify clinical phenotypes among patients undergoing CRT. We utilized hierarchical clustering analysis to group 217 patients based on 49 pretreatment variables, including demographic, clinical, and phase analysis of gated SPECT data. Fibrosis was measured by the percentage of pixels with less than 50% of maximum relative counts. LVdys was evaluated by phase SD >43° and phase bandwidth >135°.

Results: We identified three phenotypes of patients: two with similar response rates (86.2 and 87.0%) but with different characteristics, one presenting borderline LVdys, low fibrosis and nondilated heart and the other high LVdys, moderate fibrosis and a dilated heart; the third phenotype represents patients with moderate LVdys, substantial amounts of cardiac fibrosis and a dilated heart that do not have a good response to CRT (55.9%).

Conclusion: Our results suggest that evaluating cardiac dyssynchrony, fibrosis, and remodeling through phase analysis of gated SPECT is relevant in characterizing the phenotype of good responders. Patients with substantial amounts of cardiac fibrosis have less benefit from CRT. This work suggests that CRT recommendations based on customized selection criteria associated with gated SPECT can lead to higher response rates.

Background: 68 Ga prostate-specific membrane antigen (PSMA) PET/computed tomography (CT) plays a critical role in prostate cancer management. Most clinical reports, however, remain unstructured, increasing the risk of omitting essential diagnostic information. Although standardized frameworks such as PROMISE (Prostate Cancer Molecular Imaging Standardized Evaluation) have been proposed to improve consistency, real-world adherence remains variable. This study assessed the adequacy of 68 Ga PSMA PET/CT reports and evaluated their alignment with PROMISE recommendations.

Methods: A total of 189 68 Ga PSMA PET/CT reports were retrospectively reviewed and scored based on the presence (1) or absence (0) of six diagnostic elements: primary tumor localization, PSMA uptake intensity, lymph node involvement, bone metastases, visceral metastases, and seminal vesicle involvement. Reports were classified as clinically adequate (score ≥5), partially adequate (3-4), or inadequate (<3). A one-sample z test assessed whether the proportion of adequate reports met a 90% benchmark (α = 0.05). Chi-square tests evaluated differences in reporting frequencies across elements.

Results: None of the reports (0%) were inadequate. Of 189 reports, 95 (50.3%) were clinically adequate (score ≥5) and 94 (49.7%) were partially adequate (score 3-<5), with a significant overall adequacy rate ( P  < 0.0001). A notable gap existed between documentation of seminal vesicle involvement and prostate size ( P  < 0.0001). Despite consistent reporting of anatomical localization and molecular imaging tumour, node, metastases (miTNM) classification, key elements were often missing, including PSMA uptake metrics, lesion size, nodal features, CT parameters, imaging protocol, and reader confidence.

Conclusion: In this single-center study, free-text 68 Ga PSMA PET/CT reports were adequate for clinical needs but demonstrated only partial conformity to the PROMISE framework.

Objective: This study aimed to evaluate the most reliable predictors of progression-free survival (PFS) and overall survival (OS) among six different response criteria during interim PET (I-PET)/computed tomography (CT), including the change in total metabolic tumor volume (ΔTMTV) in patients with diffuse large B-cell lymphoma (DLBCL).

Methods: A retrospective analysis was conducted on patients with DLBCL who underwent baseline PET/CT and I-PET after 3-4 cycles of chemoimmunotherapy. Various response criteria were assessed, including Lugano, response evaluation criteria in lymphoma (RECIL), change in maximum standardized uptake value (ΔSUV max ), Peking, quantitative PET, and the novel ΔTMTV. Survival outcomes were obtained using Kaplan-Meier survival analysis and Cox proportional-hazards regression models and compared with Harrell's C-index for predictive accuracy.

Results: One hundred and two patients enrolled. The median PFS was 58 months, and the median OS was 63.5 months. ΔSUV max (cut-off: 66%) had the highest predictive accuracy for both PFS and OS (C-index: 0.689, 0.686; P  < 0.001). ΔTMTV (cut-off: 85.69%) was significantly associated with survival ( P  = 0.003 for PFS, P  = 0.005 for OS) but did not outperform other response criteria. RECIL showed better survival prediction when minor responders were categorized as stable disease rather than partial response. A high baseline TMTV (>126.8 cm³) was correlated with lower survival, as indicated by PFS ( P  = 0.004) and OS ( P  = 0.019).

Conclusion: Interim PET/CT response evaluation using ΔSUV max66 is the most reliable predictor of survival in DLBCL. ΔTMTV has potential but requires improvement to enhance its prognostic accuracy. Future studies should explore refined segmentation methods to enhance metabolic tumor volume assessment in clinical practice.

Objective: The aim of this study was to determine the rate of metastasis types in prostate cancer (PCa) patients with bone metastasis and to evaluate the relationship between volumetric parameters obtained from gallium-68 ( 68 Ga) prostate-specific membrane antigen (PSMA) PET/computed tomography (CT) and prostate-specific antigen (PSA) levels.

Materials and methods: We retrospectively reviewed the images of patients who underwent 68 Ga-PSMA PET/CT for restaging for recurrent PCa between 2014 and 2019. All detected bone lesions were manually grouped as 'osteoblastic (OB), osteolytic (OL), mixed (M), and radio-occult (RO) lesions' and the number and percentage were determined. Different volumetric values are obtained for each type of bone metastasis using the LIFEx v7.3.0 program. The relationship between PSA level and these volumetric values will be determined by the Spearman correlation test. The relationship between the International Society of Urological Pathology (ISUP) PCa grade group and volumetric values will be evaluated by the Kruskal-Wallis correlation test.

Results: Seventy-one patients had a total of 599 bone metastasis. Of these lesions, 268 were OB (44.7%), 39 were OL (6.5%), 72 were M (12.0%), and 220 were RO (36.7%). Total lesion volume (TLV) (P : 0.001), total lesion activity (TLA) (P : 0.001), and OB-TLA (P : 0.042) were significantly different between ISUP grades. In addition, the total number of lesions showed a statistically significant difference between ISUP grades (P : 0.019). PSA level correlated with RO lesion number ( r : 0.404, P : 0.016), RO-TLV ( r : 0.471, P : 0.004), and RO-TLA ( r : 0.528, P : 0.001).

Conclusion: 68 Ga-PSMA PET/CT can identify the source of biochemical recurrence by detecting RO lesions at early stages when bone mineral density is not affected.

Background: Lymphoma staging plays a pivotal role in treatment planning and prognosis. Yet, it still relies on manual interpretation of PET/computed tomography (CT) images, which is time-consuming, subjective, and prone to variability. This study introduces a novel radiomics-based machine learning model for automated lymphoma staging to improve diagnostic accuracy and streamline clinical workflow.

Methods: Imaging data from 241 patients with histologically confirmed lymphoma were retrospectively analyzed. Radiomics features were extracted from segmented lymph nodes and extranodal lesions using PET/CT. Three machine learning classifiers (Logistic Regression, Random Forest, and XGBoost) were trained to distinguish between early-stage (I-II) and advanced-stage (III-IV) lymphoma. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, and accuracy together with survival analysis.

Results: Among the three models evaluated, the logistic regression model incorporating both nodal and extranodal radiomic features performed the best, achieving an AUC of 0.87 and a sensitivity of 0.88 in the external validation cohort. Including extranodal features significantly improved classification accuracy compared to nodal-only models (AUC: 0.87 vs. 0.75). Survival analysis revealed advanced-stage patients had a fourfold higher mortality risk (hazard ratio: 0.22-0.26, P = 0.0036) and a median survival of 84 months. Key radiomic features, such as tumor shape irregularity and heterogeneity, were strongly associated with staging, aligning with Lugano criteria for extranodal spread.

Conclusion: This study demonstrated the potential of PET radiomics features for automated Lugano staging. Adding extranodal features significantly improved staging accuracy and informed treatment.

Purpose: Hibernating myocardium is a viable but dysfunctional myocardium state caused by chronic ischemia, with potential for recovery postrevascularization. This study evaluates the feasibility of transfer learning for predicting hibernating myocardium from rest myocardial perfusion images.

Methods: Patients who underwent myocardial viability assessment from January 2017 to September 2022 were split into training (70%) and validation (30%) sets, while those from October 2022 to January 2023 formed the testing set. Hibernating myocardium was defined as a mismatched perfusion-metabolism defect with impaired contractility. Rest myocardial perfusion polar maps were embedded using Google's InceptionV3, followed by data normalization and analysis of variance-based feature selection. Three gradient boosting algorithms were trained with stratified 10-fold cross-validation, validated, and tested. Performance was assessed using area under the curve (AUC), classification accuracy (CA), F1 score, specificity, and model interpretability via SHapley Additive exPlanations (SHAP) plots.

Results: The study included 239 patients (214 males, 25 females, mean age 56 ± 11 years); 123 (51.5%) had hibernating myocardium. All models achieved >0.700 in performance metrics across all datasets. Among them, extreme gradient boosting (xgboost) performed best on the test set (F1 score: 0.800, CA: 0.774, specificity: 0.909, AUC: 0.782). Beeswarm SHAP plots revealed a clear pattern of model interpretability for all models.

Conclusion: This study demonstrates the feasibility of transfer learning for predicting hibernating myocardium from rest myocardial perfusion images. The integration of deep convolutional neural networks with gradient boosting models highlights the potential of machine learning-based myocardial viability assessment, contributing valuable early evidence.