Annals of Nuclear Medicine最新文献

Objectives

Incomplete myocardial glucose suppression (MGS) in ketogenic 18F-FDG-PET/CT is a common problem that reduces the diagnostic accuracy in detecting myocardial inflammation. This study assesses the usefulness of a dietary logbook, blood ketone testing and risk factors for inadequate MGS.

Methods

Retrospective (2022–2024) and prospective (2024–2025) analysis was performed on all patients who underwent a ketogenic 18F-FDG-PET/CT at two tertiary hospitals. All patients were instructed to follow > 24-hour ketogenic diet and > 12-hour fast before imaging. In April 2024, blood ketone testing, a dietary logbook, and improved dietary guidelines were introduced.

Results

After introducing the dietary logbook and improved dietary guidelines, inadequate MGS rates decreased from 26% to 17% (95 patients 2022–2024 vs. 92 patients 2024–2025)(p-value 0.14). Mean blood ketone levels were significantly lower in patients with incomplete MGS (0.34mmol/L vs. 0.76mmol/L, p-value 0.04). On univariate analysis, significant risk factors for inadequate MGS included prednisolone use (75% vs. 14.9%, OR: 17.1 [95%CI 1.65-177.04], p = 0.009), low blood ketones (≤ 0.3mmol/L)(OR: 5.77 [95%CI 1.69–19.68], p = 0.003) and female sex (27.5% vs. 9.6% in males, OR: 3.57 [95%CI 1.12–11.3], p = 0.025). Multivariate analysis confirmed prednisolone use, low ketones (≤ 0.3mmol/L) and < 24-hour ketogenic diet as independent risk factors for inadequate MGS. Rates of inadequate MGS were 50%, 26% and 7% for patients with blood ketone levels of 0.1, 0.2–0.3 and ≥ 0.4mmol/L, respectively. All patients on prednisolone with ketones ≤ 0.3mmol/L had inadequate MGS.

Conclusions

Dietary logbook and clear instructions improve adherence. Low ketones, prednisolone use and short ketogenic preparation are risk factors for inadequate MGS.

Dopamine transporter (DAT) tracers like [¹²³I]I-FP-CIT are frequently used in routine practice to support the diagnosis in patients suffering from clinically uncertain parkinsonian syndromes as well as in scientific studies. The DAT is expressed not only at the plasma membrane of dopaminergic neurons, but is also trafficking within the cytoplasm. It has been well documented that the striatal [¹²³I]I-FP-CIT binding is lower in disorders neuropathologically characterized by DAT loss induced by degeneration of nigrostriatal dopaminergic neurons (e.g., Parkinson’s disease). In addition, in studies in subjects without dopaminergic degeneration it has been suggested that subtle lower striatal binding can be induced by down-regulation of the DAT. However, theoretically, down-regulation can only be measured if [¹²³I]I-FP-CIT binds predominantly to the DAT expressed at the plasma membrane, but not due to binding to the internalized DAT. We therefore looked into the literature to find support or opposition for this postulate. In this brief narrative review, we found indirect evidence that tracers like [¹²³I]I-FP-CIT binds predominantly to the DAT expressed on plasma membrane of nigrostriatal dopaminergic neurons.

Objective

The Centiloid (CL) method is a standardized quantitative approach for amyloid positron emission tomography (PET) that involves the anatomical standardization of PET images. The Global Alzheimer’s Association Interactive Network recommends performing this anatomical standardization using contemporaneously acquired three-dimensional (3D) T1-weighted magnetic resonance imaging (MRI). However, in clinical practice, contemporaneous 3D T1WI is not always available due to outdated imaging or missing sequences. Recently, alternative methods utilizing low-dose computed tomography (CT) scan images from PET/CT scan or PET images themselves for anatomical standardization have been proposed and implemented in commercially available software. This study aimed to compare the CL obtained via MRI-based standardization with CT scan-based and PET-only standardizations. Further, the clinical applicability of these alternative methods was assessed.

Methods

We retrospectively analyzed 68 patients who underwent [18 F]florbetapir PET/CT scan imaging. The CL were calculated using two commercially available software programs—AMYclz^® Neuro (PDR Pharma, Tokyo) and BRAINEER^® Model A (Splink, Tokyo)—under three conditions: anatomical standardization with MRI, CT scan, and a PET-derived template. The correlations and differences among these methods were evaluated.

Results

68 patients (age: 70.8 ± 11.2) were included in the analysis. The median CL was 33.87 (3.53–76.57). CT scan-based standardization had lower CL than MRI-based standardization (mean difference: −5.9 ± 4.8). Meanwhile, PET-only standardization had slightly lower CL than MRI-based standardization (-2.1 ± 11.0). The differences in the CL were generally within ± 15. In cases with evidently positive (CL ≥ 50) or clearly negative (CL ≤ 5) findings, the omission of MRI did not affect exceeding the threshold.

Conclusions

When contemporaneous 3D T1WI is not available, CT scan-based or PET-only anatomical standardization can be a practical alternative for the qualitative assessment of amyloid PET. However, users should recognize that CT scan-based methods have a systematic tendency to underestimate CL values compared with MRI-based methods. Meanwhile, PET-only methods have a tendency to slightly underestimate CL values. The omission of MRI may be acceptable for clear positive or negative cases. However, caution is required for borderline cases, particularly in situations requiring precise quantification such as treatment monitoring.

We conducted a systematic review and meta-analysis to assess the diagnostic accuracy of artificial intelligence (AI)-assisted 18 F-FDG PET/CT for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. A comprehensive search of PubMed, Embase, and Web of Science was conducted for studies, with a cutoff date of August 29, 2025, and updated on October 16, 2025. The QUADAS-2 technique and Grading of Recommendations Assessment, Development and Evaluation framework were employed to evaluate study quality. Diagnosis accuracy was aggregated utilizing a bivariate random-effects model. A total of 49 studies involving 3038 patients were included. The Spearman rank correlation coefficient for AI was determined to be 0.159 (P = 0.662). The pooled sensitivity, specificity, PLR, NLR, DOR of AI-assisted 18 F-FDG PET/CT for predicting pCR to NAC in breast cancer were 0.82 (95% CI 0.76–0.87), 0.83 (95% CI 0.75–0.89), 5.03 (95% CI 3.79–6.69), 0.39 (95% CI 0.31–0.49), and 17.71 (95% CI 10.37–30.25), respectively. Furthermore, the AUC was determined to be 0.83 (95% CI: 0.80–0.86). The Fagan nomogram indicated a positive likelihood ratio of 52% and a negative likelihood ratio of 6%. This meta-analysis demonstrates that AI-assisted 18 F-FDG PET/CT shows good diagnostic accuracy for predicting pCR to NAC in breast cancer, achieving better sensitivity and specificity than MRI and ultrasound, and comparable accuracy to conventional PET/CT with improved specificity. These findings highlight its potential as a reliable tool to aid clinical decision-making, though moderate heterogeneity underscores the need for standardized methods and multicenter prospective validation.

Objective

To evaluate the diagnostic accuracy of fluorine-18-fluorodeoxyglucose-positron emission tomography (¹⁸F-FDG-PET), computed tomography (CT), thyroid markers, and their combination for diagnosing thyroid follicular carcinoma.

Methods

We analyzed 53 patients (12, 10, and 31 with follicular cancer, follicular adenoma, and adenomatous goiter, respectively) selected from 12,403 consecutive patients who underwent preoperative ¹⁸F-FDG-PET/CT at our hospital between January 2013 and December 2019. Blood data, including thyroxine, triiodothyronine, thyroid-stimulating hormone, and thyroglobulin levels, as well as the patients’ age, sex, thyroid tumor maximum standardized uptake value (SUVmax), and calcification, were compared between the follicular carcinoma and benign thyroid tumor (adenoma and adenomatous goiter) groups. Comparisons were performed using Student’s t-test, Mann–Whitney U test, or chi-squared test. For factors showing significant group differences, cut-off values were determined using receiver operating characteristic (ROC) analysis.

Results

Significant differences were observed between the two groups regarding calcification, SUVmax, SUVmax/tumor size, and thyroglobulin levels (all p < 0.01). Peripheral calcification was more common in follicular carcinomas (6/12 cases) than in benign thyroid tumors (1/41 cases). The area under the ROC curve (AUC) was 0.89 for SUVmax, with a Youden index cut-off value of 5.2, yielding 100% sensitivity and 73.2% specificity. For thyroglobulin, the AUC was 0.739, with a Youden index cut-off value of 3379, resulting in 58.3% sensitivity and 87.8% specificity. Only 2.4% of benign thyroid tumors were positive for all three indicators (SUVmax > 5.2, presence of tumor calcification, and thyroglobulin > 3379), whereas 50% of follicular carcinomas were positive for all indicators, corresponding to a sensitivity and specificity for malignancy of 50% and 97.6%, respectively. Notably, no case of follicular carcinoma presented with all three indicators negative or SUVmax < 5.2 (100% specificity).

Conclusions

The combination of high SUVmax, CT-detected calcification, and high thyroglobulin levels strongly suggests follicular carcinoma and may warrant resection.

Introduction

In the Movement Disorder Society criteria for the diagnosis of Parkinson’s disease (PD), evaluation of the presynaptic dopamine system should be performed using dopamine transporter single-photon emission computed tomography (DAT SPECT). However, it is difficult for unexperienced physicians to detect a mild defect. Here, we attempted to develop a simple deep learning-based image analysis method to evaluate DAT SPECT images.

Methods

We used data from 300 patients who were diagnosed with PD and 102 control patients with non-neurodegenerative diseases as the artificial intelligence (AI) development cohort. For validation, we analyzed the data of 96 patients with PD from an independent cohort. We divided the development cohort into the training and test sets. Using the training set, we performed transfer learning using six pre-trained convolutional neural network architectures, and created AI models. We evaluated their accuracy, sensitivity, and area under the receiver operating characteristic curve, and further confirmed their validity by using the validation cohort. In addition, we compared the accuracy of the best AI model with that of two experienced neurologists and a resident.

Results

The selected AI model could interpret DAT SPECT images with an accuracy of 0.959; accuracy in the validation cohort was 0.8959–1. There was no significant difference between the accuracy of the AI model and physicians.

Conclusion

Our simple AI model for the interpretation of DAT SPECT images was accurate and robust. Its accuracy was equivalent to that of physicians.

Objective

The aim of this study was to determine the diagnostic accuracy of single and combined brain perfusion SPECT parameters (maximum perfusion [MP], maximum change [MC], maximum extension [ME], and subtraction ictal SPECT co-registered to MRI [SISCOM]) for seizure onset zone (SOZ) localization in patients with drug-resistant epilepsy (DRE), including temporal lobe epilepsy (TLE) and extra-temporal lobe epilepsy (ETLE).

Methods

This retrospective study included 49 patients with DRE who underwent ictal and interictal SPECT imaging from March 2012 to March 2022 and had favorable surgical outcomes (Engel class I–II) with at least 2 years follow-up. SPECT images were evaluated independently by two nuclear medicine physicians using visual criteria for MP, MC, ME, and SISCOM parameters, including the single largest hyperperfusion region (SIL). Diagnostic accuracy was assessed against the surgical SOZ as the reference standard, calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy. Inter-rater agreement was evaluated using Cohen’s kappa.

Results

Among single SPECT parameters, ME and MC showed strong diagnostic performance with accuracies of 89.2% and 88.2%, respectively, while SIL had the highest specificity (94.3%). Combined SPECT parameters significantly improved accuracy and specificity; notably, the combination SIL_MC_ME achieved 91.1% accuracy and 97.0% specificity. Further improvements were observed by combining MRI with SPECT parameters; particularly, MRI combined with SIS (MRI_SIS) achieved the highest accuracy (95.9%) and excellent specificity (99.2%). MRI alone exhibited the highest overall diagnostic accuracy (96.2%). The ETLE subgroup (n=16) showed similar improvements in accuracy and specificity with combined parameters (up to 88.8%). Cohen’s kappa indicated substantial inter-rater agreement, especially for SIL (κ = 0.80).

Conclusions

SPECT-derived parameters, particularly when combined with each other or integrated with MRI, demonstrate significant improvements in diagnostic accuracy for seizure localization in DRE patients. Combined parameters such as SIL_MC_ME and MRI-integrated approaches (MRI_SIS) are recommended for enhancing clinical decision-making and pre-surgical planning, particularly when MRI findings alone are inconclusive.

Objectives

To develop and evaluate the predictive efficacy of a combined model incorporating clinical parameters and PET-based radiomics signature (R-signature) for prognosis in patients with metastatic melanoma.

Methods

A total of 187 metastatic melanoma patients from two centers were included, with the datasets from each center divided into training and validation cohorts, respectively. The optimal machine learning algorithm selected from the six candidates was used to construct the model. Five-fold cross-validation was performed on the training cohort for internal validation, while the external validation cohort was used for independent validation. The area under the receiver operating characteristic curve (AUC) was used to compare the model accuracies. Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts.

Results

The cutoff values for R-signature predicting progression-free survival (PFS) and overall survival (OS) were 0.47 and 0.59, respectively. The combined model showed robust prognostic performance, with C-indices of 0.92 (95%CI: 0.83–0.98) for PFS and 0.99 (95%CI: 0.97–0.99) for OS in the train cohort. Validation cohort confirmed these findings, with C-indices of 0.95 (95%CI: 0.86–0.99) for PFS and 0.97 (95%CI: 0.92-1.00) for OS. Calibration and decision curve analyses supported the clinical value of the combined model.

Conclusion

PET-based R-signature offers valuable prognostic insight in metastatic melanoma, with the combined model further improving risk stratification. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.