Annals of Nuclear Medicine最新文献

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.

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.

Background: Cerebral blood flow (CBF) imaging can be performed using SPECT with ¹²³I-IMP; however, its spatial resolution and image quality are inferior to those of PET-CBF imaging using labeled water. This study aimed to enhance the resolution and image quality of SPECT-CBF images using the pix2pix machine learning framework.

Methods: Seventy-three patients with suspected cerebral ischemia underwent CBF imaging using SPECT (Symbia, with ¹²³I-IMP) and PET (mCT and Vision, with O-15-labeled gas). Image reconstruction was performed using OSEM for PET and Flash3D for SPECT. The SPECT and PET images were coregistered using SPM12, and a pix2pix model was trained using SPECT-CBF images as input and PET-CBF images as the target, with 43 cases used for training and 15 for testing. P2P-SPECT-CBF images were then generated for 15 cases for validation. Visual assessment on a 5-point scale, structural similarity index measure (SSIM), and region-of-interest (ROI)-based quantitative analysis were performed to evaluate image similarity and accuracy.

Results: The P2P-SPECT-CBF images demonstrated improved visual similarity to PET images, with an average score of 4.2 and 3.5 in open and blind assessments, respectively. The SSIM value of conventional SPECT images compared to PET was 0.79, while that of P2P images was 0.86 and those were significantly different, indicating enhanced structural similarity. In ROI analysis, the correlation between SPECT and PET CBF values was y = 0.13 + 0.63x, r = 0.77 (p < 0.01). The correlation between P2P-SPECT and PET was y = 0.10 + 0.65x, r = 0.78 (p < 0.01), and between P2P-SPECT and SPECT, the relationship was y = 0.01 + 0.89x, r = 0.86 (p < 0.01).

Conclusion: The proposed method generated P2P-SPECT-CBF images with image contrast closely resembling that of PET-CBF, while preserving the quantitative properties of SPECT-CBF.

Objective: The primary role of ¹⁸F-FDG PET/CT at the initial diagnosis of breast cancer is to detect distant metastases. This study aimed to investigate locoregional characteristics associated with distant metastasis, based on clinicopathological factors, standard-of-care (SOC) imaging, and ¹⁸F-FDG PET/CT-including a novel PET parameter, subcutaneous/cutaneous uptake (SCU).

Methods: This retrospective study included patients with newly diagnosed, unilateral invasive breast cancer who underwent pretreatment ¹⁸F-FDG PET/CT. Associations between distant metastasis and the following parameters-including age, SOC imaging-based clinical T and N stage, histology, histological grade, and subtype, as well as tumor SUVmax, subareolar SUV ratio (sSUVr), and subcutaneous/cutaneous uptake (SCU) on PET-were assessed using the Mann-Whitney U test, Fisher's exact test, and logistic regression. Subgroup analyses were also performed after stratifying patients by locoregional clinical stage (I-IIIA vs. IIIB-C).

Results: Among 197 women (mean age, 56 ± 14 years), distant metastasis was identified in 23 (11.6%). The prevalence of distant metastasis at each locoregional stage in SCU-positive versus SCU-negative patients was as follows: 0% vs. 0% for stage I; 22% vs. 1% for stage IIA; 25% vs. 14% for stage IIB; 25% vs. 13% for stage IIIA; 25% vs. 33% for stage IIIB; and 50% vs. 50% for stage IIIC, with a statistically significant difference observed at stage IIA. In the total cohort, univariate analysis showed that clinical T stage (p = .005), clinical N stage (p < .001), sSUVr (p = .002), and SCU (p < .001) were significantly associated with distant metastasis. In multivariate analysis, only clinical N stages (Odd ratio [OR], 6.5-32.6; p < .001-0.02) remained independent predictors. In the stage I-IIIA subgroup, SCU (OR, 4.86; p = .048) independently predicted distant metastasis, along with age (OR, 1.07; p = .01) and clinical N stages (OR, 8.40-30.26; p = .002-0.008). In the stage IIIB-C subgroup, none of the explanatory variables had significant associations with distant metastasis.

Conclusions: Age, clinical N stages, and SCU were associated with an elevated risk of distant metastasis in the stage I-IIIA disease. SCU may serve as a novel imaging marker of systemic disease and aid in the diagnosis of distant metastasis-particularly in patients with early-stage breast cancer, where such findings can critically influence treatment strategy.

Objective: To investigate whether metabolic and volumetric 18F-FDG PET parameters are associated with histopathological response, metastatic disease at diagnosis, overall survival (OS), and progression-free survival (PFS) in pediatric osteosarcoma (OST) patients. Additionally, to compare absolute and relative threshold methods for metabolic tumor volume (MTV) calculation.

Methods: This single-center retrospective study included 26 pediatric OST patients who underwent 18F-FDG PET/CT at diagnosis and, when available, after neoadjuvant chemotherapy. SUVmax, SUVpeak, MTV, total lesion glycolysis (TLG) and anatomic tumor volume of the primary tumor, along with whole-body MTV (wb-MTV) and whole-body TLG encompassing all FDG-avid metastatic lesions, were measured and their percentage changes (∆) between PET scans were calculated. MTV and TLG were calculated using absolute (SUV 2.0) and relative (40% of tumor SUVmax) threshold methods.

Results: Baseline 18F-FDG PET parameters did not predict histopathological response. But, we found that ΔSUVmax, ΔMTV (2.0), ΔTLG (2.0), and ΔTLG (40%) were associated with histopathological response (p = 0.029). Although not statistically significant, patients with metastases had higher baseline SUVmax, SUVpeak, MTV (2.0), and TLG (2.0) values. Anatomic tumor volume did not differ between the metastatic and localized groups. Patients with wb-MTV (40%) > 137.5 had a significantly higher mortality risk (HR = 4.27, p = 0.017). Kaplan-Meier analysis revealed that patients with primary tumors exhibiting SUVmax > 5.56 and SUVpeak > 4.57 had significantly lower estimated 5-year OS rates (p = 0.036 and 0.029), even after excluding patients with metastasis at diagnosis.

Conclusions: ΔSUVmax, ΔMTV (2.0), ΔTLG (2.0), and ΔTLG (40%) were found to be associated with histopathologic response, suggesting that these changes may serve as predictors of histopathologic outcome. MTV (2.0) may be a more reliable indicator of tumor aggressiveness than anatomic tumor volume, as it tended to be higher in the metastatic group. Our finding suggests that using absolute threshold may better reflect tumor burden in primary lesions with high metabolic activity, whereas relative threshold may be more suitable for evaluating total tumor burden, including low 18F-FDG uptake metastases. Inferior survival outcome is associated with elevated baseline SUVmax and SUVpeak values persisted even when patients with metastatic disease were excluded, suggesting their potential prognostic value.