Annals of Nuclear Medicine最新文献

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.

Objective: Colonoscopy is the gold standard for colorectal cancer (CRC) screening; however, its invasiveness, cost, and associated risks limit its use in population-wide programs. Therefore, effective noninvasive tools for identifying individuals at high risk for colorectal adenomas-the precursors to CRC-are needed. 2-deoxy-2-[¹⁸F] fluoro-D-glucose positron emission tomography/computed tomography (FDG PET/CT) captures systemic metabolic and inflammatory activity and may offer imaging biomarkers for adenoma risk stratification.

Methods: We retrospectively analyzed 754 asymptomatic individuals who underwent both colonoscopy and FDG PET/CT within 30 days as part of health screening. PET/CT-derived variables included standardized uptake values (SUVs) from visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), skeletal muscle, liver, spleen, bone marrow, and colorectal wall. Clinical data included age, sex, and body mass index (BMI). A least absolute shrinkage and selection operator (LASSO) logistic regression model was trained on 452 individuals and tested in a separate validation cohort of 302.

Results: The final LASSO model selected eight variables, including VAT area (positive association) and multiple tissue-specific SUV features (negative associations). In the test set, the model achieved an area under the curve (AUC) of 0.693 (95% confidence interval: 0.631-0.754), significantly outperforming individual predictors such as VAT area (AUC = 0.630, P = 0.011), VAT HU (AUC = 0.585, P = 0.001), and SAT SUVmax (AUC = 0.616, P = 0.046). Decision curve analysis demonstrated superior net clinical benefit compared to univariable models.

Conclusion: A multivariable model integrating FDG PET/CT-derived metabolic features with clinical parameters enables noninvasive prediction of colorectal adenomas. This imaging-based approach may help identify individuals most likely to benefit from colonoscopy, potentially improving the efficiency of CRC screening strategies in opportunistic or high-risk settings.

Objective: This study aimed to employ RadioTherapy extension of the Particle and Heavy Ion Transport code System (RT-PHITS) Monte Carlo (MC) simulation for estimating absorbed doses in target organs and tumors in patients administered with ¹⁷⁷Lu-DOTATATE, using single-photon emission computed tomography/computed tomography (SPECT/CT) imaging.

Methods: Quantitative SPECT/CT images were obtained from 17 patients across the abdominal region at four time points: approximately 4, 24, 72, and 120 h following the administration of ¹⁷⁷Lu-DOTATATE. The liver, spleen, left and right kidneys, and total kidneys were automatically segmented on the CT images using the TotalSegmentator tool. Tumors were manually delineated based on SPECT/CT images. Image registration was performed using an Elastix-based method, with the first SPECT/CT time point serving as the reference. Voxel-level time-integrated activity (TIA) maps were created by fitting mono-exponential functions. These TIA maps, together with the reference CT images, were input into RT-PHITS to calculate dose distributions. The absorbed doses calculated by RT-PHITS were compared with those from IDAC-Dose 2.1 through two approaches: first, by using independently derived time-integrated activity coefficients (TIACs) from each method to assess the combined effects of kinetic modeling and dose calculation technique; second, by applying the same TIACs-obtained from time-integrated activity data-to both methods to isolate the influence of the dose calculation approach.

Results: RT-PHITS yielded higher mean absorbed doses per unit of administered activity compared to IDAC-Dose. The relative differences ranged between 0.63% and 15.35%, with the right kidney showing the largest discrepancy. When the same time-integrated data were used for both RT-PHITS and IDAC-Dose, relative differences remained below 10.40%.

Conclusion: RT-PHITS is a capable tool for calculating absorbed doses in ¹⁷⁷Lu-DOTATATE therapy. It consistently produced higher dose estimates than the organ-based method, emphasizing the benefits of patient-specific dosimetry, especially in organs that contain or are near tumors.