Annals of Nuclear Medicine最新文献

Objective: To determine whether software-based de-filtering can restore the quantitative accuracy of the bone scan index (BSI) and the number of hot spots (HSn) in whole-body scintigraphy images degraded by the Clarity2D noise-reduction filter.

Methods: In this IRB-approved retrospective study, 101 adults (mean age ± SD: 67 ± 13 years) who underwent ^99mTc-HMDP whole-body scintigraphy on a cadmium-zinc-telluride (CZT) SPECT/CT system were analyzed. For each patient, three planar datasets were obtained: (i) unfiltered images, (ii) 40%-blend Clarity2D-filtered images, and (iii) software de-filtered images reconstructed with a deep learning-based inverse filter in VSBONE BSI v3.0. Quantitative indices (BSI and HSn) and lesion masks were automatically extracted. Agreement with the unfiltered reference was evaluated using Pearson correlation, Bland-Altman analysis (bias ± 95% limits), Dice coefficient, and the Hausdorff distance (p < 0.05). Additionally, lesion detection accuracy was quantified using intersection over union (IoU)-based matching to calculate precision, recall, and F1-score.

Results: Clarity2D filtering significantly impaired quantitative concordance (BSI r = 0.23, bias = - 1.55 [-6.20 to 3.10]; HSn r = 0.23, bias = - 14.4 lesions). In contrast, de-filtering restored concordance (BSI r = 0.99, bias = - 0.04 [-0.26 to 0.17]; HSn r = 0.98, bias = - 0.04 lesions) and improved spatial overlap (Dice 0.40 to 0.82) while reducing the median Hausdorff distance from 103 pixels (IQR 85-188) to 39 pixels (IQR 1-40) (all p < 0.001). The de-filtered method demonstrated superior lesion detection accuracy compared to Clarity2D (Precision: 0.77 ± 0.37 vs. 0.19 ± 0.29, Recall: 0.81 ± 0.37 vs. 0.43 ± 0.44, F1-score: 0.78 ± 0.36 vs. 0.23 ± 0.30). Furthermore, de-filtering achieved high inter-case stability (median F1-score: 1.0), whereas Clarity2D showed substantial variability (median F1-score: 0.057).

Conclusions: The proposed de-filtering algorithm reliably reverses Clarity2D-induced distortions, enabling accurate BSI and HSn measurements and robust lesion detection without additional radiation or acquisition time. This technique has the potential to broaden the clinical adoption of noise-reduction filters while preserving the integrity of downstream quantitative analyses.

Background: Quantitative analysis of amyloid positron emission tomography (PET) is increasingly applied in clinical and research settings; however, its consistency across software platforms remains uncertain. This study aimed to compare standardized uptake value ratio (SUVr) measurements obtained from CortexID Suite and VIZCalc, to evaluate their concordance with expert visual assessment, and to assess the concordance of Centiloid values derived from VIZCalc with the visual reference.

Methods: We retrospectively analyzed 116 patients who underwent ¹⁸F-flutemetamol PET at a single institution. SUVr values were calculated using both CortexID Suite and VIZCalc, while Centiloid values were derived from VIZCalc only. Visual assessments were performed by two nuclear medicine physicians. Correlations among indices were examined using Pearson's correlation. Agreement between SUVr values was assessed with Bland-Altman analysis. Agreement with the non-independent visual reference was evaluated using receiver operating characteristic (ROC) analysis, and areas under the curves (AUCs) were compared with DeLong's test.

Results: SUVr values from CortexID and VIZCalc were strongly correlated (r = 0.986, p < 0.001), with a small mean difference of + 0.0397. Both platforms showed high concordance with the non-blinded visual assessment (AUC: 0.991 for CortexID; 0.989 for VIZCalc). Centiloid values also showed high agreement with the visual reference (AUC: 0.994) and were strongly correlated with SUVr values (r = 0.975 for CortexID; r = 0.965 for VIZCalc, p < 0.001). No significant difference was observed between platforms (p = 0.84).

Conclusions: CortexID Suite and VIZCalc demonstrated high concordance with the non-blinded visual assessment and showed consistent quantitative trends. Both platforms can be reliably applied for amyloid burden quantification, provided that software-specific characteristics are appropriately considered.

Purpose: Although papillary thyroid carcinoma (PTC) is generally associated with a favorable prognosis, progression to radioactive iodine-refractory (RAIR) disease in metastatic cases leads to significantly poorer clinical outcomes. This study aimed to analyze the clinical value of clinicopathological, pre-operative ultrasonographic features, and fibroblast activation protein (FAP) immunoreactivity scores for the pretherapeutic prediction of the efficacy of radioiodine (RAI, ¹³¹I) treatment in PTC.

Methods: A retrospective analysis was conducted on the medical records, clinicopathological data, and pre-operative ultrasonographic imaging of 167 PTC patients treated with ¹³¹I (113 clinical complete remission group, 54 RAIR group). Their specimens were collected for FAP immunohistochemical staining and scoring. Statistical analyses were performed to identify RAIR risk factors and a predictive model for RAIR PTC was established.

Results: Binary logistic regression analysis revealed that a maximum tumor diameter of ≥ 17.5 mm, microcalcifications, and a FAP immunoreactivity score of ≥ 3.44 were identified as independent risk factors for RAIR PTC. The combined model showed high sensitivity (75.9%), specificity (77.0%), and accuracy (AUC = 0.812) in the pretherapeutic prediction of ¹³¹I therapeutic efficacy in PTC. Furthermore, calibration curve and decision curve analysis (DCA) confirmed that the combined predictive model exhibited good accuracy and clinical utility.

Conclusion: Clinical significance was observed in the clinicopathological characteristics, ultrasonographic features of PTC and FAP immunoreactivity scores for evaluating ¹³¹I therapeutic efficacy. High sensitivity, specificity, and diagnostic accuracy were achieved when a maximum tumor diameter of ≥ 17.5 mm, microcalcifications, and a FAP immunoreactivity score of ≥ 3.44 were combined for assessment.

Purpose: Assessing the prognostic value of ¹⁸F-fluorodeoxyglucose (FDG) PET/CT in patients with pulmonary invasive mucinous adenocarcinoma (IMA).

Methods: This dual-centre retrospective study included patients diagnosed with pulmonary IMA between January 2010 and August 2020. The patients were categorized into three groups based on CT morphology: solitary pulmonary nodule (SPN), pneumonic, and multifocal types. FDG avidity of the primary lesion was visually assessed using the mediastinal blood pool as a reference. Disease-free survival (DFS) was analysed in patients who underwent curative surgical resection.

Results: A total of 139 patients (mean age: 69.1 years) were included. Most patients had SPN-type tumors (63.3%), of which 60.2% were FDG-avid, whereas all patients with pneumonic-type (18.0%) were FDG-avid. DFS analysis was performed in 78 surgically treated patients, with recurrence observed in 18 cases. Univariate analysis identified T stage, nodal involvement, CT imaging phenotype, visceral pleural invasion (VPI), and FDG avidity as significant prognostic factors. In a multivariate analysis, CT imaging phenotype, VPI, and FDG avidity remained independent prognostic factors. When patients were stratified into three groups-non-FDG-avid SPN-type, FDG-avid SPN-type, and pneumonic-type-Kaplan-Meier analysis demonstrated a significantly longer DFS in non-FDG-avid SPN-type patients than in the other groups. The median DFS was not reached for non-FDG-avid or FDG-avid SPN-type groups, whereas it was 21.0 months for patients in the pneumonic-type group.

Conclusion: Utilization of FDG PET/CT, particularly when combined with CT findings, may enhance the prognostic stratification of patients with curatively resected IMA of the lung, as visual FDG avidity is associated with worse prognosis.