Annals of Nuclear Medicine最新文献

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.

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.