ABSTRACT
Background: Single-photon emission computed tomography-computed tomography (SPECT/CT) quantification has emerged as a valuable tool for assessing disease prognosis by accurately identifying and characterizing abnormal lesions with accumulated radionuclides. Papillary thyroid carcinoma (PTC) is the most prevalent type of thyroid cancer, and radioactive iodine (RAI) therapy is a standard treatment following total thyroidectomy. This study aimed to explore the potential utility the quantitative parameters of the thyroid bed under iodine-131 (I-131) SPECT/CT in the efficacy of RAI adjuvant therapy for patients with PTC. Methods: The retrospective cohort study enrolled 107 patients with PTC who underwent RAI adjuvant therapy from June 2020 to January 2023. Three days after the RAI adjuvant therapy, all patients underwent I-131 whole-body scans and SPECT/CT imaging. The quantitative parameters, including maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), and percent injected dose (%ID), were measured using image analysis software based on I-131 SPECT/CT thyroid bed uptake. Successful therapy was defined as inhibitory thyroglobulin (Tg) <0.2 ng/mL with negative thyroglobulin antibody (TgAb) and negative imaging examination 6 months after RAI adjuvant therapy. The relationship between the quantitative parameters and the treatment efficacy, in addition to the potential influencing factors, were analyzed. Results: The quantitative parameters from the successful group [SUVmax: median 6.15 g/mL, interquartile range (IQR) 2.34-13.80 g/mL; SUVmean: median 2.02 g/mL, IQR 0.89-4.93 g/mL; %ID: median 2.00%, IQR 1.00-4.00%] were significantly lower than those from the unsuccessful group (SUVmax: median 19.03 g/mL, IQR 5.31-45.10 g/mL, SUVmean 4.64 g/mL, IQR 2.07-19.05 g/mL; %ID: median 8.00%, IQR 3.00-18.00%) (SUVmax: Z=-3.755; SUVmean; Z=-3.671; %ID: Z=-4.070; all P values <0.001). SUVmax, SUVmean and %ID were positively correlated with the stimulated thyroglobulin (sTg) and inhibitory Tg at 6 months after RAI adjuvant therapy, respectively (all P values <0.001). SUVmax [odds ratio (OR) =1.045], SUVmean (OR =1.130), and %ID (OR =1.092) were predictive factors for the failure of RAI adjuvant therapy (all P values <0.001). Conclusions: Our study suggested that quantitative parameters (SUVmax, SUVmean, and %ID) derived from I-131 SPECT/CT imaging of the thyroid bed can serve as useful tools for predicting therapy outcomes following RAI adjuvant therapy.
ABSTRACT
Purpose: The purpose of this study was to explore the application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) image radiomics in the identification of spine multiple myeloma (MM) and bone metastasis (BM), and whether this method could improve the classification diagnosis performance compared with traditional methods. Methods: This retrospective study collected a total of 184 lesions from 131 patients between January 2017 and January 2021. All images were visually evaluated independently by two physicians with 20 years of experience through the double-blind method, while the maximum standardized uptake value (SUVmax) of each lesion was recorded. A total of 279 radiomics features were extracted from the region of interest (ROI) of CT and PET images of each lesion separately by manual method. After the reliability test, the least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation were used to perform dimensionality reduction and screening of features. Two classification models of CT and PET were derived from CT images and PET images, respectively and constructed using the multivariate logistic regression algorithm. In addition, the ComModel was constructed by combining the PET model and the conventional parameter SUVmax. The performance of the three classification diagnostic models, as well as the human experts and SUVmax, were evaluated and compared, respectively. Results: A total of 8 and 10 features were selected from CT and PET images for the construction of radiomics models, respectively. Satisfactory performance of the three radiomics models was achieved in both the training and the validation groups (Training: AUC: CT: 0.909, PET: 0.949, ComModel: 0.973; Validation: AUC: CT: 0.897, PET: 0.929, ComModel: 0.948). Moreover, the PET model and ComModel showed significant improvement in diagnostic performance between the two groups compared to the human expert (Training: P = 0.01 and P = 0.001; Validation: P = 0.018 and P = 0.033), and no statistical difference was observed between the CT model and human experts (P = 0.187 and P = 0.229, respectively). Conclusion: The radiomics model constructed based on 18F-FDG PET/CT images achieved satisfactory diagnostic performance for the classification of MM and bone metastases. In addition, the radiomics model showed significant improvement in diagnostic performance compared to human experts and PET conventional parameter SUVmax.