Objective:To explore the application value of radiomics, deep learning, and their combined models in predicting the efficacy of radioiodine adjuvant therapy in patients with papillary thyroid cancer (PTC).Methods:A retrospective analysis was conducted on the clinical and imaging data of 131 PTC patients (38 males, 93 females; age 41(33, 48) years) who received first 131I treatment at the Affiliated Hospital of Guilin Medical University from January 2018 to March 2023. Patients were randomly divided into a training set ( n=105) and a test set ( n=26) at the ratio of 8∶2. Multivariate logistic regression analysis was used to screen clinical features to determine independent predictors affecting the efficacy of 131I therapy. Radiomics and deep learning features were extracted from the enhanced CT scans and were combined by using the extremely randomized trees (ExtraTrees) algorithm to construct radiomics, deep learning, and combined models. The predictive abilities of the models were evaluated by AUC, and the Delong test was applied to compare the difference between AUCs. Results:Higher pre-ablation stimulated thyroglobulin (ps-Tg) levels (odds ratio( OR)=1.060, 95% CI: 1.025-1.095, P=0.004) and bilateral lesions ( OR=5.085, 95% CI: 1.452-17.814, P=0.033) were independent predictors of the efficacy of 131I therapy in intermediate to high-risk PTC patients. In the training set, the radiomics model (AUC=0.853) and combined model (AUC=0.880) significantly outperformed the deep learning model (AUC=0.711; Z values: 2.48, 3.09, P values: 0.013, 0.002), while there was no statistically significant difference between the radiomics and combined models ( Z=0.51, P=0.610). In the test set, AUCs of the radiomics, deep learning, and combined models were 0.746, 0.624, and 0.876, respectively, and the AUC of the combined model was higher than that of the radiomics model or deep learning model ( Z values: 2.05, 1.99, P values: 0.040, 0.047). Conclusion:The combined model demonstrates superior performance over the standalone radiomics model and deep learning model in predicting the efficacy of 131I treatment in PTC patients.

Objective:To explore the application value of radiomics, deep learning, and their combined models in predicting the efficacy of radioiodine adjuvant therapy in patients with papillary thyroid cancer (PTC).Methods:A retrospective analysis was conducted on the clinical and imaging data of 131 PTC patients (38 males, 93 females; age 41(33, 48) years) who received first 131I treatment at the Affiliated Hospital of Guilin Medical University from January 2018 to March 2023. Patients were randomly divided into a training set ( n=105) and a test set ( n=26) at the ratio of 8∶2. Multivariate logistic regression analysis was used to screen clinical features to determine independent predictors affecting the efficacy of 131I therapy. Radiomics and deep learning features were extracted from the enhanced CT scans and were combined by using the extremely randomized trees (ExtraTrees) algorithm to construct radiomics, deep learning, and combined models. The predictive abilities of the models were evaluated by AUC, and the Delong test was applied to compare the difference between AUCs. Results:Higher pre-ablation stimulated thyroglobulin (ps-Tg) levels (odds ratio( OR)=1.060, 95% CI: 1.025-1.095, P=0.004) and bilateral lesions ( OR=5.085, 95% CI: 1.452-17.814, P=0.033) were independent predictors of the efficacy of 131I therapy in intermediate to high-risk PTC patients. In the training set, the radiomics model (AUC=0.853) and combined model (AUC=0.880) significantly outperformed the deep learning model (AUC=0.711; Z values: 2.48, 3.09, P values: 0.013, 0.002), while there was no statistically significant difference between the radiomics and combined models ( Z=0.51, P=0.610). In the test set, AUCs of the radiomics, deep learning, and combined models were 0.746, 0.624, and 0.876, respectively, and the AUC of the combined model was higher than that of the radiomics model or deep learning model ( Z values: 2.05, 1.99, P values: 0.040, 0.047). Conclusion:The combined model demonstrates superior performance over the standalone radiomics model and deep learning model in predicting the efficacy of 131I treatment in PTC patients.

BACKGROUND:Molars are characterized by multi-root, multi-root canal, multi-directional, different geometric shape and distribution. Single-root canal teeth post-core theory was used to guide molar repair in the clinic. It is easy to cause root canal perforation or vertical fracture due to excessive post preparation. Therefore, it is necessary to make further study and investigation in the design of fiber post-resin core for repairing molars. OBJECTIVE: To evaluate the clinical therapeutic effects of fiber post-resin core with different numbers of posts in the repair of molar residual roots and crowns. METHODS: A total of 54 human molar residual roots and crowns with sound root canal filing in 48 patients were selected and restored with fiber post of different numbers and resin core as wel as complete coronal restoration. There were 17 cases (20 samples) restored with single fiber post core, 16 cases (18 samples) restored with double fiber post cores, and 15 cases (16 samples) restored with three fiber post cores. They were folowed up for 24 months and the repair results were compared. RESULTS AND CONCLUSION:After 24 months of folow-up, the success rates were 85%, 94% and 94% in the single fiber post, double fiber post and three fiber post groups, respectively, and no significant difference was detected among the three groups. Five failures were observed among 54 teeth: three cases of fiber post shedding in the single fiber post group, one case of fiber post shedding in the double fiber post group, and one case of fiberpost shedding in the three fiber post group, and no root fracture occurred. Three kinds of fiber post-resin cores for repairing molar residual roots and crowns can get a better short-term clinical result. The repair effects were not different because of the different numbers of fiber posts.

Fracture of multiple implants is rare.This report describes 1 case with the fracture of multiple implants related to occlusal over-load,bruxism,periodontitis and other factors in the posterior position.The fractured implants were removed and immediate reimplantation was performed.The panoramic film showed that the implants were integrated successfully 12 months after prosthesis.