Objective To observe the value of 18F-D3FSP PET/CT machine learning(ML)models for evaluating subjective cognitive decline(SCD).Methods Thirty-two SCD patients(SCD group)and 16 healthy volunteers(control group)who received 18F-D3FSP PET/CT were selected from Sino Longitudinal Study on Cognitive Decline(SILCODE)and divided into training set(n=34)and test set(n=14)at a ratio of 7∶3.Support vector machine(SVM),random forest(RF)and logistic regression(LR)models were constructed based on Hamilton anxiety scale(HAMA)and standard uptake value ratio(SUVR)of brain regions being significantly different between groups to evaluate SCD.Then PET/CT data were amplified by format conversion and divided into training set(including 8 775 CT images and 1 833 PET images)and test set(including 2 025 CT images and 423 PET images)at the ratio of 8∶2.VGG16 models were constructed based on CT and PET images to evaluate SCD,respectively.Results The area under the receiver operating characteristic curve(AUC)of SVM,RF and LR model for evaluating SCD in training set was all 1.000,while was 0.863,0.872 and 1.000 in test set,respectively.LR model was overfitting,and RF model had better performance.The accuracy of VGG16 model for evaluating SCD based on CT and PET images tended to be stable after the 175th and 150th iterations,with the highest accuracy of 67.11％ and 65.35％ in training set,which tended to be stable after the 165th and 145th iterations,with the highest accuracy of 62.43％ and 59.16％ in test set,respectively.Conclusion 18F-D3FSP PET/CT ML models could be used to evaluate SCD.

Renal metastasis from differentiated thyroid cancer (DTC) is uncommon and is hard to be distinguished from primary renal cell carcinoma. There is no consensus on diagnosis and treatment, and decisions about surgery and 131I therapy doses usually depend on experience. To better understand clinical characteristics and current treatment status, this study systematically reviews existing studies, exploring epidemiological features, clinical symptoms, imaging findings (including lesions characteristics across various imaging modalities), therapeutic strategies, and prognosis of DTC renal metastases, providing evidence-based references for clinical practice.

Objective To observe the value of 18F-D3FSP PET/CT machine learning(ML)models for evaluating subjective cognitive decline(SCD).Methods Thirty-two SCD patients(SCD group)and 16 healthy volunteers(control group)who received 18F-D3FSP PET/CT were selected from Sino Longitudinal Study on Cognitive Decline(SILCODE)and divided into training set(n=34)and test set(n=14)at a ratio of 7∶3.Support vector machine(SVM),random forest(RF)and logistic regression(LR)models were constructed based on Hamilton anxiety scale(HAMA)and standard uptake value ratio(SUVR)of brain regions being significantly different between groups to evaluate SCD.Then PET/CT data were amplified by format conversion and divided into training set(including 8 775 CT images and 1 833 PET images)and test set(including 2 025 CT images and 423 PET images)at the ratio of 8∶2.VGG16 models were constructed based on CT and PET images to evaluate SCD,respectively.Results The area under the receiver operating characteristic curve(AUC)of SVM,RF and LR model for evaluating SCD in training set was all 1.000,while was 0.863,0.872 and 1.000 in test set,respectively.LR model was overfitting,and RF model had better performance.The accuracy of VGG16 model for evaluating SCD based on CT and PET images tended to be stable after the 175th and 150th iterations,with the highest accuracy of 67.11％ and 65.35％ in training set,which tended to be stable after the 165th and 145th iterations,with the highest accuracy of 62.43％ and 59.16％ in test set,respectively.Conclusion 18F-D3FSP PET/CT ML models could be used to evaluate SCD.

Renal metastasis from differentiated thyroid cancer (DTC) is uncommon and is hard to be distinguished from primary renal cell carcinoma. There is no consensus on diagnosis and treatment, and decisions about surgery and 131I therapy doses usually depend on experience. To better understand clinical characteristics and current treatment status, this study systematically reviews existing studies, exploring epidemiological features, clinical symptoms, imaging findings (including lesions characteristics across various imaging modalities), therapeutic strategies, and prognosis of DTC renal metastases, providing evidence-based references for clinical practice.