Objective To explore the current situation and experience of the development of multi-hospital areas in foreign medical institutions,and to analyze its enlightenment to the construction of"one hospital with multiple campuses"in public hospitals in China.Methods Through the combing of relevant literature,it systematically analyzes the development status of multi-hospital construction of medical institutions in typical countries such as the United States,the United Kingdom,and Germany,summarizes the relevant experience of different countries,and analyzes the current situation of the construction of"one hospital with multiple campuses"in China's public hospitals.Results At present,the orderly development of multiple hospitals of foreign medical institutions mainly depends on the homogenization of medical care,the scientific management of human resources and the improvement of information construction.China can learn from its experience and technical means to build a development pattern of"one hospital with multiple campuses"suitable for China's national conditions.Conclusion In the future,the construction of"one hospital with multiple campuses"in China's public hospitals should focus on"rationalization of human resource allocation,homogenization of medical service quality,and intelligent information system construction",improve"human resource allocation",establish and improve"information sharing mechanism",differentiate the layout of"hospital functions",and strengthen"quality supervision and patient feedback",aiming to improve the construction effect of"one hospital with multiple campuses"in China's public hospitals.

Objective:To investigate the prognostic value of 18F-FDG PET/CT-based habitat radiomics combined with stacking ensemble learning model in overall survival (OS) of patients with hepatocellular carcinoma (HCC). Methods:A total of 136 HCC patients (114 males, 22 females, age (55.3±10.4) years) who underwent 18F-FDG PET/CT before treatment between January 2018 and January 2023 were retrospectively analyzed. Eighty-five cases from Tianjin First Central Hospital and 51 cases from Tianjin Medical University Cancer Institute and Hospital were used as a training cohort and an external validation cohort, respectively. The tumor volume of interest (VOI) was delineated on PET and CT images, and a total of 4 habitats were segmented by using the Otsu algorithm, including PET high ∩ CT low, PET low ∩ CT low, PET high ∩ CT high, and PET low ∩ CT high. After the feature selection, a total of 36 stacking ensemble learning models were established, and the optimal model was selected based on the calculated concordance index (C-index). Moreover, a combined model was developed by integrating the optimal model with clinical information. The predictive efficacy of those models was assessed by time-dependent ROC curves. Results:The model based on PET high ∩ CT high habitat radiomics features with multilayer perceptron (MLP) classifier had the highest C-index (0.770) in the external validation cohort, and it was regarded as the optimal radiomics model. The combined model incorporating this model with clinical information achieved an improved C-index of 0.815 in the external validation cohort. The combined model outperformed the other models for OS prediction, with a time-dependent AUC of 0.919, 0.900, and 0.862 in predicting the 1-year, 2-year, and 3-year OS, respectively. Conclusions:18F-FDG PET/CT-based habitat analysis outperforms traditional radiomics in OS prediction for HCC patients. By integrating the optimal habitat model with the clinical model, the combined model is able to improve the predictive efficacy.

Objective:To explore the value of a model based on 18F-FDG PET/CT radiomics features in assessing the prognosis of patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer undergoing neoadjuvant targeted chemotherapy. Methods:This retrospective analysis included 132 female patients (age (50±11) years) diagnosed with HER2-positive breast cancer who underwent 18F-FDG PET/CT prior to treatment between January 2016 and August 2022 in Tianjin Medical University Cancer Institute and Hospital. Data were split into training (105 cases) and validation (27 cases) cohorts using stratified sampling (8∶2). Clinical pathological data and progression-free survival (PFS) were recorded. PET and CT images were annotated for lesion delineation and radiomics features extraction. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select features in the training cohort, and the radiomics score (Rad-score) was calculated. Cox proportional hazards regression analysis was performed to identify risk factors for PFS. A nomogram model was constructed, and the concordance index (C-index) was calculated to assess predictive performance. Results:Univariate Cox regression showed that N stage (hazard ratio ( HR)=2.36, 95% CI: 1.04-5.37, P=0.040) and Rad-score ( HR=14.50, 95% CI: 3.39-62.13, P<0.001) were related to PFS in patients with HER2-positive breast cancer after neoadjuvant therapy. Multivariate analysis indicated the Rad-score as an independent risk factor for PFS ( HR=13.32, 95% CI: 3.10-57.20, P<0.001). The nomogram model combining N stage and Rad-score predicted PFS more accurately than the Rad-score model alone, with C-indexes of 0.80 vs 0.74 in the training cohort, and 0.77 vs 0.71 in the validation cohort. Conclusions:Radiomics based on pre-treatment 18F-FDG PET/CT can predict PFS in patients with HER2-positive breast cancer undergoing neoadjuvant targeted chemotherapy. The nomogram model combining radiomics features and clinical risk factor improves prognostic prediction.

Objective To explore the current situation and experience of the development of multi-hospital areas in foreign medical institutions,and to analyze its enlightenment to the construction of"one hospital with multiple campuses"in public hospitals in China.Methods Through the combing of relevant literature,it systematically analyzes the development status of multi-hospital construction of medical institutions in typical countries such as the United States,the United Kingdom,and Germany,summarizes the relevant experience of different countries,and analyzes the current situation of the construction of"one hospital with multiple campuses"in China's public hospitals.Results At present,the orderly development of multiple hospitals of foreign medical institutions mainly depends on the homogenization of medical care,the scientific management of human resources and the improvement of information construction.China can learn from its experience and technical means to build a development pattern of"one hospital with multiple campuses"suitable for China's national conditions.Conclusion In the future,the construction of"one hospital with multiple campuses"in China's public hospitals should focus on"rationalization of human resource allocation,homogenization of medical service quality,and intelligent information system construction",improve"human resource allocation",establish and improve"information sharing mechanism",differentiate the layout of"hospital functions",and strengthen"quality supervision and patient feedback",aiming to improve the construction effect of"one hospital with multiple campuses"in China's public hospitals.

Objective:To investigate the prognostic value of 18F-FDG PET/CT-based habitat radiomics combined with stacking ensemble learning model in overall survival (OS) of patients with hepatocellular carcinoma (HCC). Methods:A total of 136 HCC patients (114 males, 22 females, age (55.3±10.4) years) who underwent 18F-FDG PET/CT before treatment between January 2018 and January 2023 were retrospectively analyzed. Eighty-five cases from Tianjin First Central Hospital and 51 cases from Tianjin Medical University Cancer Institute and Hospital were used as a training cohort and an external validation cohort, respectively. The tumor volume of interest (VOI) was delineated on PET and CT images, and a total of 4 habitats were segmented by using the Otsu algorithm, including PET high ∩ CT low, PET low ∩ CT low, PET high ∩ CT high, and PET low ∩ CT high. After the feature selection, a total of 36 stacking ensemble learning models were established, and the optimal model was selected based on the calculated concordance index (C-index). Moreover, a combined model was developed by integrating the optimal model with clinical information. The predictive efficacy of those models was assessed by time-dependent ROC curves. Results:The model based on PET high ∩ CT high habitat radiomics features with multilayer perceptron (MLP) classifier had the highest C-index (0.770) in the external validation cohort, and it was regarded as the optimal radiomics model. The combined model incorporating this model with clinical information achieved an improved C-index of 0.815 in the external validation cohort. The combined model outperformed the other models for OS prediction, with a time-dependent AUC of 0.919, 0.900, and 0.862 in predicting the 1-year, 2-year, and 3-year OS, respectively. Conclusions:18F-FDG PET/CT-based habitat analysis outperforms traditional radiomics in OS prediction for HCC patients. By integrating the optimal habitat model with the clinical model, the combined model is able to improve the predictive efficacy.

Objective:To explore the value of a model based on 18F-FDG PET/CT radiomics features in assessing the prognosis of patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer undergoing neoadjuvant targeted chemotherapy. Methods:This retrospective analysis included 132 female patients (age (50±11) years) diagnosed with HER2-positive breast cancer who underwent 18F-FDG PET/CT prior to treatment between January 2016 and August 2022 in Tianjin Medical University Cancer Institute and Hospital. Data were split into training (105 cases) and validation (27 cases) cohorts using stratified sampling (8∶2). Clinical pathological data and progression-free survival (PFS) were recorded. PET and CT images were annotated for lesion delineation and radiomics features extraction. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select features in the training cohort, and the radiomics score (Rad-score) was calculated. Cox proportional hazards regression analysis was performed to identify risk factors for PFS. A nomogram model was constructed, and the concordance index (C-index) was calculated to assess predictive performance. Results:Univariate Cox regression showed that N stage (hazard ratio ( HR)=2.36, 95% CI: 1.04-5.37, P=0.040) and Rad-score ( HR=14.50, 95% CI: 3.39-62.13, P<0.001) were related to PFS in patients with HER2-positive breast cancer after neoadjuvant therapy. Multivariate analysis indicated the Rad-score as an independent risk factor for PFS ( HR=13.32, 95% CI: 3.10-57.20, P<0.001). The nomogram model combining N stage and Rad-score predicted PFS more accurately than the Rad-score model alone, with C-indexes of 0.80 vs 0.74 in the training cohort, and 0.77 vs 0.71 in the validation cohort. Conclusions:Radiomics based on pre-treatment 18F-FDG PET/CT can predict PFS in patients with HER2-positive breast cancer undergoing neoadjuvant targeted chemotherapy. The nomogram model combining radiomics features and clinical risk factor improves prognostic prediction.

Objective To observe the value of PET/CT radiomics for predicting Ki-67 expression level of non-small cell lung carcinoma(NSCLC).Methods 18F-FDG PET/CT data of 139 NSCLC patients were retrospectively analyzed.The patients were divided into high-expression group(≥40%,n=75)and low-expression group(＜40%,n=64)according to Ki-67 expression level of NSCLC.CT,PET and PET/CT data were divided into training set and test set at a ratio of 7∶3 and make the distribution of Ki-67 expression levels balanced between sets,respectively.CT,PET and PET/CT radiomics features of NSCLC were extracted,and the optimal radiomics features were screened,then random forest(RF),categorical boosting(CatBoost)and extreme gradient boosting(XGBoost)algorithms were used to construct models,respectively.Receiver operating characteristic curve was plotted,and the area under the curve(AUC)was calculated to screen the radiomics model with the highest efficacy for predicting Ki-67 expression level of NSCLC.Results RF models had the highest performance among radiomics models constructed based on CT,PET and PET/CT.The efficacy of RFCT,RFPET and RFPET/CT models for predicting Ki-67 expression level of NSCLC in test set increased sequentially,with AUC of 0.830,0.870 and 0.940,respectively(all P＜0.05),and RFPET/CT was the best radiomics model.Conclusion PET/CT radiomics could be used to effectively predict Ki-67 expression level of NSCLC,and RFPET/CT model had the best performance.

Objective To observe the value of PET/CT radiomics for predicting Ki-67 expression level of non-small cell lung carcinoma(NSCLC).Methods 18F-FDG PET/CT data of 139 NSCLC patients were retrospectively analyzed.The patients were divided into high-expression group(≥40%,n=75)and low-expression group(＜40%,n=64)according to Ki-67 expression level of NSCLC.CT,PET and PET/CT data were divided into training set and test set at a ratio of 7∶3 and make the distribution of Ki-67 expression levels balanced between sets,respectively.CT,PET and PET/CT radiomics features of NSCLC were extracted,and the optimal radiomics features were screened,then random forest(RF),categorical boosting(CatBoost)and extreme gradient boosting(XGBoost)algorithms were used to construct models,respectively.Receiver operating characteristic curve was plotted,and the area under the curve(AUC)was calculated to screen the radiomics model with the highest efficacy for predicting Ki-67 expression level of NSCLC.Results RF models had the highest performance among radiomics models constructed based on CT,PET and PET/CT.The efficacy of RFCT,RFPET and RFPET/CT models for predicting Ki-67 expression level of NSCLC in test set increased sequentially,with AUC of 0.830,0.870 and 0.940,respectively(all P＜0.05),and RFPET/CT was the best radiomics model.Conclusion PET/CT radiomics could be used to effectively predict Ki-67 expression level of NSCLC,and RFPET/CT model had the best performance.

Objective:To compare the imaging quality and metabolic quantitative parameters of pulmonary nodules between Q. Flex whole information five-dimensional (5D) and conventional three-dimensional (3D) PET/CT imaging for clinical evaluation.Methods:Fifty-four patients (30 males, 24 females, age: 60(42, 75) years; 78 solid pulmonary nodules (maximum diameter≤3 cm) with abnormal uptake of 18F-FDG) from Tianjin Cancer Hospital Airport Hospital between June 2022 and August 2022 were enrolled in this retrospective study. All patients underwent 5D scanning and 3D, 5D reconstruction. Image quality scores, signal-to-noise ratio (SNR), SUV max, SUV mean and metabolic tumor volume (MTV) of pulmonary nodules of 5D group and 3D group were evaluated and compared with χ2 test and Wilcoxon signed rank test. Correlation of quantitative parameters between 2 groups were analyzed by using Spearman rank correlation analysis. Results:Thirty-five of 78(45%) pulmonary nodules with image quality score≥4 were found in 5D group, which were more than those in 3D group (22/78(28%); χ2=4.67, P=0.031). Meanwhile, SNR, SUV max, SUV mean, and MTV were significantly positively correlated between the 2 groups ( rs values: 0.86, 0.86, 0.85, and 0.95, all P<0.001). SNR, SUV max and SUV mean of pulmonary nodules in 5D group were significantly higher than those in 3D group, which were 37.46(18.42, 62.00) vs 32.72(16.97, 54.76) ( z=-4.07, P<0.001), 9.71(5.48, 13.82) vs 8.96(4.82, 12.63) ( z=-3.05, P<0.001) and 6.30(3.39, 8.94) vs 5.61(2.99, 7.63)( z=-4.07, P<0.001) respectively. MTV of pulmonary nodules in 5D group was significantly lower than that in 3D group, which was 1.72(0.66, 2.74) cm 3vs 1.98(1.06, 4.63) cm 3 ( z=-7.13, P<0.001). Quantitative parameters of lower lung field and nodules with maximum diameters of >10 mm and ≤20 mm based on 5D scanning changed most significantly compared with those based on 3D scanning ( z values: from -5.23 to -2.48, all P<0.05). Conclusion:Q. Flex 5D PET significantly improves the quantitative accuracy of SUV and MTV of pulmonary nodules, and the improvement of image quality is substantial without increasing the radiation dose, which has clinical practical value.

Objective:To explore the predictive value of 18F-FDG PET/CT in molecular subtyping of triple-negative breast cancer. Methods:A retrospective analysis was performed on the clinical and imaging data of 227 breast cancer patients who underwent 18F-FDG PET/CT examination in the Tianjin Medical University Cancer Institute & Hospital from January 1, 2010 to December 31, 2022. Based on the expression levels of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) in the primary breast cancer, the patients were categorized into two groups: triple-negative breast cancer (TNBC) and non-TNBC. Radiomic features were extracted from images of both groups, and a radiomic model was constructed to predict the molecular subtype of the TNBC groups. In addition, the clinical data, CT morphological features, and PET metabolic parameters of both groups were compared to determine the indicators with statistically significant differences and develop a comprehensive radiomic model combined with clinical characteristics. Results:Compared to the non-TNBC group, the TNBC groups exhibited more significant invasiveness in terms of tumor diameter, margins, ipsilateral axillary lymph node metastasis, invasion of neighboring skin or papillae, and PET metabolic parameters ( t = -3.19; χ2 = 7.30, 8.10, 5.34; t = 3.80, 3.30, 3.42, P < 0.05). The constructed 18F-FDG PET/CT radiomic model proved effective in predicting the molecular subtype of the TNBC group, and the receiver operating characteristic (ROC) curve showed an area under the curve (AUC) of 0.83 (95% CI 0.78-0.88), an accuracy of 75.9%, a sensitivity of 74.5%, and a specificity of 77.2%. In contrast, the constructed comprehensive radiomic model displayed an AUC of 0.86 (95% CI 0.81-0.90), an accuracy of 77.2%, a sensitivity of 78.6%, and a specificity of 75.9%. Conclusions:18F-FDG PET/CT plays an important role in predicting molecular subtypes of TNBC. The constructed radiomic model and comprehensive radiomic model can further enhance the prediction efficacy of PET metabolic parameters and accelerate the development of accurate treatment protocols in clinical practice, thus improving the prognosis of breast cancer.