Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

Objective To study the intervention effect of nursing staff's participation in antimicrobial stewardship(AMS)on the rational use of antimicrobial agents,and explore its role in constructing a scientific healthcare-associa-ted infection(HAI)control management.Methods The data on perioperative prophylactic use of antimicrobial agents,surgical-related HAI control,and pathogen detection before therapeutic use of antimicrobial agents among hospitalized patients in a hospital from January 2016 to December 2024 were collected.Relevant evaluation indica-tors before and after nursing staff participating in AMS were compared.2016-2018,2019-2021,and 2022-2024 were stages before intervention,during intervention,and after intervention,respectively.Results After nursing staff participated in AMS,the use rate of prophylactic antimicrobial agents 0.5-1 hour before surgery and discon-tinuation rate of antimicrobial agents within 24 hours after class Ⅰ incision surgery increased from 64.54％and 81.41％before intervention to 75.31％and 84.56％after intervention,respectively.Incidences of surgical-related HAI and surgical site infection in patients decreased from 3.11％and 0.96％before intervention to 1.37％and 0.17％after intervention,respectively.Pathogen detection rates before restricted-and special-grade antimicrobial agents treatment increased from 50.80％and 68.70％before intervention to 55.19％and 80.53％after interven-tion,respectively.Proportion of blood specimen from which coagulase-negative Staphylococcus was detected de-creased from 29.30％before intervention to 21.26％after intervention.Proportion of respiratory specimen from which Haemophilus influenzae was detected increased from 2.00％to 3.98％.Differences were all statistically sig-nificant(all P＜0.05).Conclusion As important members of the AMS team,nursing staff can effectively reduce irrational antimicrobial use,optimize medication timing and duration,and have a positive effect on ensuring patient safety through participating in the use and management of antimicrobial agents in hospitalized patients.

Objective To study the intervention effect of nursing staff's participation in antimicrobial stewardship(AMS)on the rational use of antimicrobial agents,and explore its role in constructing a scientific healthcare-associa-ted infection(HAI)control management.Methods The data on perioperative prophylactic use of antimicrobial agents,surgical-related HAI control,and pathogen detection before therapeutic use of antimicrobial agents among hospitalized patients in a hospital from January 2016 to December 2024 were collected.Relevant evaluation indica-tors before and after nursing staff participating in AMS were compared.2016-2018,2019-2021,and 2022-2024 were stages before intervention,during intervention,and after intervention,respectively.Results After nursing staff participated in AMS,the use rate of prophylactic antimicrobial agents 0.5-1 hour before surgery and discon-tinuation rate of antimicrobial agents within 24 hours after class Ⅰ incision surgery increased from 64.54％and 81.41％before intervention to 75.31％and 84.56％after intervention,respectively.Incidences of surgical-related HAI and surgical site infection in patients decreased from 3.11％and 0.96％before intervention to 1.37％and 0.17％after intervention,respectively.Pathogen detection rates before restricted-and special-grade antimicrobial agents treatment increased from 50.80％and 68.70％before intervention to 55.19％and 80.53％after interven-tion,respectively.Proportion of blood specimen from which coagulase-negative Staphylococcus was detected de-creased from 29.30％before intervention to 21.26％after intervention.Proportion of respiratory specimen from which Haemophilus influenzae was detected increased from 2.00％to 3.98％.Differences were all statistically sig-nificant(all P＜0.05).Conclusion As important members of the AMS team,nursing staff can effectively reduce irrational antimicrobial use,optimize medication timing and duration,and have a positive effect on ensuring patient safety through participating in the use and management of antimicrobial agents in hospitalized patients.

Objective:To develop habitat radiomics models to predict early treatment responses to the hepatic arterial infusion chemotherapy (HAIC) combined with targeted therapy or immunotherapy in advanced hepatocellular carcinoma (HCC) patients, and to guide clinical diagnosis and treatment.Methods:From October 2021 to Decemeber 2023, at Army Characteristic Medical Center of PLA (Chongqing Daping Hospital) and the First Affiliated Hospital of Chongqing Medical University, 94 patients with advanced HCC who received HAIC combined with targeted therapy or immunotherapy were retrospectively enrolled. According to the treatment results, the patients were divided into response group and non-response group. Univariate and multivariate logistic regression were performed to analyze the clinical data of the patients. Based on contrast-enhanced CT images, tumor habitats were delineated and habitat features were extracted with k-means clustering, and the imaging features of arterial and venous phases were also extracted. The least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction. Feature selection was performed using LASSO to reduce dimensions, and then the selected features were further refined through stepwise logistic regression analysis.Binary logistic regression models were conducted to develop the habitat radiomics model, arterial phase radiomics model (APRM), venous phase radiomics model (VPRM), clinical data model, as well as the combination of radiomics model and clinical data model to predict early treatment (after 2 treatment cycles) response. Receiver operating characteristic curves (ROC) were plotted, and model performance was evaluated by the area under the curve (AUC), calibration curves, and decision curve. The models were validated through Bootstrap methods (1 000 times). DeLong test was used to compare AUC values.Results:The results of cluster analysis identified 3 characteristic habitats in HCC imaging: low-, medium-, and high-enhancement tumor habitats. The proportion of high-enhancement habitats was higher than that in the non-response group. A predictive model was established based on the proportions of these 3 habitats. Based on the proportion of low-, medium-, and high-enhancement habitats within the tumor, a habitat radiomics model was constructed. After LASSO selection and logistic regression analysis, 3 arterial phase and 3 venous phase radiomic features were selected to build the APRM and VPRM, respectively. Logistic regression analysis identified the following factors for the clinical data model: comorbidities ( OR=0.275, P=0.031), maximum tumor diameter ( OR=1.149, P=0.019), red blood cell count ( OR=0.463, P=0.022), alpha fetoprotein >400 μg/L ( OR=3.452, P=0.017), and tyrosine kinase inhibitor therapy ( OR=3.072, P=0.048). Among the single predictive model′s comparison, the AUC of habitat radiomics model was 0.860 (95% confidence interval(95% CI): 0.789 to 0.932), while those of the APRM、VPRM and clinical data model were 0.850 (95% CI: 0.773 to 0.926), 0.855 (95% CI: 0.782 to 0.928), and 0.774 (95% CI: 0.681 to 0.867), respectively, and there were no statistically significant among these models (all P>0.05). Among the combination models, the AUC of the habitat rediomic-clinical data combination model was 0.881 (95% CI: 0.814 to 0.947); the AUC of arterial phase rediomic-clinical data combination model was 0.897 (95% CI: 0.833 to 0.961); and the AUC of venous phase rediomic-clinical data combination model was 0.888 (95% CI: 0.826 to 0.951), but there were no statistically significant among the 3 models (all P>0.05). The calibration curve showed that the habitat rediomic-clinical data combination model had the most accurate predictive probability. Internal validation showed that the AUC of habitat rediomic-clinical data combination model was 0.848 (95% CI: 0.772 to 0.922), and the predictive performance was better than that of the clinical-data model (0.733 (95% CI: 0.670 to 0.863)). Conclusion:The habitat radiomics model based on enhanced CT can effectively predict early treatment responses to the HAIC combined with targeted therapy or immunotherapy in advanced HCC patients, which provides theoretical basis for individualized treatment in advanced HCC.

According to the subject characteristics of radiation oncology, three teaching practices were carried out in the teaching of standardized residency training: teaching situation transformation under the guidance of constructivism theory, expanding teaching with points to areas, and organ system-centered medical classroom under the guidance of case analysis. At the same time, it combines the guidance of the residents' active exploration, integrated thinking and cooperative learning. Through the modulation of teaching and learning practices guided by constructivism, the residents' learning and understanding of radiation oncology knowledge and the integration and construction ability of "organ system-centered" medical knowledge are promoted, their active learning potential and innovative thinking ability are stimulated, and finally the teaching quality of this specialty is improved.

Radiotherapy is the most common treatment for nasopharyngeal carcinoma, and the radiotherapy technique is essential for the prognosis of nasopharyngeal carcinoma. Due to the complexity of the structure of the intensity-modulated device and the accuracy of the clinical requirements of radiotherapy, it is inevitable that higher requirements will be imposed on the process of intensity-modulated radiotherapy. Currently, gaps exist in the radiotherapy equipment and personnel qualification among radiotherapy units, and thus the homogenization in the radiotherapy remains to be strengthened in China. With the application of radiotherapy information management system, digital medicine and artificial intelligence technologies in the field of radiotherapy, the original process fails to meet the application needs of the new precise radiotherapy technology. Therefore, this process is designed based on the existing radiotherapy procedures for nasopharyngeal carcinoma in combination with the latest developments in the field of radiotherapy, aiming to establish a novel standard process recommendation, ensuring the standardization and homogenization of radiotherapy and achieve the individualized intensity-modulated radiotherapy for nasopharyngeal carcinoma patients.

Objective To explore the feasibility of reduction by using cone beam computed tomography (CBCT) before intensity modulated radiation therapy(IMRT) in the patients with nasopharyngeal carcinoma.Methods Twenty-three patients with nasopharyngeal carcinoma (NPC) undergoing IMRT were included in this study.The reverse IMRT plan with CBCT verification was prepared with location center coordinates origin as the planned central point.Before therapy,the CBCT reduction was adopted,the CBCT scanning was performed before the second and third radiotherapies.The registering data in 3 times were analyzed and summarized.Results In CBCT reduction,the absolute value at any direction≤3 mm accounted for 89.9％ (62/69),＜5 mm accounted for 98.6 ％ (68/69),and the deviation value at every direction was (0.6 ± 2.1)mm;in the second and third CBCT,the absolute value at any direction ≤3 mm accounted for 92.8％ (128 q38),＜5 mm accounted for 99.3％ (137/138),and the deviation value at every direction was (0.4 ± 2.0) mm:the difference between the two sets of data had no statistically significant difference (P＞ 0.05).Conclusion In formulating the nasopharyngeal carcinoma IMRT plan withthe location center coordinates origin as the planned central point,adopting the CBCT reduction is intuitional,convenient,practicable and feasible.