Objective:To investigate and validate the performance of a dosiomics model that utilized 3D dose distribution to forecast radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) patients following intensity-modulated radiotherapy (IMRT).Methods:Clinical data of 3578 patients diagnosed with NPC admitted to Jiangsu Cancer Hospital from January 2011 to December 2021 were retrospectively analyzed. According to the inclusion and exclusion criteria, 97 NPC patients who developed RTLI were assigned into the case group. A 1:1 propensity score matching (PSM) method was used to match 97 NPC patients without RTLI as the control group. Patients were assigned into the training cohort ( n=135) and the validation cohort ( n=59) at a 7:3 ratio by simple random method. Dosiomics features were extracted from the patients' three-dimensional dose distribution maps. Spearman rho and the least absolute shrinkage and selection operator regression were used to select dosiomics features. Clinical features were collected and screened by univariate and multivariate analyses. Eight machine learning classifiers were then trained to build dosiomics models and clinical models, respectively. The area under the ROC curve (AUC), sensitivity, and specificity were calculated to compare the predictive performance of the dosiomics and clinical models. Multivariate analysis was conducted using logistic regression to assess the influencing factors, while comparisons of the ROC curves between two different models were performed using the DeLong test. Results:A total of 1130 dosiomics features were extracted from the three-dimensional dose distribution maps, and 14 features were retained for model building after feature selection. The model based on the support vector machine (SVM) classifier achieved the highest AUC value of 0.977 (95% CI: 0.949-1.000) in the validation cohort, with an AUC of 1.000 (95% CI: 1.000-1.000) in the training cohort. By conducting univariate and multivariate analyses of the patients' clinical features, 2 clinical features were retained to build the clinical model. The model based on the SVM classifier achieved the optimal AUC value of 0.667 (95% CI: 0.523-0.810) in the validation cohort, with an AUC of 0.804 (95% CI: 0.730-0.878) in the training cohort. DeLong test showed that the difference between the dosiomics and clinical models was statistically significant ( P<0.05). Conclusion:The dosiomics model based on 3D dose distribution yields high predictive performance for RTLI in NPC patients after IMRT, which surpasses the clinical feature model, providing a new approach for early clinical prediction of RTLI.

Objective To explore the application value of dual-energy computed tomography(DECT)pulmonary perfusion parameters in the evaluation of pulmonary hypertension(PH).Methods A retrospective selection was performed on 214 patients with suspec-ted PH who underwent DECT pulmonary perfusion examination,and the patients were divided into PH group(n=97)and non-PH group(n=117)according to the pulmonary artery systolic pressure measured by echocardiography.Inter-group comparison,univari-ate and multivariate logistic regression,and the receiver operating characteristic(ROC)curve were used to analyze the two-phase DECT pulmonary perfusion parameters and traditional CT anatomical parameters of the two groups of patients,so as to obtain the best parameters and models for predicting PH.Results There were statistically significant differences in DECT pulmonary perfusion parameters and traditional CT anatomical parameters between the PH group and the non-PH group(P<0.05).In DECT pulmonary perfusion parameters,the ratio of main pulmonary artery enhancement to whole lung enhancement in the pulmonary arterial phase(MPAenh1/WLenh1),the difference between the whole lung enhancement in the pulmonary venous phase and the whole lung enhancement in the pulmonary arterial phase(WLenh2-WLenh1),and in traditional CT anatomical parameters,the main pulmonary artery diam-eter(MPAD)and the ratio of right ventricular diameter to left ventricular diameter(RVD/LVD)were independent predictors of PH.The DECT pulmonary perfusion parameters model had similar diagnostic efficacy to the traditional CT anatomical parameters model for PH,and the combined model of the two parameters had the best diagnostic efficacy,with an area under the curve(AUC)of 0.955 and sensitivity and specificity of 0.866 and 0.940,respectively.Conclusion DECT pulmonary perfusion parameters can be used to evaluate PH,and the combination of traditional CT anatomical parameters and DECT pulmonary perfusion parameters can significantly improve the diagnostic efficacy of PH.

Objective To explore the application value of dual-energy computed tomography(DECT)pulmonary perfusion parameters in the evaluation of pulmonary hypertension(PH).Methods A retrospective selection was performed on 214 patients with suspec-ted PH who underwent DECT pulmonary perfusion examination,and the patients were divided into PH group(n=97)and non-PH group(n=117)according to the pulmonary artery systolic pressure measured by echocardiography.Inter-group comparison,univari-ate and multivariate logistic regression,and the receiver operating characteristic(ROC)curve were used to analyze the two-phase DECT pulmonary perfusion parameters and traditional CT anatomical parameters of the two groups of patients,so as to obtain the best parameters and models for predicting PH.Results There were statistically significant differences in DECT pulmonary perfusion parameters and traditional CT anatomical parameters between the PH group and the non-PH group(P<0.05).In DECT pulmonary perfusion parameters,the ratio of main pulmonary artery enhancement to whole lung enhancement in the pulmonary arterial phase(MPAenh1/WLenh1),the difference between the whole lung enhancement in the pulmonary venous phase and the whole lung enhancement in the pulmonary arterial phase(WLenh2-WLenh1),and in traditional CT anatomical parameters,the main pulmonary artery diam-eter(MPAD)and the ratio of right ventricular diameter to left ventricular diameter(RVD/LVD)were independent predictors of PH.The DECT pulmonary perfusion parameters model had similar diagnostic efficacy to the traditional CT anatomical parameters model for PH,and the combined model of the two parameters had the best diagnostic efficacy,with an area under the curve(AUC)of 0.955 and sensitivity and specificity of 0.866 and 0.940,respectively.Conclusion DECT pulmonary perfusion parameters can be used to evaluate PH,and the combination of traditional CT anatomical parameters and DECT pulmonary perfusion parameters can significantly improve the diagnostic efficacy of PH.

Objective:To investigate and validate the performance of a dosiomics model that utilized 3D dose distribution to forecast radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC) patients following intensity-modulated radiotherapy (IMRT).Methods:Clinical data of 3578 patients diagnosed with NPC admitted to Jiangsu Cancer Hospital from January 2011 to December 2021 were retrospectively analyzed. According to the inclusion and exclusion criteria, 97 NPC patients who developed RTLI were assigned into the case group. A 1:1 propensity score matching (PSM) method was used to match 97 NPC patients without RTLI as the control group. Patients were assigned into the training cohort ( n=135) and the validation cohort ( n=59) at a 7:3 ratio by simple random method. Dosiomics features were extracted from the patients' three-dimensional dose distribution maps. Spearman rho and the least absolute shrinkage and selection operator regression were used to select dosiomics features. Clinical features were collected and screened by univariate and multivariate analyses. Eight machine learning classifiers were then trained to build dosiomics models and clinical models, respectively. The area under the ROC curve (AUC), sensitivity, and specificity were calculated to compare the predictive performance of the dosiomics and clinical models. Multivariate analysis was conducted using logistic regression to assess the influencing factors, while comparisons of the ROC curves between two different models were performed using the DeLong test. Results:A total of 1130 dosiomics features were extracted from the three-dimensional dose distribution maps, and 14 features were retained for model building after feature selection. The model based on the support vector machine (SVM) classifier achieved the highest AUC value of 0.977 (95% CI: 0.949-1.000) in the validation cohort, with an AUC of 1.000 (95% CI: 1.000-1.000) in the training cohort. By conducting univariate and multivariate analyses of the patients' clinical features, 2 clinical features were retained to build the clinical model. The model based on the SVM classifier achieved the optimal AUC value of 0.667 (95% CI: 0.523-0.810) in the validation cohort, with an AUC of 0.804 (95% CI: 0.730-0.878) in the training cohort. DeLong test showed that the difference between the dosiomics and clinical models was statistically significant ( P<0.05). Conclusion:The dosiomics model based on 3D dose distribution yields high predictive performance for RTLI in NPC patients after IMRT, which surpasses the clinical feature model, providing a new approach for early clinical prediction of RTLI.

Objective:To explore the value of coronary artery plaque progression parameters based on coronary CT angiography (CCTA) in predicting the occurrence of major adverse cardiovascular events (MACE) in patients with non-obstructive coronary artery disease.Methods:The study included clinical, imaging, and prognosis (MACE) parameters of non-obstructive coronary artery disease patients who underwent CCTA at the First Affiliated Hospital of Nanjing Medical University from September 2010 to December 2022. Patients were grouped based on the occurrence of MACE, and differences in clinical data, plaque baseline, and progression parameters between the two groups were compared. Univariate and multivariate Cox regression analyses were employed to identify factors that could effectively predict the occurrence of MACE in patients. Models were constructed using plaque baseline parameters, plaque progression parameters, and a combination of both. The concordance index-time curve, net reclassification improvement and integrated discrimination improvement were used to evaluate the risk stratification ability of the models.Results:A total of 258 patients were included, of whom 62 cases experienced MACE during the follow-up period. In comparison to the MACE(-) group, patients in the MACE(+) group exhibited longer lesion length, greater degree of stenosis, larger plaque total volume, calcified plaque volume, non-calcified plaque volume, fibrous plaque volume, total plaque burden, lipid-rich plaque burden, higher peri-coronary adipose tissue attenuation index (FAI), and annual change of diameter stenosis(ΔDS/y). There were also more cases of coronary artery disease reporting and data system upgrades and non-obstructive progression to obstructive status ( P<0.05). Multivariate Cox analysis revealed that FAI, ΔDS/y, and non-obstructive progression to obstructive status were independent predictors of MACE occurrence. Concordance index-time curve results indicated that the combined model had a better predictive efficacy for MACE in patients with non-obstructive coronary artery disease compared to models based on plaque baseline parameters and plaque progression parameters. Conclusion:The plaque progression parameters and FAI based on CCTA have the potential to predict the high-risk population for MACE in patients with non-obstructive coronary artery disease, demonstrating good risk stratification value.

Objective:To explore the application and effect of scenario simulation teaching in ST-segment elevation myocardial infarction (STEMI) course for emergency training of "5+3" professional master.Methods:A randomized controlled trial was conducted among 48 "5+3" professional masters who would rotate in the emergency department, to compare the test results of the total and three scenes between scenario simulation teaching group ( n=24) and traditional teaching group ( n=24). The STEMI teaching was carried out through simulating the emergency room, standardized patients and first-aid simulators in the simulation teaching group, and the traditional teaching group was taught by conventional classroom teaching. After the class, "Theoretical Examination" and "Questionnaire Survey" were used to evaluate the teaching effect. The former was further divided into "first diagnosis and differential diagnosis of chest pain", "fast identification and processing of STEMI" and "rescue of cardiac arrest" for inter-group and intra-group evaluation. SPSS 20.0 was used to conduct t-test. Results:In the theoretical examination, the scenario simulation teaching group was superior to the traditional teaching group in the test of emergency processing for STEMI course [(82.38±2.41) vs . (68.00±1.95), t=4.64, P<0.001]. In the sub-analysis of scenario simulation teaching group, students in the role-play group had significantly higher scores than others in the non-role-play group [(90.50±3.04) vs . (79.67±2.79), t=2.09, P=0.049]. Scenario simulation teaching group was also superior to traditional teaching group in the "Questionnaire Survey". Conclusion:In the training and teaching of STEMI emergency processing, scenario simulation teaching group proves to be superior to traditional teaching, which deserves further promotion.

With the rapid development of medical imaging technology, there are many corresponding kinds of medical images. The traditional teaching and examination mode based on typical images cannot meet the needs of medical imaging teaching. Therefore, we took the post competence of medical imaging students as the guidance, integrated multidisciplinary medical image resources, and established the human-computer dialogue teaching and examination system for medical images. The system was applied to medical imaging teaching and examination, achieving the standardization and clinical simulation of teaching and examination, and effectively improving the post competence of students.

Objective To investigate the influence of iodixanol-320 and iopromide-370 on the heart rate of patients in coronary dual-source CT angiography (CTA).Methods The data of 389 patients underwent coronary CTA examinations were retrospectively collected and received contrast media (CM) with either iodixanol-320 (group A) or iopromide-370 (group B), randomly.The heart rate before CM injection (predose HR), during injection (postdose HR) were both recorded.As for the preclinical protocol, patients with heart rate less than 75 beats per minute were pretreated with nitrates (n=278),0.25 mg.Mean heart rate changes from pre to postdose HR were assessed.Results The patients whose mean heart rate changes from pre to postdose were larger than 10 beats per minute was 8(4.4%) for group A and 10(4.8%) for group B.No statistically differences were observed between them(P>0.05).With only intravenous injections of two contrast agents, the patients' heart rates decreased in both groups (4.2 vs 2.7 beats per minute,P>0.05), while the effect could be reduced by nitrates.Conclusion There is no difference in the heart rate between the 2 agents after intravenous injection of either iodixanol-320 or iopromide-370 in coronary CTA with approximately 4% patients whose mean heart rate changes from preto postdose were larger than 10 beats per minute in each group.

Objective To investigate the role of the percentage of the total cross-sectional area of small pulmonary vessels for the lung area (%CSA) from multi-slice CT (MSCT) in evaluating the severity of chronic obstructive pulmonary disease (COPD). Methods One hundred and sixty-six COPD patients and 166 normal subjects underwent chest MSCT scans and all data were analyzed retrospectively. COPD patients underwent pulmonary function tests (PFT), including forced expiratory volume in one second (FEV1%) and FEV1/forced vital capacity (FEV1/FVC), and were classified into mild (n=32), moderate (n=65), severe (n=69) groups according to pulmonary function results, respectively. The%CSA less than 5 mm2 and 5—10 mm2 for the lung area (%CSA<5 and %CSA5-10) of small pulmonary vessels were measured with Image J image-processing program. Comparison of%CSA<5 and%CSA5-10 between the COPD and control groups was perfomred using t test, and the comparison between the 3 COPD subgroups and control group were carried out using ANOVA test. The correlation between %CSA and PFT was evaluated by the Spearman rank correlation test. The sensitivity and specificity of %CSA to diagnose COPD and the best cutoff were calculated from areas under the ROC curves. Results %CSA<5 of COPD patients and control group were (0.56 ± 0.19)%and (0.82 ± 0.15)%(t=12.80, P<0.001), respectively.%CSA5-10 of the two groups were (0.19 ± 0.09)%and (0.33 ± 0.16)%(t=8.93,P<0.001), respectively. The AUC values of%CSA<5 and%CSA5-10 were 0.866 and 0.790, respectively. When the cut-off values of%CSA<5 and%CSA5-10 were 0.65%and 0.24%, the sensitivities and specificities were 88%and 71%, 76%and 81%, respectively. The mean values of%CSA<5 in mild, moderate and severe groups were (0.67±0.20)%, (0.61±0.16)%and (0.44±0.14)%, respectively (P<0.05). The mean values of %CSA5-10 in the three groups were (0.19 ± 0.06)%, (0.19 ± 0.10)% and (0.20 ± 0.08)%, respectively.%CSA5-10 in the three groups were of no significant difference (P>0.05). FEV1%and FEV1/FVC in COPD patients were (60.38±15.52)%and 57.95±22.27.%CSA<5 in COPD patients correlated positively with both FEV1%and FEV1/FVC (r=0.609 and 0.721, P<0.01, respectively).%CSA5-10 in COPD patients correlated positively with both FEV1%and FEV1/FVC (r=0.271 and 0.288, P<0.01, respectively). Conclusion The measurement of%CSA<5 and%CSA5-10 in MSCT images correlated with PFTs and%CSA<5, which may play an important role in evaluating the severity of COPD.

Using the Medical Imaging Experimental Teaching Demonstration Center of Nanjing Medical University and related web site as a platform, we have established a case library and online learning system which fused medical imaging, pathology and anatomy images according to the human organ system. The system has achieved a“medical imaging”teaching real-time, diversified, systematic and clinical simulation technology. Medical students can perform image processing, simulate writing reports and discuss online using this system. Teachers can use the system to carry out teaching reform-problem-based learning (PBL), and the integration of teaching with horgan systems and case based curriculum.