Objective:To construct an intelligent APP-assisted process assessment mode for standardized residency training and explore its application effect.Methods:Based on the "XUEYIKU" APP platform developed in Peking University Third Hospital, we built a standardized process of process assessment for standardized residency training assisted by intelligent APP and combining both online and offline components. We analyzed the frequency of APP use, satisfaction with the APP, the problems solved by the APP, and the aspects that need to be improved. SPSS 26.0 was used for t-test and Chi-square test. Results:The satisfaction score of teachers with the APP was (7.66±1.86) points. The satisfaction score of professional base teaching directors/process assessment coordinators was higher than that of clinical teachers [(8.28±1.30) vs. (7.42±1.99), P=0.013]. Most residents were satisfied with the APP (67.44%, 29/43), regarded its role in supporting the entire assessment process as important (55.81%, 24/43), and reviewed teacher feedback (65.12%, 28/43). Some teachers indicated that the APP solved problems such as paperless exam scoring (59.00%, 59/100), standardized exam processes (43.00%, 43/100), score statistics (42.00%, 42/100), and score reporting (42.00%, 42/100). The residents believed that the APP resolved issues such as exam notification (44.19%, 19/43), scheduling (41.86%, 18/43), and result feedback (41.86%, 18/43). Both teachers and residents mentioned the need for further strengthening the stability of the APP system and simplifying operational steps. Conclusions:With APP as a link, through the instant transmission and feedback of data, the intelligent APP-assisted process assessment mode drives the reflection and summarization of professional bases, clinical teachers, and residents, and promotes the post competence of residents.

Objective To explore the preoperative diagnostic value of a radiomics nomogram based on intratumoral and peritumoral apparent diffusion coefficient(ADC)maps for prostate cancer.Methods A retrospective collection was conducted on MRI images of 503 patients with prostate lesions confirmed by pathology.The region of interest(ROI)was delineated on the ADC maps and extended 1-5 mm outward to form the peritumoral region.Radiomics features were extracted from both intratumoral and peritumoral regions,and radiomics models were established.A combined model integrating clinical model was constructed and a nomogram was drawn.The performance of each model and nomogram were evaluated.Results The combined model achieved the highest area under the curve(AUC)in the test set(AUC=0.823)at a peritumoral distance of 3 mm.The nomogram based on the combined model showed good predictive performance and clinical utility on both decision curve analysis(DCA)and calibration curve.Conclusion The radiomics nomogram based on intratumoral and peritumoral ADC maps has the greatest diagnostic value in distinguishing benign and malignant prostate cancer at a peritumoral distance of 3 mm before surgery.

Objective:To construct an intelligent APP-assisted process assessment mode for standardized residency training and explore its application effect.Methods:Based on the "XUEYIKU" APP platform developed in Peking University Third Hospital, we built a standardized process of process assessment for standardized residency training assisted by intelligent APP and combining both online and offline components. We analyzed the frequency of APP use, satisfaction with the APP, the problems solved by the APP, and the aspects that need to be improved. SPSS 26.0 was used for t-test and Chi-square test. Results:The satisfaction score of teachers with the APP was (7.66±1.86) points. The satisfaction score of professional base teaching directors/process assessment coordinators was higher than that of clinical teachers [(8.28±1.30) vs. (7.42±1.99), P=0.013]. Most residents were satisfied with the APP (67.44%, 29/43), regarded its role in supporting the entire assessment process as important (55.81%, 24/43), and reviewed teacher feedback (65.12%, 28/43). Some teachers indicated that the APP solved problems such as paperless exam scoring (59.00%, 59/100), standardized exam processes (43.00%, 43/100), score statistics (42.00%, 42/100), and score reporting (42.00%, 42/100). The residents believed that the APP resolved issues such as exam notification (44.19%, 19/43), scheduling (41.86%, 18/43), and result feedback (41.86%, 18/43). Both teachers and residents mentioned the need for further strengthening the stability of the APP system and simplifying operational steps. Conclusions:With APP as a link, through the instant transmission and feedback of data, the intelligent APP-assisted process assessment mode drives the reflection and summarization of professional bases, clinical teachers, and residents, and promotes the post competence of residents.

Objective To explore the preoperative diagnostic value of a radiomics nomogram based on intratumoral and peritumoral apparent diffusion coefficient(ADC)maps for prostate cancer.Methods A retrospective collection was conducted on MRI images of 503 patients with prostate lesions confirmed by pathology.The region of interest(ROI)was delineated on the ADC maps and extended 1-5 mm outward to form the peritumoral region.Radiomics features were extracted from both intratumoral and peritumoral regions,and radiomics models were established.A combined model integrating clinical model was constructed and a nomogram was drawn.The performance of each model and nomogram were evaluated.Results The combined model achieved the highest area under the curve(AUC)in the test set(AUC=0.823)at a peritumoral distance of 3 mm.The nomogram based on the combined model showed good predictive performance and clinical utility on both decision curve analysis(DCA)and calibration curve.Conclusion The radiomics nomogram based on intratumoral and peritumoral ADC maps has the greatest diagnostic value in distinguishing benign and malignant prostate cancer at a peritumoral distance of 3 mm before surgery.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.

Purpose To explore the value of deep learning feature prediction based on the ResNet50 deep residual network model for predicting early hematoma expansion in spontaneous intracerebral hemorrhage without traditional imaging manifestations. Materials and Methods A retrospective study was performed on 235 patients with spontaneous intracerebral hemorrhage in Jiangdu People's Hospital Affiliated to Yangzhou University from January 2019 and December 2022. These patients had undergone their initial plain cranial CT scan within 6 hours of symptom onset and a subsequent follow-up scan within 24 hours of admission. They were randomly assigned to a training set consisting of 188 cases and a test set of 47 cases,using an 8︰2 ratio. The region of interest (ROI) of hematoma was traced layer by layer on the first plain head CT,and image genomics features were extracted. The maximum two-dimensional cross-sectional ROI of the hematoma 3D-ROI,as well as ROI images at 1 mm and 2 mm above and below the maximum two-dimensional cross-sectional ROI,were then cut and input into the pre-trained ResNet50 model for feature extraction. The image genomics features were then fused with the extracted deep learning features using a least absolute shrinkage and selection operator regression model. A support vector machine classifier was used to construct a prediction model,which was evaluated using receiver operating characteristic curves and decision curve analysis. Results In the training set,the area under curve (AUC) of the deep learning feature model was 0.972,which was higher than that of the image genomics feature model (0.951) and the fused feature model (0.968),but this difference was not statistically significant (P>0.05). In the testing set,the AUCs of the deep learning feature model and the fused feature model were 0.867 and 0.895,respectively,which were significantly higher than that of the image genomics feature model (0.833),with statistically significant differences (Z=-1.794,-2.191,both P<0.05). The AUC of the fused feature model showed an improvement compared to the deep learning feature model,but the difference was not statistically significant (P>0.05). In the test set,decision curve analysis revealed that the fused feature model yielded greater benefits compared to both the deep learning feature model and the radiomic feature model. Conclusion The deep learning feature model based on ResNet50 deep residual network shows better performance in predicting early hematoma expansion than the image genomics feature model,and the fused feature model has a beneficial effect on predicting hematoma expansion. This deep learning approach provides a prediction tool with supervisory capability for clinical decision-making.

Objective To observe the value of subregional non-contrast CT(NCCT)radiomics features based on habitat imaging technology for predicting hematoma expansion(HE)in patients with spontaneous intracranial hemorrhage(sICH).Methods Data of 228 sICH patients with negative conventional imaging signs were retrospectively analyzed and divided into HE group(n=99)or non HE(NHE)group(n=129)based on the occurrence of HE nor not.also divided into training set(n=182)or test set(n=46)at a ratio of 8:2.Clinical data,NCCT data and laboratory examination results were compared between groups.Logistic regressive analysis was performed to screen the impact factors of HE.ROI of whole hematoma(ROIwhole)was sketched and clustered into 3 sub-regions(ROIsub1,ROIsub2 and ROIsub3,the latter located in the critical area between hematoma and brain tissue)with habitat imaging technology,and radiomics features of ROI were extracted and screened.Then 4 prediction models were constructed based on the above 4 ROI,and the efficacy of each model for predicting HE was analyzed.Results The fasting blood glucose in HE group was higher than that in NHE group(t=2.047,P=0.041),which was not independent impact factor for predicting HE in sICH patients(P=0.070)according to logistic regression analysis.The area under the curve of ROIsub3 radiomics model for predicting sICH HE in training and test set was 0.945 and 0.863,respectively,not significantly different with that of ROIwhole(0.921,0.813),ROIsub1(0.925,0.807)nor ROIsub2(0.909,0.720)(all P＞0.05).Decision curve analysis showed that ROIsub3 radiomics model could bring greater benefits than the other 3 models.Conclusion NCCT radiomics features of the critical area between hematoma and brain tissue based on habitat imaging technology had high value for predicting HE in sICH patients.

[Abstract] Objective: To explore the mRNA molecular targets for diagnosis of hepatic carcionoma and to investigate their functional roles in proliferation and cell cycle of hepatic cancer cells. Methods: Based on the statistical analysis of miRNA expression data from 377 hepatic carcionoma samples and 37 adjacent non-cancerous samples in TCGAdatabase, a group of 33 differentially expressed miRNAs were identified.A further screen of these differentially expressed miRNAs was performed using the receiver operating characteristic curve (ROC curve) and Kaplan-Meier survival analysis; and with referring to the current publications, miR-451 was screened as the study subject. HepG2 cells were transfected with pLVX-shRNA2-miR-451 to over-express miR-451. The effect of miR-451 over-expression on the proliferation of HepG2 cell was determined by CCK-8 assay; while the effect on cell cycles was detected by flow cytometry. Results: The expression of miR-451 in the adjacent non-cancerous tissues was significantly lower than that in cancer tissues ([473.40±390.24] vs [1 990.47±2 118.04], P<0.05). MiR-451 could be used as an early diagnostic biomarker of hepatic carcionoma, with a high ROC value of 0.91 (sensitivity 0.89, specificity 0.87). The results of in vitro experiments showed that the proliferation of HepG2 cells was significantly decreased after miR-451 over-expression (48 h: [0.69±0.04] vs [1.08±0.05]; 72 h: [0.76±0.07] vs [1.52± 0.02]; all P<0.01), and a large number of cells were blocked in S phase(P<0.05). Conclusion: miR-451 has the potential to be used as a biomarker for hepatic carcionoma diagnosis and prognosis; moreover, it also exhibits the inhibitory effect on proliferation of hepatic cancer cells.