Objective To study the image examination of renal injuries and discuss renal explorative indications so as to spare the kidney or nephron as much as possible and improve curative rate of diagnosis and treatment. Methods An analysis was done on 286 cases that included 231 cases with close injury, 54 with open injuries, one with iatrogenic injury and 91 with combined injuries. Of all, 212 cases were examined by B-ultrasonography, 163 by CT and 132 by intravenous urography(IVU) and 6 by digital subtraction angiography(DSA); 202 cases were treated with conservative treatment and 84 with operation. Results The diagnostic positive rates of IVU, B-ultrasonography and CT were 67.4%, 72.2% and 87.7%, respectively. Among the operation cases, 42 cases were treated by renal repair, 12 by partial nephrectomy and 30 by nephrectomy. The operation rate was 29.4% and the nephrectomy rate 35.5%. Interventional treatment of the kidney was carried out in three cases. Conclusions For renal injury cases, the first and most important step is to evaluate the injury condition so as to correctly determine whether an operation exploration is needed. The injury conditions and severity are mainly determined by the image examinations that change according to injury cause, injury type and clinical symptoms. Renal exploration or not, and the operation time exert great influence on renal reservation rate and complication rate.

Objective To explore the value of deep learning models in predicting axillary lymph node(ALN)metastasis of breast cancer based on intratumoral and peritumoral dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI).Methods A retrospective analysis was conducted on cases from Tianjin Medical University Cancer Hospital and Laoshan Branch of Affiliated Hospital of Qingdao University,involving a total of 850 lesions in 850 patients.The region of interest within the tumor was delineated at the largest area of the lesion on the first enhancement images and automatically expanded by 3 mm and 6 mm in a conformal fashion.Deep learning prediction models based on ResNet50 were developed via intratumoral,peritumoral,and intratumoral combined peritumoral models,respectively,and a comprehensive prediction model was developed by integrating semantic features of imaging reports.Cases from Tianjin Medical University Cancer Hospital were randomly divided into training and test cohorts in a 7∶3 ratio,while cases from Laoshan Branch of Affiliated Hospital of Qingdao University served as the external validation cohort.The area under the curve(AUC),accuracy,sensitivity,specificity,F1-score,and Brier-score were calculated,respectively.Results The model incorporating intratumoral,peritumoral(3 mm),and semantic features demonstrated the highest performance,with AUC of 0.801[95％confidence interval(CI)0.765-0.845],0.781(95％CI 0.745-0.817),and 0.752(95％CI 0.700-0.793)in the training cohort,test cohort,and external validation cohort,respectively,and there was no significant difference in AUC between combined model and intratumoral/peritumoral model,respectively,but it demonstrated the higher sensitivity and F1-score,and the lower Brier-score.Conclusion Incorporating peritumoral images into the conventional model based on intratumoral images enhanced the predictive ability of ALN metastasis in breast cancer.