Objective To explore the value of apparent diffusion coefficient(ADC)value combined with ovarian-adnexal reporting and data system(O-RADS)MRI score in differentiating benign and malignant adnexal lesions with score 3-5.Methods The imaging data of 241 adnexal lesions with O-RADS MRI score 3-5 proved by pathology were analyzed retrospectively.The ADC values of all lesions were measured,and the optimal thresholds were determined by receiver operating characteristic(ROC)curve analysis,the comprehensive model was established using binary logistic regression analysis,the corresponding diagnostic efficacy was calculated.Results The median ADC values of the benign,borderline and malignant groups were 2.166 × 10-3 mm2/s,1.383 ×10-3 mm2/s and 0.839× 10-3mm2/s,respectively(P＜0.05).The area under the curve(AUC)of the combination of ADC value with O-RADS MRI score for differentiating benign and malignant adnexal lesions was 0.928[95％ confidence interval(CI)0.888-0.958],which was higher than that of O-RADS MRI score and ADC value alone(P＜0.05).The sensitivity and specificity of the three models in differ-entiating benign and malignant adnexal lesions were 93.5％,98.9％,83.9％,respectively;and 79.1％,58.8％,79.1％,respectively.Conclusion ADC value combined with O-RADS MRI score can be the most effective in the diagnosis of benign and malignant adnexal lesions,which is higher than O-RADS MRI score and ADC value.Compared with O-RADS MRI score,ADC value combined with O-RADS MRI score maintained good sensitivity and increased diagnostic specificity.

Objective To observe the value of training about ovarian-adnexal reporting and data system((O-RADS)for risk stratification of adnexal lesions diagnostic efficacy of different seniority physicians before and after training.Methods A total of 575 O-RADS 1-5 point lesions from 470 patients who received non-contrast enhanced pelvic MR and dynamic contrast-enhanced MRI(DCE-MRI)were retrospectively included.The lesions were scored by 1 junior radiologist(radiologist A)and 1 senior radiologist(radiologist B)independently according to O-RADS risk stratification,and the results were recorded as R1 and R2,respectively.Three months later,the lesions were rescored by radiologist A and B after receiving systematic training from gynecological imaging experts,and the results were recorded as R11 and R22,respectively.Two gynecological imaging experts conducted a consensus scoring,and the results were recorded as R0.Taken O-RADS score＞3 as the criterion for malignant,the diagnostic efficacy of radiologist A and B before and after training were evaluated,while taken R0 as the reference,the intra-observer,inter-observer consistency between radiologist A and B,as well as their consistency with R0 were calculated.Results The diagnostic sensitivity and specificity of R0 was 95.21%and 93.14%,respectively.The diagnostic sensitivity of radiologist A before and after training was 92.22%and 95.21%,with specificity of 83.33%and 89.46%,respectively.For radiologist B,the sensitivity before and after training was 95.81%and 95.21%,with specificity of 92.89%and 91.91%,respectively.Good intra-observer consistency of O-RADS score was observed both in radiologist A and B,with Kappa value of 0.845 and 0.884,respectively,which also noticed between radiologist A and B,with Kappa value of 0.761,and the Kappa value of R1,R2 and R0 was 0.781 and 0.911,respectively.After training,the inter-observer consistency of radiologist A and B increased,and Kappa value of R11,R22 and R0 was 0.844 and 0.915,respectively.Conclusion Training about O-RADS for risk stratification was helpful to improving diagnostic specificity of benign and malignant adnexal lesions,especially for junior radiologists.

Objective To evaluate the value of deep learning(DL)-based artificial intelligence(AI)automatic reconstruction for evaluation of grafts in patients who underwent coronary artery bypass grafting(CABG).Methods Coronary CT angiography data of 90 patients who underwent CABG with a total of 197 grafts were retrospectively analyzed.Taken manual evaluation results(manual group)as the standards,the efficacy of AI(AI group)for evaluating the degree of stenosis of graft and distal autologous blood vessels were assessed.The consistency between calculating unprotected coronary territory(UCT)and the total time for image post-processing and diagnosis were compared between groups.Results AI group showed average consistency with manual group for evaluating the number of grafts([intra-class correlation coefficient,ICC]=0.743,P＜0.05),average to excellent for evaluating the maximum degree of graft stenosis(Kappa=0.310-1.000,all P＜0.05),also average to good consistency for evaluating the maximum degree of stenosis of the native vessel distal to the graft insertion(Kappa=0.292-0.795,all P＜0.05).AI group had moderate consistency with manual group for UCT(ICC=0.469,P＜0.05),achieved an area under the curve of 0.811.The overall time of image post-processing and diagnosis in AI group were both significantly shorter than that in manual group(P＜0.05).Conclusion Having acceptable consistency with manual evaluation and ability for assistant,AI was efficient for automatic reconstructing coronary artery bypass graft and quantifying the degree of graft stenosis.

Objective To explore the value of apparent diffusion coefficient(ADC)value combined with ovarian-adnexal reporting and data system(O-RADS)MRI score in differentiating benign and malignant adnexal lesions with score 3-5.Methods The imaging data of 241 adnexal lesions with O-RADS MRI score 3-5 proved by pathology were analyzed retrospectively.The ADC values of all lesions were measured,and the optimal thresholds were determined by receiver operating characteristic(ROC)curve analysis,the comprehensive model was established using binary logistic regression analysis,the corresponding diagnostic efficacy was calculated.Results The median ADC values of the benign,borderline and malignant groups were 2.166 × 10-3 mm2/s,1.383 ×10-3 mm2/s and 0.839× 10-3mm2/s,respectively(P＜0.05).The area under the curve(AUC)of the combination of ADC value with O-RADS MRI score for differentiating benign and malignant adnexal lesions was 0.928[95％ confidence interval(CI)0.888-0.958],which was higher than that of O-RADS MRI score and ADC value alone(P＜0.05).The sensitivity and specificity of the three models in differ-entiating benign and malignant adnexal lesions were 93.5％,98.9％,83.9％,respectively;and 79.1％,58.8％,79.1％,respectively.Conclusion ADC value combined with O-RADS MRI score can be the most effective in the diagnosis of benign and malignant adnexal lesions,which is higher than O-RADS MRI score and ADC value.Compared with O-RADS MRI score,ADC value combined with O-RADS MRI score maintained good sensitivity and increased diagnostic specificity.

Objective To observe the value of training about ovarian-adnexal reporting and data system((O-RADS)for risk stratification of adnexal lesions diagnostic efficacy of different seniority physicians before and after training.Methods A total of 575 O-RADS 1-5 point lesions from 470 patients who received non-contrast enhanced pelvic MR and dynamic contrast-enhanced MRI(DCE-MRI)were retrospectively included.The lesions were scored by 1 junior radiologist(radiologist A)and 1 senior radiologist(radiologist B)independently according to O-RADS risk stratification,and the results were recorded as R1 and R2,respectively.Three months later,the lesions were rescored by radiologist A and B after receiving systematic training from gynecological imaging experts,and the results were recorded as R11 and R22,respectively.Two gynecological imaging experts conducted a consensus scoring,and the results were recorded as R0.Taken O-RADS score＞3 as the criterion for malignant,the diagnostic efficacy of radiologist A and B before and after training were evaluated,while taken R0 as the reference,the intra-observer,inter-observer consistency between radiologist A and B,as well as their consistency with R0 were calculated.Results The diagnostic sensitivity and specificity of R0 was 95.21%and 93.14%,respectively.The diagnostic sensitivity of radiologist A before and after training was 92.22%and 95.21%,with specificity of 83.33%and 89.46%,respectively.For radiologist B,the sensitivity before and after training was 95.81%and 95.21%,with specificity of 92.89%and 91.91%,respectively.Good intra-observer consistency of O-RADS score was observed both in radiologist A and B,with Kappa value of 0.845 and 0.884,respectively,which also noticed between radiologist A and B,with Kappa value of 0.761,and the Kappa value of R1,R2 and R0 was 0.781 and 0.911,respectively.After training,the inter-observer consistency of radiologist A and B increased,and Kappa value of R11,R22 and R0 was 0.844 and 0.915,respectively.Conclusion Training about O-RADS for risk stratification was helpful to improving diagnostic specificity of benign and malignant adnexal lesions,especially for junior radiologists.

Objective To evaluate the value of deep learning(DL)-based artificial intelligence(AI)automatic reconstruction for evaluation of grafts in patients who underwent coronary artery bypass grafting(CABG).Methods Coronary CT angiography data of 90 patients who underwent CABG with a total of 197 grafts were retrospectively analyzed.Taken manual evaluation results(manual group)as the standards,the efficacy of AI(AI group)for evaluating the degree of stenosis of graft and distal autologous blood vessels were assessed.The consistency between calculating unprotected coronary territory(UCT)and the total time for image post-processing and diagnosis were compared between groups.Results AI group showed average consistency with manual group for evaluating the number of grafts([intra-class correlation coefficient,ICC]=0.743,P＜0.05),average to excellent for evaluating the maximum degree of graft stenosis(Kappa=0.310-1.000,all P＜0.05),also average to good consistency for evaluating the maximum degree of stenosis of the native vessel distal to the graft insertion(Kappa=0.292-0.795,all P＜0.05).AI group had moderate consistency with manual group for UCT(ICC=0.469,P＜0.05),achieved an area under the curve of 0.811.The overall time of image post-processing and diagnosis in AI group were both significantly shorter than that in manual group(P＜0.05).Conclusion Having acceptable consistency with manual evaluation and ability for assistant,AI was efficient for automatic reconstructing coronary artery bypass graft and quantifying the degree of graft stenosis.

Objective:To investigate the characteristics of magnetic resonance imaging and clinical application of multi-modality magnetic resonance imaging (MRI) for evaluating the response of neoadjuvant chemotherapy (NACT) in inflammatory breast cancer (IBC).Methods:A total of 36 IBC patients were enrolled in the study.The morphological, hemodynamic and diffusion-weighted imaging features of MRI were analyzed. Eleven patients underwent MRI examination before and after NAT. The imaging changes were analyzed and the efficacy of NACT was evaluated.Results:There were 38 identified breast carcinoma in these 36 cases, among which abnormal skin thickening and enhancement, extensive edema was found in 37 breast lesions. Enhancement of breast lesions in 25 cases was non-mass-like enhancement. Diffusion limitation was found in all lesions. The number of vessels in affected side was more than that in healthy side in MIP images. Thirty three cases had axillary lymph node enlargement.The sensitivity and specificity of MRI in evaluating residual breast tumors and vascular thrombus were high, but the evaluation of axillary lymph nodes was relatively low.Conclusions:Multi-modal MRI can be used for early and accurate diagnosis of IBC. It can also be used to predict and evaluate the effect of neoadjuvant chemotherapy.