Objective To explore the value of radiomics model and deep learning model based on unenhanced MRI in predicting microvascular invasion(MVI)of hepatocellular carcinoma(HCC)preoperatively.Methods A total of 189 patients with postopera-tive pathologically confirmed HCC from two centers were retrospectively selected,of which 119 cases from Zhujiang Hospital of Southern Medical University were used as the training set[60 cases with negative MVI,59 cases with positive MVI],and 70 cases from Shenzhen People's Hospital were used as the external test set[38 cases with negative MVI and 32 cases with positive MVI].Clinical indicators were analyzed by univariate and multivariate logistic regression analysis and the independent predictors of positive MVI were screened.Deep transfer learning(DTL)and traditional radiomics methods were used to construct radiomics model and deep learning model based on unenhanced MRI.The predictive performances of each model were compared using receiver operating charac-teristic(ROC)curves and area under the curve(AUC).DeLong test was employed to compare statistical differences in performance of the models.Results Alkaline phosphatase(ALP)and prothrombin time(PT)were independent predictors of positive MVI(P<0.05).The deep learning model based on T2WI had the best predictive efficacy,with AUC of 0.779[95%confidence interval(CI)0.696-0.863]and 0.741(95%CI 0.620-0.861)in the training set and external test set,respectively,and there were statistically significant differences compared with the radiomics model and the clinical model based on T1WI(P<0.05).Conclusion Deep learning model based on T2WI has a certain application value in preoperative noninvasive prediction of MVI status in HCC patients.

Objective To explore the value of radiomics model and deep learning model based on unenhanced MRI in predicting microvascular invasion(MVI)of hepatocellular carcinoma(HCC)preoperatively.Methods A total of 189 patients with postopera-tive pathologically confirmed HCC from two centers were retrospectively selected,of which 119 cases from Zhujiang Hospital of Southern Medical University were used as the training set[60 cases with negative MVI,59 cases with positive MVI],and 70 cases from Shenzhen People's Hospital were used as the external test set[38 cases with negative MVI and 32 cases with positive MVI].Clinical indicators were analyzed by univariate and multivariate logistic regression analysis and the independent predictors of positive MVI were screened.Deep transfer learning(DTL)and traditional radiomics methods were used to construct radiomics model and deep learning model based on unenhanced MRI.The predictive performances of each model were compared using receiver operating charac-teristic(ROC)curves and area under the curve(AUC).DeLong test was employed to compare statistical differences in performance of the models.Results Alkaline phosphatase(ALP)and prothrombin time(PT)were independent predictors of positive MVI(P<0.05).The deep learning model based on T2WI had the best predictive efficacy,with AUC of 0.779[95%confidence interval(CI)0.696-0.863]and 0.741(95%CI 0.620-0.861)in the training set and external test set,respectively,and there were statistically significant differences compared with the radiomics model and the clinical model based on T1WI(P<0.05).Conclusion Deep learning model based on T2WI has a certain application value in preoperative noninvasive prediction of MVI status in HCC patients.

Objective To observe the value of radiomics models based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid(Gd-EOB-DTPA)enhanced hepatobiliary phase(HBP)MRI for assessing clinical pathological stage of hepatic fibrosis(HF).Methods Data of 240 patients with pathologically/clinically diagnosed and clinical pathological staged HF who underwent Gd-EOB-DTPA enhanced MR examination were retrospectively analyzed.The liver-to-muscle signal intensity ratio(SIR1)and liver-to-spleen signal intensity ratio(SIR2)were measured based on HBP images.Radiomics features of HBP images were extracted and screened to construct radiomics models.The signal intensity ratio(SIR)-radiomics combined models were constructed based on SIR and radiomics signatures.Receiver operating characteristic(ROC)curves were drawn to evaluate the efficacy of each model for assessing clinical pathological stage of HF.Results The area under the curve(AUC)of SIR1 and SIR2 models for assessing clinical pathological stage of HF were 0.63-0.70 and 0.65-0.71,respectively.The most effective radiomics model for assessing HF,significant HF,advanced HF and early cirrhosis was support vector machine(SVM),SVM,light gradient boosting machine and K-nearest neighbor model,respectively,with the AUC in validation set of 0.87,0.82,0.81 and 0.80,respectively,while the AUC of SIR-radiomics combined models in validation set of 0.88,0.82,0.82 and 0.81,respectively.Conclusion The radiomics models based on Gd-EOB-DTPA enhanced HBP MRI were helpful for assessing clinical pathological stage of HF.Combining with HBP SIR could improve their efficacy.

Background: To evaluate the feasibility of treating odontoid fractures in the Chinese population with two cortical screws based on computed tomography (CT) scans and describe a new measurement strategy to guide screw insertion in treating these fractures. Methods: A retrospective review of cervical computed tomographic scans of 128 patients (aged 18–76 years; men, 55 [43.0%]) was performed. The minimum external transverse diameter (METD), minimum external anteroposterior diameter (MEAD), maximum screw length (MSL), and screw projection back angle (SPBA) of the odontoid process were measured on coronal and sagittal CT images. Results: The mean values of METD and MEAD were 10.0 ± 1.1 mm and 12.0 ± 1.0 mm, respectively, in men and 9.2 ± 1.0 mm and 11.0 ± 1.0 mm, respectively, in women. Both measurements were significantly higher in men (p < 0.001). In total, 87 individuals (68%) had METD > 9.0 mm that could accommodate two 3.5-mm cortical screws. The mean MSL value and SPBA range were 34.4 ± 2.9 mm and 13.5°–24.2°, respectively, with no statistically significant difference between men and women. Conclusions The insertion of two 3.5-mm cortical screws was possible for anterior fixation of odontoid fractures in 87 patients (68%) in our study, and there was a statistically significant difference between men and women.