Purpose To explore deep transfer learning(DTL)based on multi-parameter MRI for joint prediction of uterine endometrial cancer microsatellite expression with clinical parameters.Materials and Methods Retrospective analysis was conducted on 262 patients with uterine endometrial cancer in the Affiliated Huaian NO.1 People's Hospital of Nanjing Medical University from January 2020 to December 2022,who were randomly divided into a training cohort(n=183)and a validation cohort(n=79)in a 7∶3 ratio.DTL features were extracted from T2WI and diffusion-weighted imaging(DWI)using a 50-layer residual neural network(ResNet50).Subsequently,T2-net,DWI-net,multi-sequence fusion and a combined model were constructed.A combined model was constructed via incorporating multiparametric fusion DTL features and clinically independent prognostic factors identified through both univariate and multivariate Logistic regression analysis.Model performance was assessed using the area under the curve,calibration curve and decision curve.Grad-CAM was used for model visualization analysis.Results Compared with single-sequence models,the multi-sequence fusion models exhibited superior performance,with area under the curve value of 0.898 for the validation cohort,accuracy of 0.823,sensitivity of 0.812,specificity of 0.825,respectively,and F1 score was 0.650.Univariate and multivariate Logistic regression analyses revealed that serum human epididymis protein 4 levels and the presence of uterine fibroids were clinically independent risk factors.Ultimately,the combined model demonstrated the best predictive performance in the validation cohort,with area under the curve value of 0.924,accuracy of 0.835,sensitivity of 0.875,specificity of 0.825,respectively,and F1 score was 0.683.Conclusion The multi-parameter fusion model based on DTL features can effectively and non-invasively predict the microsatellite status of endometrial cancer patients.

Purpose To explore deep transfer learning(DTL)based on multi-parameter MRI for joint prediction of uterine endometrial cancer microsatellite expression with clinical parameters.Materials and Methods Retrospective analysis was conducted on 262 patients with uterine endometrial cancer in the Affiliated Huaian NO.1 People's Hospital of Nanjing Medical University from January 2020 to December 2022,who were randomly divided into a training cohort(n=183)and a validation cohort(n=79)in a 7∶3 ratio.DTL features were extracted from T2WI and diffusion-weighted imaging(DWI)using a 50-layer residual neural network(ResNet50).Subsequently,T2-net,DWI-net,multi-sequence fusion and a combined model were constructed.A combined model was constructed via incorporating multiparametric fusion DTL features and clinically independent prognostic factors identified through both univariate and multivariate Logistic regression analysis.Model performance was assessed using the area under the curve,calibration curve and decision curve.Grad-CAM was used for model visualization analysis.Results Compared with single-sequence models,the multi-sequence fusion models exhibited superior performance,with area under the curve value of 0.898 for the validation cohort,accuracy of 0.823,sensitivity of 0.812,specificity of 0.825,respectively,and F1 score was 0.650.Univariate and multivariate Logistic regression analyses revealed that serum human epididymis protein 4 levels and the presence of uterine fibroids were clinically independent risk factors.Ultimately,the combined model demonstrated the best predictive performance in the validation cohort,with area under the curve value of 0.924,accuracy of 0.835,sensitivity of 0.875,specificity of 0.825,respectively,and F1 score was 0.683.Conclusion The multi-parameter fusion model based on DTL features can effectively and non-invasively predict the microsatellite status of endometrial cancer patients.

Objective ToexplorethediagnosticvalueofenhancedCTandGd-EOB-DTPA-enhanced MRIinthedetectionofhepatocellular carcinoma (HCC).Methods 41patientswith52HCCsunderwentenhancedCTandGd-EOB-DTPA-enhancedMRIrespectively.The imagingmanifestationswereanalyzed,andthelesionsweregrouped.Theimagingdiagnositicresultswerecomparedwiththepathologicalresults, andtheefficacyofthetwomodalitieswasevaluatedusingtheaccuracyindex.Results Ofthetotal52HCCs,42lesionswereidentifiedexactlyby enhancedCT,and51byGd-EOB-DTPA-enhancedMRI.Foralllesions,includingsmalllesions(≤2cm),theaccuracyratioofGd-EOB-DTPA-enhancedMRIwashigherthanthatofenhancedCT withastatisticallysignificantdifference(P<0.05),however,therewasnosignificant differenceforthelesionsbiggerthan2cm (P>0.05).Conclusion EnhancedCTislimitedindiagnosisoftheHCCssmallerthanor equalto2cm.ThediagnosticefficacyofGd-EOB-DTPA-enhanced MRIishigherthanthatofenhancedCT,anditcansignificantly improvethediagnosisofsmallHCC.

Objective To explore the value of apparent diffusion coefficient (ADC)in differentiating brain tuberculomas from me-tastases.Methods Conventional and enhanced MRI as well as diffusion weighted imaging (DWI)were performed in 24 cases of brain tuberculomas(immature in 18 cases and mature in 6 case)and 36 cases of metastases.The mean ADC values and relative ADC (rADC)values were calculated from the enhanced and non-enhanced regions of mass and the peripheral edema regions of brain le-sions.Results The mean ADC values and rADC values in the enhanced,non-enhanced and the peripheral edema regions were 796.90×10 -6 mm2/s and 1.1 6,864.85×10 -6 mm2/s and 1.27,1 531.60×10 -6 mm2/s and 2.24 for the immature brain tuberculo-mas;791.95×10 -6 mm2/s and 1.1 6,61 1.80×10 -6 mm2/s and 0.87,and 1 488.45×10 -6 mm2/s and 2.10 for the mature tubercu-lomas;421.95×10 -6 mm2/s and 0.61,961.00×10 -6 mm2/s and 1.36,1 545.00×10 -6 mm2/s and 2.18 for the brain metastases, respectively.There were significant differences in the mean ADC values (H =42.293,P ≤0.05)and rADC values (H =42.575, P ≤0.05)for the enhance regions in the three groups .There were also significant differences in the mean ADC values (H =33.100, P ≤0.05)and rADC values (H =1 7.867,P ≤0.05)for the non-enhance regions.No significant difference in the mean ADC values (H =1.550,P ≥0.05)and rADC values (H =5.511,P ≥0.05)were found for the peripheral edema regions.Conclusion The ADC values of DWI can help to differentiate brain tuberculomas from metastases,when combining with the conventional and enhanced MRI.