Objective To evaluate an ultrasound-based deep learning radiomics nomogram(DLR_Nomo-gram)for non-invasively differentiating between type Ⅰ and type Ⅱ epithelial ovarian cancer(EOC)before surgery.Methods In this study,a cohort of 195 patients diagnosed with EOC was analyzed.Participants were randomly divided into a training set and a testing set at an 8∶2 ratio.Following data preprocessing,region of interest(ROI)delineation,feature extraction and selection,as well as the clipping and extraction of the maximum section sonogram for each sample,three initial models were developed:the radiomics signature(Rad_Sig),the deep transfer learning signature(DTL_Sig),and the clinical signature(Clinic_Sig).Subsequently,an integrated model—referred to as the DLR_Nomogram—was constructed by combining Rad_Sig,DTL_Sig,and Clinic_Sig,and was presented in the form of a nomogram.The performance of the model was evaluated using the receiver operating characteristic(ROC)curve and the corresponding area under the curve(AUC).Results In the testing set,the DLR_Nomogram demonstrated superior predictive performance(AUC:0.951,95%CI:0.876～1.000)compared to Rad_Sig(AUC:0.709,95%CI:0.539～0.880),DTL_Sig(AUC:0.842,95%CI:0.712～0.972),and Clinic_Sig(AUC:0.916,95%CI:0.827～1.000).The Hosmer-Lemeshow goodness-of-fit test for the DLR_Nomogram resulted in a p-value exceeding 0.05,indicating adequate model calibration.Moreover,decision curve analysis revealed that the DLR_No-mogram offers a higher net clinical benefit across a defined range of threshold probabilities.Conclusions The ultrasound-based DLR_Nomogram exhibits a robust ability to differentiate between Type Ⅰ and Type Ⅱ EOC,and may serve as a valuable clinical tool for guiding individualized preoperative diagnostic and therapeutic decision-making.

Objective To evaluate an ultrasound-based deep learning radiomics nomogram(DLR_Nomo-gram)for non-invasively differentiating between type Ⅰ and type Ⅱ epithelial ovarian cancer(EOC)before surgery.Methods In this study,a cohort of 195 patients diagnosed with EOC was analyzed.Participants were randomly divided into a training set and a testing set at an 8∶2 ratio.Following data preprocessing,region of interest(ROI)delineation,feature extraction and selection,as well as the clipping and extraction of the maximum section sonogram for each sample,three initial models were developed:the radiomics signature(Rad_Sig),the deep transfer learning signature(DTL_Sig),and the clinical signature(Clinic_Sig).Subsequently,an integrated model—referred to as the DLR_Nomogram—was constructed by combining Rad_Sig,DTL_Sig,and Clinic_Sig,and was presented in the form of a nomogram.The performance of the model was evaluated using the receiver operating characteristic(ROC)curve and the corresponding area under the curve(AUC).Results In the testing set,the DLR_Nomogram demonstrated superior predictive performance(AUC:0.951,95%CI:0.876～1.000)compared to Rad_Sig(AUC:0.709,95%CI:0.539～0.880),DTL_Sig(AUC:0.842,95%CI:0.712～0.972),and Clinic_Sig(AUC:0.916,95%CI:0.827～1.000).The Hosmer-Lemeshow goodness-of-fit test for the DLR_Nomogram resulted in a p-value exceeding 0.05,indicating adequate model calibration.Moreover,decision curve analysis revealed that the DLR_No-mogram offers a higher net clinical benefit across a defined range of threshold probabilities.Conclusions The ultrasound-based DLR_Nomogram exhibits a robust ability to differentiate between Type Ⅰ and Type Ⅱ EOC,and may serve as a valuable clinical tool for guiding individualized preoperative diagnostic and therapeutic decision-making.