The Journal of Practical Medicine >

2025 , Vol. 41 >Issue 18: 2920 - 2927

DOI: https://doi.org/10.3969/j.issn.1006-5725.2025.18.020

Medical Examination and Clinical Diagnosis

Ultrasound⁃based deep learning radiomics nomogram to differentiate type Ⅰ and type Ⅱ epithelial ovarian cancer

  • Yangchun DU ,
  • Hongyu ZHENG ,
  • Haining CHEN ,
  • Wenwen GUO ,
  • Jinxiu YAO ,
  • Tongliu LAN ,
  • Yanju XIAO
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  • Department of Ultrasound，the People's Hospital of Guangxi Zhuang Autonomous Region，Nanning 530021，Guangxi，China

Received date: 2025-06-06

  Online published: 2025-09-25

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Abstract

Objective To evaluate an ultrasound-based deep learning radiomics nomogram （DLR_Nomogram） 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_Nomogram 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.

Key words： epithelial ovarian cancer; ultrasound; radiomics; deep transfer learning; nomogram

Cite this article

Yangchun DU , Hongyu ZHENG , Haining CHEN , Wenwen GUO , Jinxiu YAO , Tongliu LAN , Yanju XIAO . Ultrasound⁃based deep learning radiomics nomogram to differentiate type Ⅰ and type Ⅱ epithelial ovarian cancer[J]. The Journal of Practical Medicine, 2025 , 41(18) : 2920 -2927 . DOI: 10.3969/j.issn.1006-5725.2025.18.020

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