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Clinical Article
Differentiation of borderline and malignant epithelial tumors based on MRI-T2WI radiomics nomogram
DING Cong  WEI Mingxiang  JIA jianye  ZHOU Wei  BAI Genji 

Cite this article as: Ding C, Wei MX, Jia JY, et al. Differentiation of borderline and malignant epithelial tumors based on MRI-T2WI radiomics nomogram[J]. Chin J Magn Reson Imaging, 2022, 13(7): 55-60. DOI:10.12015/issn.1674-8034.2022.07.010.

[Abstract] Objective To develop and validate a radiomics nomogram that was based on MRI-T2WI to distinguish between borderline epithelial ovarian tumors (BEOTs) and malignant epithelial ovarian tumors (MEOTs).Materials and Methods The clinical and imaging data of 192 patients with epithelial ovarian tumors confirmed by pathology from January 2016 to May 2021 were retrospectively analyzed in the Affiliated Huaian First People's Hospital of Nanjing Medical University, including EBOTs (n=72) and MEOTs (n=153) were enrolled. According to the ratio of 8∶2,all cases were randomly divided into the training group (n=153) and validation group (n=39). We used T2WI to manually delineated ROI and extract radiomics features. Mann-Whitney U test, correlation and LASSO regression were used to select features, and then constructed radiomics model by these features, used to calculate Radscore. Combining Radscore with clinic factors, we used multiple logistic regression to construct radiomics nomogram. ROC curve, calibration curve and decision curve analysis and correction were used to evaluate the clinical value of radiomics nomogram.Results We reserved 10 radiomics features after the feature was filtered. The AUC of the radiomics nomogram which combined HE4 with Radscore in the training group and validation group (training group: 0.947, validation group: 0.914) were higher than those of the single radiomics model (training group:0.925, validation group:0.819). ROC and DCA results showed that the radiomics nomogram had higher reliability.Conclusions The radiomics nomogram combined radiomics feature based on T2WI and clinical factors is able to distinguish between BEOTs and MEOTs intuitively and accurately and provide guidance for the next clinical decision.
[Keywords] ovarian tumors;radiomics;machine learning;nomogram;magnetic resonance imaging;T2-weighted imaging

DING Cong1   WEI Mingxiang2   JIA jianye1   ZHOU Wei1   BAI Genji1*  

1 Department of Imaging, the Affiliated Huaian No.1 People's Hospital of Nanjing Medical University,Huaian 223000, China

2 Department of Imaging, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215000, China

Bai GJ, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Beijing Medical Health Public Welfare Fund (No. B20240ES).
Received  2022-03-08
Accepted  2022-07-08
DOI: 10.12015/issn.1674-8034.2022.07.010
Cite this article as: Ding C, Wei MX, Jia JY, et al. Differentiation of borderline and malignant epithelial tumors based on MRI-T2WI radiomics nomogram[J]. Chin J Magn Reson Imaging, 2022, 13(7): 55-60. DOI:10.12015/issn.1674-8034.2022.07.010.

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