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• Clinical Article •
T2WI-based radiomics for discriminating between ovarian adult-type granulosa cell tumor and ovarian fibroma-thecoma with high-signal intensity on DWI
WANG Feng  QIN Siyuan  ZHOU Yan  WANG Qizheng  LIU Jianyu  LANG Ning 

Cite this article as: WANG F, QIN S Y, ZHOU Y, et al. T2WI-based radiomics for discriminating between ovarian adult-type granulosa cell tumor and ovarian fibroma-thecoma with high-signal intensity on DWI[J]. Chin J Magn Reson Imaging, 2024, 15(8): 152-157, 165. DOI:10.12015/issn.1674-8034.2024.08.023.


[Abstract] Objective To investigate the value of T2WI-based radiomics nomogram for the preoperative differentiation of ovarian adult-type granulosa cell tumor and ovarian fibroma-thecoma with high-signal intensity on diffusion weighted imaging (DWI).Materials and Methods This retrospective study included 29 patients with ovarian granulosa cell tumors and 61 cases with fibroma-thecomas with high-signal intensity on DWI, which were confirmed by surgical pathology in Peking University Third Hospital from January 2019 to October 2023. All tumors were randomly divided into a training set and a validation set at a ratio of 7∶3. The clinical model was constructed by clinical and routine MRI features which were selected by univariate analysis and multivariate logistic regression. Radiomics features were extracted from T2WI. Select K best and least absolute shrinkage and selection operator (LASSO) algorithm were used to reduce the dimension and then the radiomics model was constructed by selected features, and a radiomics score (Rad-score) was calculated. The nomogram model was constructed by combining with clinical model and Rad-score. The receiver operator characteristic (ROC) curves were used to evaluate the performance of three models. The decision curve analysis (DCA) was used to evaluate the clinical value.Results The logistic regression results showed that a "honeycomb" cyst [odds ratio (OR)=0.20, 95% confidence interval (CI)=0.05-0.79, P=0.022] and intratumoral hemorrhage (OR=0.16, 95% CI=0.03-0.98, P=0.048) can be used to construct the clinical model. A total of 9 features were extracted from T2WI to build the radiomics model. Finally, the nomogram model incorporating Rad-score, a "honeycomb" cyst and intratumoral hemorrhage was established. The AUCs of radiomics model and nomogram model were higher than those of clinical model (training set: 0.983 vs. 0.742, Z=-4.058, P<0.001; 0.969 vs. 0.742, Z=-3.817, P<0.001. validation set: 0.858 vs. 0.731, Z=-1.388, P=0.165; 0.883 vs. 0.731, Z=-1.612, P=0.107). There was no significantly difference in AUCs between the radiomics model and nomogram model (training set: Z=-1.040, P=0.298; validation set: Z=0.822, P=0.411). DCA results showed that the nomogram model and radiomics model had higher net benefits than the clinical model.Conclusions The MRI-based radiomics model and nomogram model constructed in this study can distinguish ovarian granulosa cell tumor from ovarian fibroma-thecoma with high-signal intensity on DWI effectively, which is better than the conventional T2WI-based clinical model.
[Keywords] ovarian tumors；magnetic resonance imaging；radiomics；nomogram；diagnosis

WANG Feng   QIN Siyuan   ZHOU Yan   WANG Qizheng   LIU Jianyu   LANG Ning*  

Department of Radiology, Peking University Third Hospital, Beijing 100191, China

Corresponding author: LANG N, E-mail: langning800129@126.com

Conflicts of interest   None.

Received  2024-05-20
Accepted  2024-07-29
DOI: 10.12015/issn.1674-8034.2024.08.023
Cite this article as: WANG F, QIN S Y, ZHOU Y, et al. T2WI-based radiomics for discriminating between ovarian adult-type granulosa cell tumor and ovarian fibroma-thecoma with high-signal intensity on DWI[J]. Chin J Magn Reson Imaging, 2024, 15(8): 152-157, 165. DOI:10.12015/issn.1674-8034.2024.08.023.

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