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Radiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasms

  • Oncology
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To evaluate the efficiency of 2- and 3-class classification predictive tasks constructed from radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic (PK) protocol in discriminating among benign, borderline, and malignant ovarian tumors.


One hundred and four ovarian lesions were evaluated using preoperative DCE-MRI. Radiomics features were extracted from 7 types of DCE-MR images. To explore the differential ability of radiomics between three types of ovarian tumors, two- and three-class classification tasks were established. The 2-class classification task was divided into three subtasks: benign vs. borderline (task A), benign vs. malignant (task B), and borderline vs. malignant (task C). For the 3-class classification task, 104 lesions were randomly divided into training (72 lesions) and validation (32 lesions) cohorts. The discrimination abilities of the radiomics signatures were established with the training cohort and tested with the independent validation cohort. The predictive performance of the task was evaluated by receiver operating characteristic (ROC) curve, calibration curve analysis, and decision curve analysis (DCA).


For the 2-class classification task, the combination of PK radiomics signatures model (PK model) showed a good diagnostic ability with the highest area under the ROC curves (AUCs) of 0.899, 0.865, and 0.893 for tasks A, B, and C, respectively. Additionally, the 3-class classification task demonstrated a good discrimination performance with AUCs of 0.893, 0.944, and 0.891 for the benign, borderline, and malignant groups, respectively.


Radiomics analysis based on the DCE-MRI PK protocol showed promise for discriminating among benign, borderline, and malignant ovarian tumors.

Key Points

• Two-class classification predictive task of DCE-MRI PK protocol enabled the classification of 3 categories of ovarian tumors through the pairwise comparison strategy with a perfect diagnostic ability.

• Three-class classification predictive task maintained good performance to effectively judge each category of ovarian tumors directly.

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Arterial input function


Area under the ROI curve


Contrast-enhanced ratio


Decision curve analysis


Dynamic contrast enhanced


Blood plasma volume


Initial area under the gadolinium contrast agent concentration time curve

Kep :

Rate of contrast agent transport from the tumor to the blood

Ktrans :

Rate of contrast agent uptake into the tumor from the blood


Liver acquisition with volume acceleration


Magnetic resonance imaging


Minimum redundancy maximum relevance


Receiver operating characteristic

Ve :

Volume of the extravascular extracellular space


Volume of interest


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Applied Basic Research Programs of Shanxi Province (201801D221116, 201701D121142).

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Correspondence to Jinliang Niu.

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The scientific guarantor of this publication is Dr. Jinliang Niu.

Conflict of interest

One of the authors of this manuscript (Jia-Liang Ren) is an employee of GE Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors (Jia-Liang Ren) has significant statistical expertise.

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Written informed consent was waived in this study.

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Institutional Review Board approval was obtained.


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Song, Xl., Ren, JL., Zhao, D. et al. Radiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasms. Eur Radiol 31, 368–378 (2021).

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