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Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging

  • Imaging Informatics and Artificial Intelligence
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A Correction to this article was published on 26 April 2021

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There currently lacks a noninvasive and accurate method to distinguish benign and malignant ovarian lesion prior to treatment. This study developed a deep learning algorithm that distinguishes benign from malignant ovarian lesion by applying a convolutional neural network on routine MR imaging.


Five hundred forty-five lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training, validation, and testing set in a 7:2:1 ratio. Model performance was compared with four junior and three senior radiologists on the test set.


Compared with junior radiologists averaged, the final ensemble model combining MR imaging and clinical variables had a higher test accuracy (0.87 vs 0.64, p < 0.001) and specificity (0.92 vs 0.64, p < 0.001) with comparable sensitivity (0.75 vs 0.63, p = 0.407). Against the senior radiologists averaged, the final ensemble model also had a higher test accuracy (0.87 vs 0.74, p = 0.033) and specificity (0.92 vs 0.70, p < 0.001) with comparable sensitivity (0.75 vs 0.83, p = 0.557). Assisted by the model’s probabilities, the junior radiologists achieved a higher average test accuracy (0.77 vs 0.64, Δ = 0.13, p < 0.001) and specificity (0.81 vs 0.64, Δ = 0.17, p < 0.001) with unchanged sensitivity (0.69 vs 0.63, Δ = 0.06, p = 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Δ = 0.11, p = 0.005) but similar accuracy (0.77 vs 0.74, Δ = 0.03, p = 0.409) and sensitivity (0.69 vs 0.83, Δ = -0.146, p = 0.097) when compared with the senior radiologists.


These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance.

Key Points

• Artificial Intelligence based on deep learning can assess the nature of ovarian lesions on routine MRI with higher accuracy and specificity than radiologists.

• Assisted by the deep learning model’s probabilities, junior radiologists achieved better performance that matched those of senior radiologists.

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We would like to acknowledge and thank Huizhou Li (HZL) and Dan Li (DL) for performing the segmentation of MR images on the test set. We would like to thank Hui Liu (HL), Ting Huang (TH), Xin Su (XS), and Yijun Zhao (YJZ) for evaluating the test set as junior radiologists, and Dehong Peng (DHP), Shuanglin Zeng (SLZ), and Juan Chen (JC) for evaluating the test set as senior radiologists.


This work was supported by RSNA Research Scholar Grant to H. Bai, by a training grant from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (5T32EB1680) to K. Chang and by the National Cancer Institute (NCI) of the National Institutes of Health (F30CA239407) to K. Chang. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. TL was partially supported by Institutional Development Award Number U54GM115677 from the National Institute of General Medical Sciences of the National Institutes of Health which funds Advance Clinical and Translational Research (Advance-CTR).

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Correspondence to Jing Wu or Harrison X. Bai.

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The scientific guarantor of this publication is Harrison Bai.

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Wang, R., Cai, Y., Lee, I.K. et al. Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging. Eur Radiol 31, 4960–4971 (2021).

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