4.7 Article

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

期刊

EUROPEAN RADIOLOGY
卷 31, 期 7, 页码 4960-4971

出版社

SPRINGER
DOI: 10.1007/s00330-020-07266-x

关键词

Ovarian neoplasms; Deep learning; Magnetic resonance imaging

资金

  1. RSNA Research Scholar Grant
  2. National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health [5T32EB1680]
  3. National Cancer Institute (NCI) of the National Institutes of Health [F30CA239407]
  4. National Institute of General Medical Sciences of the National Institutes of Health [U54GM115677]

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This study developed a deep learning algorithm that accurately distinguishes benign from malignant ovarian lesions on routine MR imaging. The results show that the AI-based model can assist radiologists in improving their performance in assessing the nature of ovarian lesions, especially compared to junior radiologists.
Objectives 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. Methods 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. Results 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, Delta = 0.13,p < 0.001) and specificity (0.81 vs 0.64, Delta = 0.17,p < 0.001) with unchanged sensitivity (0.69 vs 0.63, Delta = 0.06,p = 0.302). With the AI probabilities, the junior radiologists had higher specificity (0.81 vs 0.70, Delta = 0.11,p = 0.005) but similar accuracy (0.77 vs 0.74, Delta = 0.03,p = 0.409) and sensitivity (0.69 vs 0.83, Delta = -0.146,p = 0.097) when compared with the senior radiologists. Conclusions These results demonstrate that artificial intelligence based on deep learning can assist radiologists in assessing the nature of ovarian lesions and improve their performance.

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