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Ovarian cancer is the fifth leading cause of cancer deaths among women of all ages, more deaths than any other gynecological malignancy.
Ovarian cancer is the fifth leading cause of cancer deaths among women of all ages, more deaths than any other gynecological malignancy.
Magnetic resonance imaging (MRI) has high soft tissue resolution and has become an important examination method for ovarian disease at this stage.
Recently, a study published in the journal European Radiology has developed a deep learning algorithm that uses convolutional neural networks on conventional MR imaging to identify benign and malignant ovarian lesions, and establishes a preoperative evaluation of non-invasive ovarian lesions.
A total of 455 lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training set, validation set and test set, with a ratio of 7:2:1.
A total of 455 lesions (379 benign and 166 malignant) from 451 patients from a single institution were divided into training set, validation set and test set, with a ratio of 7:2:1.
Compared with the average level of junior radiologists, the final combined model combining MR imaging and clinical variables has higher detection accuracy (0.
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This research report developed a deep learning algorithm based on conventional MR imaging and clinical variables to distinguish benign and malignant ovarian tumors.
Original source: Original source:
Robin Wang,Yeyu Cai,Iris K Lee,et al.
Robin Wang,Yeyu Cai,Iris K Lee,et al.
Evaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imaging.
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1007/s00330-020-07266-x Robin Wang,Yeyu Cai,Iris K Lee, Evaluation of A Convolutional Al et Neural Network for ovarian tumor differentiation based Magnetic Resonance imaging.
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