Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors.
Ya-Ting JanPei-Shan TsaiWen-Hui HuangLing-Ying ChouShih-Chieh HuangJing-Zhe WangPei-Hsuan LuDao-Chen LinChun-Sheng YenJu-Ping TengGreta S P MokCheng-Ting ShihTung-Hsin WuPublished in: Insights into imaging (2023)
We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.
Keyphrases
- deep learning
- artificial intelligence
- machine learning
- computed tomography
- end stage renal disease
- contrast enhanced
- convolutional neural network
- ejection fraction
- chronic kidney disease
- image quality
- big data
- squamous cell carcinoma
- prognostic factors
- dual energy
- positron emission tomography
- patient reported outcomes
- magnetic resonance imaging
- peritoneal dialysis