Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors

Background To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors. Methods We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included...

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Veröffentlicht in:	Insights into imaging Jg. 14; H. 1; S. 68 - 10
Hauptverfasser:	Jan, Ya-Ting, Tsai, Pei-Shan, Huang, Wen-Hui, Chou, Ling-Ying, Huang, Shih-Chieh, Wang, Jing-Zhe, Lu, Pei-Hsuan, Lin, Dao-Chen, Yen, Chun-Sheng, Teng, Ju-Ping, Mok, Greta S. P., Shih, Cheng-Ting, Wu, Tung-Hsin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Vienna Springer Vienna 24.04.2023 Springer Nature B.V SpringerOpen
Schlagworte:	Accuracy Artificial intelligence Computed tomography Deep learning Diagnostic Radiology Image contrast Image enhancement Imaging Internal Medicine Interventional Radiology Machine learning Medical imaging Medicine Medicine & Public Health Model accuracy Neuroradiology Original Original Article Ovarian cancer Ovarian tumor Ovaries Radiology Radiomics Sensitivity Tumors Ultrasound Deep learning Computed tomography Machine learning Ovarian tumor Radiomics
ISSN:	1869-4101, 1869-4101
Online-Zugang:	Volltext
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