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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Vydáno v:Insights into imaging Ročník 14; číslo 1; s. 68 - 10
Hlavní autoři: 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
Médium: Journal Article
Jazyk:angličtina
Vydáno: Vienna Springer Vienna 24.04.2023
Springer Nature B.V
SpringerOpen
Témata:
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
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Shrnutí: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 and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set. Results  Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists. Conclusions  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. Key points CT-based radiomics and deep learning features could differentiate ovarian tumors. Radiomics, deep learning features, and clinical data provided complementary tumor information. The ensemble model improved the radiologists’ performance in assessing ovarian tumors.
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ISSN:1869-4101
1869-4101
DOI:10.1186/s13244-023-01412-x