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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| Published in: | Insights into imaging Vol. 14; no. 1; pp. 68 - 10 |
|---|---|
| Main Authors: | , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Vienna
Springer Vienna
24.04.2023
Springer Nature B.V SpringerOpen |
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| ISSN: | 1869-4101, 1869-4101 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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.BACKGROUNDTo develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors.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.METHODSWe 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. 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.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. 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.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. Abstract 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. BackgroundTo develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors.MethodsWe 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 pointsCT-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. 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. 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. 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. 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. 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. 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. |
| ArticleNumber | 68 |
| Author | Jan, Ya-Ting Chou, Ling-Ying Shih, Cheng-Ting Huang, Wen-Hui Lin, Dao-Chen Huang, Shih-Chieh Yen, Chun-Sheng Wu, Tung-Hsin Lu, Pei-Hsuan Teng, Ju-Ping Mok, Greta S. P. Tsai, Pei-Shan Wang, Jing-Zhe |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37093321$$D View this record in MEDLINE/PubMed |
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| Keywords | Deep learning Computed tomography Machine learning Ovarian tumor Radiomics |
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CA: A Cancer J Clinic 69:127–157 JeongYYOutwaterEKKangHKImaging evaluation of ovarian massesRadiographics200020144514701:STN:280:DC%2BD3cvpvVajtQ%3D%3D10.1148/radiographics.20.5.g00se10144510992033 WangSLiuZRongYDeep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancerRadiother Oncol201913217117710.1016/j.radonc.2018.10.01930392780 SongXLRenJLZhaoDWangLRenHNiuJRadiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasmsEur Radiol2021313683781:CAS:528:DC%2BB3cXhsFOitLjM10.1007/s00330-020-07112-032767049 JianJYaLiPickhardtPJMR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancersEur Radiol20213140341010.1007/s00330-020-07091-232743768 WangRCaiYLeeIKEvaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imagingEur Radiol202010.1007/s00330-020-07266-x332415148043900 Siegel RL, Miller KD, Jemal A (2019) Cancer statistics. CA Cancer J Clinic 69: 7–34 VargasHAVeeraraghavanHMiccoMA novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcomeEur Radiol2017273991400110.1007/s00330-017-4779-y282899455545058 YuXPWangLYuHYMDCT-based radiomics features for the differentiation of serous borderline ovarian tumors and serous malignant ovarian tumorsCancer Manage Res20211332933610.2147/CMAR.S284220 AnHWangYWongEMFCT texture analysis in histological classification of epithelial ovarian carcinomaEur Radiol2021315050505810.1007/s00330-020-07565-333409777 FotiPVAttinàGSpadolaSMR imaging of ovarian masses: classification and differential diagnosisInsights Imaging20167214110.1007/s13244-015-0455-426671276 ZhangHMaoYChenXMagnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary studyEur Radiol2019293358337110.1007/s00330-019-06124-930963272 Moore BJ, Steiner CA, Davis PH, Stocks C, Barrett ML (2006) Trends in hysterectomies and oophorectomies in hospital inpatient and ambulatory settings, 2005–2013: statistical brief #214healthcare cost and utilization project (HCUP) statistical briefs. 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| Snippet | Background
To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from... To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant... BackgroundTo develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from... CT-based radiomics and deep learning features could differentiate ovarian tumors.Radiomics, deep learning features, and clinical data provided complementary... Abstract Background To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish... |
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| StartPage | 68 |
| SubjectTerms | 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 |
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| Title | Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors |
| URI | https://link.springer.com/article/10.1186/s13244-023-01412-x https://www.ncbi.nlm.nih.gov/pubmed/37093321 https://www.proquest.com/docview/2805295062 https://www.proquest.com/docview/2805518148 https://pubmed.ncbi.nlm.nih.gov/PMC10126170 https://doaj.org/article/1384111336284d9faefb12e058bd8cb4 |
| Volume | 14 |
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