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 |
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| Hlavní autoři: | , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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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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| Cites_doi | 10.1007/s00330-020-07266-x 10.1016/j.artmed.2021.102164 10.1007/s00261-020-02668-3 10.1016/j.ygyno.2019.04.366 10.1158/1078-0432.CCR-17-0853 10.2147/CMAR.S279990 10.1109/CVPR.2017.243 10.2214/AJR.09.3522 10.1148/radiol.2361041618 10.2147/CMAR.S284220 10.1186/s41747-021-00226-0 10.1007/s00330-020-07091-2 10.3322/caac.21551 10.1016/j.ygyno.2022.07.024 10.1007/s00330-017-4779-y 10.1007/s00330-020-07565-3 10.1007/s00330-018-5389-z 10.1097/01.AOG.0000167394.38215.56 10.1007/s13244-015-0455-4 10.1016/S1470-2045(18)30413-3 10.1007/s00330-019-06124-9 10.3390/electronics8010020 10.1007/s00330-020-07112-0 10.1002/uog.23530 10.1007/978-3-642-21735-7_7 10.1148/radiology.214.1.r00ja3939 10.1001/jama.1993.03500090055032 10.3322/caac.21552 10.1016/j.radonc.2018.10.019 10.1148/radiographics.20.5.g00se101445 10.1109/ICECA.2018.8474912 10.1016/j.ygyno.2021.04.004 10.3389/fonc.2020.00418 10.1097/AOG.0000000000001768 10.1093/humupd/5.5.546 |
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| Keywords | Deep learning Computed tomography Machine learning Ovarian tumor Radiomics |
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| References | LassAThe fertility potential of women with a single ovaryHum Reprod Update199955465501:STN:280:DC%2BD3c%2FkslKitQ%3D%3D10.1093/humupd/5.5.54610582792 HricakHChenMCoakleyFVComplex adnexal masses: detection and characterization with MR imaging–multivariate analysisRadiology200021439461:STN:280:DC%2BD3c7gs12itw%3D%3D10.1148/radiology.214.1.r00ja393910644099 Bi WL, Hosny A, Schabath MB et al (2019) Artificial intelligence in cancer imaging: Clinical challenges and applications. 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. Agency for healthcare research and quality (US), Rockville (MD) ChristiansenFEpsteinELSmedbergEÅkerlundMSmithKEpsteinEUltrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessmentUltrasound Obstet Gynecol2021571551631:STN:280:DC%2BB3s3gtVWmsA%3D%3D10.1002/uog.23530331423597839489 SunRLimkinEJVakalopoulouMA radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort studyLancet Oncol201819118011911:CAS:528:DC%2BC1cXhsFansrzL10.1016/S1470-2045(18)30413-330120041 ParkerWHBroderMSLiuZShoupeDFarquharCBerekJSOvarian conservation at the time of hysterectomy for benign diseaseObstet Gynecol200510621922610.1097/01.AOG.0000167394.38215.5616055568 AkazawaMHashimotoKArtificial intelligence in gynecologic cancers: current status and future challenges – a systematic reviewArtif Intell Med202112010.1016/j.artmed.2021.10216434629152 XiaXGongJHaoWComparison and fusion of deep learning and radiomics features of ground-glass nodules to predict the invasiveness risk of stage-I lung adenocarcinomas in CT scanFront Oncol20201041810.3389/fonc.2020.00418322966457136522 VununuCLeeS-HKwonK-RA deep feature extraction method for HEp-2 cell image classificationElectronics201982010.3390/electronics8010020 NewtsonAMMattsonJNGoodheartMJPrediction of optimal surgical outcomes with radiologic images using deep learning artificial intelligenceGynecol Oncol201915415610.1016/j.ygyno.2019.04.366 MasciJMeierUCireşanDSchmidhuberJHonkelaTDuchWGirolamiMKaskiSStacked convolutional auto-encoders for hierarchical feature extractionArtificial neural networks and machine learning – ICANN 20112011Berlin HeidelbergSpringer525910.1007/978-3-642-21735-7_7 American College of Obstetricians and Gynecologists’ Committee on Practice Bulletins—GynecologyPractice bulletin no. 174: evaluation and management of adnexal massesObstet Gynecol20161285e210e22610.1097/AOG.0000000000001768 KinkelKLuYMehdizadeAPelteMFHricakHIndeterminate ovarian mass at US: incremental value of second imaging test for characterization–meta-analysis and Bayesian analysisRadiology2005236859410.1148/radiol.236104161815955864 ChaudharyKPoirionOBLuLGarmireLXDeep learning-based multi-omics integration robustly predicts survival in liver cancerClin Cancer Res201824124812591:CAS:528:DC%2BC1cXks1ymur0%3D10.1158/1078-0432.CCR-17-085328982688 ShresthaPPoudyalBYadollahiSA systematic review on the use of artificial intelligence in gynecologic imaging - background, state of the art, and future directionsGynecol Oncol202210.1016/j.ygyno.2022.07.02435914978 ChiappaVInterlenghiMSalvatoreCUsing rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumorsGynecol Oncol20211618388441:STN:280:DC%2BB3sbit1Gjtw%3D%3D10.1016/j.ygyno.2021.04.00433867144 ChiappaVInterlenghiMBoganiGA decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125Eur Radiol Exp202152810.1186/s41747-021-00226-0343084878310829 RizzoSBottaFRaimondiSRadiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 monthsEur Radiol2018284849485910.1007/s00330-018-5389-z29737390 IyerVRLeeSIMRI, CT, and PET/CT for ovarian cancer detection and adnexal lesion characterizationAJR Am J Roentgenol201019431132110.2214/AJR.09.352220093590 Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 2261–2269 HandRFremgenAChmielJSStaging procedures, clinical management, and survival outcome for ovarian carcinomaJAMA1993269111911221:STN:280:DyaK3s7msVeltw%3D%3D10.1001/jama.1993.035000900550328433466 ZhouJZengZYLiLProgress of artificial intelligence in gynecological malignant tumorsCancer Manage Res20201212823128401:CAS:528:DC%2BB3MXnvVOntbo%3D10.2147/CMAR.S279990 Dara S, Tumma P (2018) Feature extraction by using deep learning: a survey2018 second international conference on electronics, communication and aerospace technology (ICECA), pp 1795–1801 FontiVBelitserEFeature selection using lassoVU Amsterdam Res Paper Business Anal201730125 ParkHQinLGuerraPBayCPShinagareABDecoding incidental ovarian lesions: use of texture analysis and machine learning for characterization and detection of malignancyAbdom Radiol (NY)2021462376238310.1007/s00261-020-02668-332728871 1412_CR1 American College of Obstetricians and Gynecologists’ Committee on Practice Bulletins—Gynecology (1412_CR3) 2016; 128 A Lass (1412_CR8) 1999; 5 H Park (1412_CR28) 2021; 46 WH Parker (1412_CR9) 2005; 106 1412_CR7 YY Jeong (1412_CR4) 2000; 20 H Zhang (1412_CR23) 2019; 29 AM Newtson (1412_CR18) 2019; 154 S Wang (1412_CR24) 2019; 132 K Chaudhary (1412_CR16) 2018; 24 J Jian (1412_CR22) 2021; 31 VR Iyer (1412_CR5) 2010; 194 V Chiappa (1412_CR17) 2021; 5 K Kinkel (1412_CR6) 2005; 236 S Rizzo (1412_CR19) 2018; 28 H An (1412_CR27) 2021; 31 R Wang (1412_CR30) 2020 R Hand (1412_CR2) 1993; 269 X Xia (1412_CR25) 2020; 10 F Christiansen (1412_CR29) 2021; 57 XP Yu (1412_CR26) 2021; 13 R Sun (1412_CR14) 2018; 19 J Zhou (1412_CR11) 2020; 12 C Vununu (1412_CR34) 2019; 8 H Hricak (1412_CR36) 2000; 214 V Fonti (1412_CR35) 2017; 30 P Shrestha (1412_CR13) 2022 M Akazawa (1412_CR12) 2021; 120 XL Song (1412_CR20) 2021; 31 PV Foti (1412_CR37) 2016; 7 HA Vargas (1412_CR21) 2017; 27 1412_CR10 V Chiappa (1412_CR15) 2021; 161 J Masci (1412_CR31) 2011 1412_CR32 1412_CR33 |
| References_xml | – reference: NewtsonAMMattsonJNGoodheartMJPrediction of optimal surgical outcomes with radiologic images using deep learning artificial intelligenceGynecol Oncol201915415610.1016/j.ygyno.2019.04.366 – reference: ChristiansenFEpsteinELSmedbergEÅkerlundMSmithKEpsteinEUltrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessmentUltrasound Obstet Gynecol2021571551631:STN:280:DC%2BB3s3gtVWmsA%3D%3D10.1002/uog.23530331423597839489 – reference: RizzoSBottaFRaimondiSRadiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 monthsEur Radiol2018284849485910.1007/s00330-018-5389-z29737390 – reference: 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 – reference: Dara S, Tumma P (2018) Feature extraction by using deep learning: a survey2018 second international conference on electronics, communication and aerospace technology (ICECA), pp 1795–1801 – reference: 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 – reference: JianJYaLiPickhardtPJMR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancersEur Radiol20213140341010.1007/s00330-020-07091-232743768 – reference: ChaudharyKPoirionOBLuLGarmireLXDeep learning-based multi-omics integration robustly predicts survival in liver cancerClin Cancer Res201824124812591:CAS:528:DC%2BC1cXks1ymur0%3D10.1158/1078-0432.CCR-17-085328982688 – reference: AnHWangYWongEMFCT texture analysis in histological classification of epithelial ovarian carcinomaEur Radiol2021315050505810.1007/s00330-020-07565-333409777 – reference: YuXPWangLYuHYMDCT-based radiomics features for the differentiation of serous borderline ovarian tumors and serous malignant ovarian tumorsCancer Manage Res20211332933610.2147/CMAR.S284220 – reference: Siegel RL, Miller KD, Jemal A (2019) Cancer statistics. CA Cancer J Clinic 69: 7–34 – reference: Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 2261–2269 – reference: LassAThe fertility potential of women with a single ovaryHum Reprod Update199955465501:STN:280:DC%2BD3c%2FkslKitQ%3D%3D10.1093/humupd/5.5.54610582792 – reference: FotiPVAttinàGSpadolaSMR imaging of ovarian masses: classification and differential diagnosisInsights Imaging20167214110.1007/s13244-015-0455-426671276 – reference: ParkerWHBroderMSLiuZShoupeDFarquharCBerekJSOvarian conservation at the time of hysterectomy for benign diseaseObstet Gynecol200510621922610.1097/01.AOG.0000167394.38215.5616055568 – reference: KinkelKLuYMehdizadeAPelteMFHricakHIndeterminate ovarian mass at US: incremental value of second imaging test for characterization–meta-analysis and Bayesian analysisRadiology2005236859410.1148/radiol.236104161815955864 – reference: Bi WL, Hosny A, Schabath MB et al (2019) Artificial intelligence in cancer imaging: Clinical challenges and applications. CA: A Cancer J Clinic 69:127–157 – reference: JeongYYOutwaterEKKangHKImaging evaluation of ovarian massesRadiographics200020144514701:STN:280:DC%2BD3cvpvVajtQ%3D%3D10.1148/radiographics.20.5.g00se10144510992033 – reference: ZhouJZengZYLiLProgress of artificial intelligence in gynecological malignant tumorsCancer Manage Res20201212823128401:CAS:528:DC%2BB3MXnvVOntbo%3D10.2147/CMAR.S279990 – reference: AkazawaMHashimotoKArtificial intelligence in gynecologic cancers: current status and future challenges – a systematic reviewArtif Intell Med202112010.1016/j.artmed.2021.10216434629152 – reference: ShresthaPPoudyalBYadollahiSA systematic review on the use of artificial intelligence in gynecologic imaging - background, state of the art, and future directionsGynecol Oncol202210.1016/j.ygyno.2022.07.02435914978 – reference: ZhangHMaoYChenXMagnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary studyEur Radiol2019293358337110.1007/s00330-019-06124-930963272 – reference: WangRCaiYLeeIKEvaluation of a convolutional neural network for ovarian tumor differentiation based on magnetic resonance imagingEur Radiol202010.1007/s00330-020-07266-x332415148043900 – reference: IyerVRLeeSIMRI, CT, and PET/CT for ovarian cancer detection and adnexal lesion characterizationAJR Am J Roentgenol201019431132110.2214/AJR.09.352220093590 – reference: SunRLimkinEJVakalopoulouMA radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort studyLancet Oncol201819118011911:CAS:528:DC%2BC1cXhsFansrzL10.1016/S1470-2045(18)30413-330120041 – reference: ChiappaVInterlenghiMSalvatoreCUsing rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumorsGynecol Oncol20211618388441:STN:280:DC%2BB3sbit1Gjtw%3D%3D10.1016/j.ygyno.2021.04.00433867144 – reference: 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. Agency for healthcare research and quality (US), Rockville (MD) – reference: XiaXGongJHaoWComparison and fusion of deep learning and radiomics features of ground-glass nodules to predict the invasiveness risk of stage-I lung adenocarcinomas in CT scanFront Oncol20201041810.3389/fonc.2020.00418322966457136522 – reference: FontiVBelitserEFeature selection using lassoVU Amsterdam Res Paper Business Anal201730125 – reference: MasciJMeierUCireşanDSchmidhuberJHonkelaTDuchWGirolamiMKaskiSStacked convolutional auto-encoders for hierarchical feature extractionArtificial neural networks and machine learning – ICANN 20112011Berlin HeidelbergSpringer525910.1007/978-3-642-21735-7_7 – reference: HandRFremgenAChmielJSStaging procedures, clinical management, and survival outcome for ovarian carcinomaJAMA1993269111911221:STN:280:DyaK3s7msVeltw%3D%3D10.1001/jama.1993.035000900550328433466 – reference: VununuCLeeS-HKwonK-RA deep feature extraction method for HEp-2 cell image classificationElectronics201982010.3390/electronics8010020 – reference: ChiappaVInterlenghiMBoganiGA decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125Eur Radiol Exp202152810.1186/s41747-021-00226-0343084878310829 – reference: HricakHChenMCoakleyFVComplex adnexal masses: detection and characterization with MR imaging–multivariate analysisRadiology200021439461:STN:280:DC%2BD3c7gs12itw%3D%3D10.1148/radiology.214.1.r00ja393910644099 – reference: American College of Obstetricians and Gynecologists’ Committee on Practice Bulletins—GynecologyPractice bulletin no. 174: evaluation and management of adnexal massesObstet Gynecol20161285e210e22610.1097/AOG.0000000000001768 – reference: ParkHQinLGuerraPBayCPShinagareABDecoding incidental ovarian lesions: use of texture analysis and machine learning for characterization and detection of malignancyAbdom Radiol (NY)2021462376238310.1007/s00261-020-02668-332728871 – reference: 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 – year: 2020 ident: 1412_CR30 publication-title: Eur Radiol doi: 10.1007/s00330-020-07266-x – volume: 120 year: 2021 ident: 1412_CR12 publication-title: Artif Intell Med doi: 10.1016/j.artmed.2021.102164 – volume: 46 start-page: 2376 year: 2021 ident: 1412_CR28 publication-title: Abdom Radiol (NY) doi: 10.1007/s00261-020-02668-3 – volume: 154 start-page: 156 year: 2019 ident: 1412_CR18 publication-title: Gynecol Oncol doi: 10.1016/j.ygyno.2019.04.366 – volume: 24 start-page: 1248 year: 2018 ident: 1412_CR16 publication-title: Clin Cancer Res doi: 10.1158/1078-0432.CCR-17-0853 – volume: 12 start-page: 12823 year: 2020 ident: 1412_CR11 publication-title: Cancer Manage Res doi: 10.2147/CMAR.S279990 – ident: 1412_CR32 doi: 10.1109/CVPR.2017.243 – volume: 194 start-page: 311 year: 2010 ident: 1412_CR5 publication-title: AJR Am J Roentgenol doi: 10.2214/AJR.09.3522 – volume: 236 start-page: 85 year: 2005 ident: 1412_CR6 publication-title: Radiology doi: 10.1148/radiol.2361041618 – ident: 1412_CR7 – volume: 13 start-page: 329 year: 2021 ident: 1412_CR26 publication-title: Cancer Manage Res doi: 10.2147/CMAR.S284220 – volume: 5 start-page: 28 year: 2021 ident: 1412_CR17 publication-title: Eur Radiol Exp doi: 10.1186/s41747-021-00226-0 – volume: 31 start-page: 403 year: 2021 ident: 1412_CR22 publication-title: Eur Radiol doi: 10.1007/s00330-020-07091-2 – ident: 1412_CR1 doi: 10.3322/caac.21551 – year: 2022 ident: 1412_CR13 publication-title: Gynecol Oncol doi: 10.1016/j.ygyno.2022.07.024 – volume: 27 start-page: 3991 year: 2017 ident: 1412_CR21 publication-title: Eur Radiol doi: 10.1007/s00330-017-4779-y – volume: 31 start-page: 5050 year: 2021 ident: 1412_CR27 publication-title: Eur Radiol doi: 10.1007/s00330-020-07565-3 – volume: 28 start-page: 4849 year: 2018 ident: 1412_CR19 publication-title: Eur Radiol doi: 10.1007/s00330-018-5389-z – volume: 106 start-page: 219 year: 2005 ident: 1412_CR9 publication-title: Obstet Gynecol doi: 10.1097/01.AOG.0000167394.38215.56 – volume: 7 start-page: 21 year: 2016 ident: 1412_CR37 publication-title: Insights Imaging doi: 10.1007/s13244-015-0455-4 – volume: 19 start-page: 1180 year: 2018 ident: 1412_CR14 publication-title: Lancet Oncol doi: 10.1016/S1470-2045(18)30413-3 – volume: 30 start-page: 1 year: 2017 ident: 1412_CR35 publication-title: VU Amsterdam Res Paper Business Anal – volume: 29 start-page: 3358 year: 2019 ident: 1412_CR23 publication-title: Eur Radiol doi: 10.1007/s00330-019-06124-9 – volume: 8 start-page: 20 year: 2019 ident: 1412_CR34 publication-title: Electronics doi: 10.3390/electronics8010020 – volume: 31 start-page: 368 year: 2021 ident: 1412_CR20 publication-title: Eur Radiol doi: 10.1007/s00330-020-07112-0 – volume: 57 start-page: 155 year: 2021 ident: 1412_CR29 publication-title: Ultrasound Obstet Gynecol doi: 10.1002/uog.23530 – start-page: 52 volume-title: Artificial neural networks and machine learning – ICANN 2011 year: 2011 ident: 1412_CR31 doi: 10.1007/978-3-642-21735-7_7 – volume: 214 start-page: 39 year: 2000 ident: 1412_CR36 publication-title: Radiology doi: 10.1148/radiology.214.1.r00ja3939 – volume: 269 start-page: 1119 year: 1993 ident: 1412_CR2 publication-title: JAMA doi: 10.1001/jama.1993.03500090055032 – ident: 1412_CR10 doi: 10.3322/caac.21552 – volume: 132 start-page: 171 year: 2019 ident: 1412_CR24 publication-title: Radiother Oncol doi: 10.1016/j.radonc.2018.10.019 – volume: 20 start-page: 1445 year: 2000 ident: 1412_CR4 publication-title: Radiographics doi: 10.1148/radiographics.20.5.g00se101445 – ident: 1412_CR33 doi: 10.1109/ICECA.2018.8474912 – volume: 161 start-page: 838 year: 2021 ident: 1412_CR15 publication-title: Gynecol Oncol doi: 10.1016/j.ygyno.2021.04.004 – volume: 10 start-page: 418 year: 2020 ident: 1412_CR25 publication-title: Front Oncol doi: 10.3389/fonc.2020.00418 – volume: 128 start-page: e210 issue: 5 year: 2016 ident: 1412_CR3 publication-title: Obstet Gynecol doi: 10.1097/AOG.0000000000001768 – volume: 5 start-page: 546 year: 1999 ident: 1412_CR8 publication-title: Hum Reprod Update doi: 10.1093/humupd/5.5.546 |
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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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