These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
200 related articles for article (PubMed ID: 35412367)
1. Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment. Chen H; Yang BW; Qian L; Meng YS; Bai XH; Hong XW; He X; Jiang MJ; Yuan F; Du QW; Feng WW Radiology; 2022 Jul; 304(1):106-113. PubMed ID: 35412367 [TBL] [Abstract][Full Text] [Related]
2. Integrating Contrast-enhanced US to O-RADS US for Classification of Adnexal Lesions with Solid Components: Time-intensity Curve Analysis versus Visual Assessment. Wu M; Wang Y; Su M; Wang R; Sun X; Zhang R; Mu L; Xiao L; Wen H; Liu T; Meng X; Huang L; Zhang X Radiol Imaging Cancer; 2024 Nov; 6(6):e240024. PubMed ID: 39392388 [TBL] [Abstract][Full Text] [Related]
4. Developing a deep learning model for predicting ovarian cancer in Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US) Category 4 lesions: A multicenter study. Xie W; Lin W; Li P; Lai H; Wang Z; Liu P; Huang Y; Liu Y; Tang L; Lyu G J Cancer Res Clin Oncol; 2024 Jul; 150(7):346. PubMed ID: 38981916 [TBL] [Abstract][Full Text] [Related]
5. External Validation of O-RADS US Risk Stratification and Management System. Hack K; Gandhi N; Bouchard-Fortier G; Chawla TP; Ferguson SE; Li S; Kahn D; Tyrrell PN; Glanc P Radiology; 2022 Jul; 304(1):114-120. PubMed ID: 35438559 [TBL] [Abstract][Full Text] [Related]
6. Diagnostic Performance of Ultrasonography-Based Risk Models in Differentiating Between Benign and Malignant Ovarian Tumors in a US Cohort. Yoeli-Bik R; Longman RE; Wroblewski K; Weigert M; Abramowicz JS; Lengyel E JAMA Netw Open; 2023 Jul; 6(7):e2323289. PubMed ID: 37440228 [TBL] [Abstract][Full Text] [Related]
7. Contrast-enhanced US to Improve Diagnostic Performance of O-RADS US Risk Stratification System for Malignancy. Yuan K; Huang YJ; Mao MY; Li T; Wang SJ; He DN; Liu WF; Li MX; Zhu XM; Chen XY; Zhu YX Radiology; 2023 Aug; 308(2):e223003. PubMed ID: 37552073 [TBL] [Abstract][Full Text] [Related]
8. Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours. Du Y; Xiao Y; Guo W; Yao J; Lan T; Li S; Wen H; Zhu W; He G; Zheng H; Chen H Biomed Eng Online; 2024 Apr; 23(1):41. PubMed ID: 38594729 [TBL] [Abstract][Full Text] [Related]
9. Prediction of benign and malignant ovarian tumors using Resnet34 on ultrasound images. Miao K; Zhao N; Lv Q; He X; Xu M; Dong X; Li D; Shao X J Obstet Gynaecol Res; 2023 Dec; 49(12):2910-2917. PubMed ID: 37696522 [TBL] [Abstract][Full Text] [Related]
10. Validation of American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US): Analysis on 1054 adnexal masses. Cao L; Wei M; Liu Y; Fu J; Zhang H; Huang J; Pei X; Zhou J Gynecol Oncol; 2021 Jul; 162(1):107-112. PubMed ID: 33966893 [TBL] [Abstract][Full Text] [Related]
11. Efficacy of IOTA simple rules, O-RADS, and CA125 to distinguish benign and malignant adnexal masses. Xie WT; Wang YQ; Xiang ZS; Du ZS; Huang SX; Chen YJ; Tang LN J Ovarian Res; 2022 Jan; 15(1):15. PubMed ID: 35067220 [TBL] [Abstract][Full Text] [Related]
12. The Ovarian-Adnexal Reporting and Data System (O-RADS) US Score Effect on Surgical Resection Rate. Shen L; Sadowski EA; Gupta A; Maturen KE; Patel-Lippmann KK; Zafar HM; Kamaya A; Antil N; Guo Y; Barroilhet LM; Jha P Radiology; 2024 Oct; 313(1):e240044. PubMed ID: 39377674 [TBL] [Abstract][Full Text] [Related]
13. Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study. Du Y; Guo W; Xiao Y; Chen H; Yao J; Wu J BMC Med Imaging; 2024 Apr; 24(1):89. PubMed ID: 38622546 [TBL] [Abstract][Full Text] [Related]
14. External Validation of the Ovarian-Adnexal Reporting and Data System (O-RADS) Lexicon and the International Ovarian Tumor Analysis 2-Step Strategy to Stratify Ovarian Tumors Into O-RADS Risk Groups. Timmerman S; Valentin L; Ceusters J; Testa AC; Landolfo C; Sladkevicius P; Van Holsbeke C; Domali E; Fruscio R; Epstein E; Franchi D; Kudla MJ; Chiappa V; Alcazar JL; Leone FPG; Buonomo F; Coccia ME; Guerriero S; Deo N; Jokubkiene L; Kaijser J; Scambia G; Andreotti R; Timmerman D; Bourne T; Van Calster B; Froyman W JAMA Oncol; 2023 Feb; 9(2):225-233. PubMed ID: 36520422 [TBL] [Abstract][Full Text] [Related]
15. Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment. Christiansen F; Epstein EL; Smedberg E; Åkerlund M; Smith K; Epstein E Ultrasound Obstet Gynecol; 2021 Jan; 57(1):155-163. PubMed ID: 33142359 [TBL] [Abstract][Full Text] [Related]
16. Does Combing O-RADS US and CA-125 Improve Diagnostic Accuracy in Assessing Adnexal Malignancy Risk in Women With Different Menopausal Status? Wu M; Wang Q; Zhang M; Cao J; Chen Y; Zheng J; Luo L; Su M; Lin X; Kuang X; Zhang X J Ultrasound Med; 2023 Feb; 42(3):675-685. PubMed ID: 35880406 [TBL] [Abstract][Full Text] [Related]
18. Evaluation of American College of Radiology Ovarian-Adnexal Reporting and Data System ultrasound to predict malignancy risk in adnexal lesions. Li Y; Shao G; Wu M; Zhang F; Zhang Y; Shao C J Obstet Gynaecol Res; 2024 Feb; 50(2):225-232. PubMed ID: 37990446 [TBL] [Abstract][Full Text] [Related]
19. Nomogram based on the O-RADS for predicting the malignancy risk of adnexal masses with complex ultrasound morphology. Gong LP; Li XY; Wu YN; Dong S; Zhang S; Feng YN; Lv YE; Guo XJ; Peng YQ; Du XS; Tian JW; Sun CX; Sun LT J Ovarian Res; 2023 Mar; 16(1):57. PubMed ID: 36945000 [TBL] [Abstract][Full Text] [Related]
20. Adnexal masses difficult to classify as benign or malignant using subjective assessment of gray-scale and Doppler ultrasound findings: logistic regression models do not help. Valentin L; Ameye L; Savelli L; Fruscio R; Leone FP; Czekierdowski A; Lissoni AA; Fischerova D; Guerriero S; Van Holsbeke C; Van Huffel S; Timmerman D Ultrasound Obstet Gynecol; 2011 Oct; 38(4):456-65. PubMed ID: 21520475 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]