141 related articles for article (PubMed ID: 38812784)
1. Ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features for automatic classification of ovarian masses according to O-RADS.
Liu L; Cai W; Tian H; Wu B; Zhang J; Wang T; Hao Y; Yue G
Front Oncol; 2024; 14():1377489. PubMed ID: 38812784
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.
Liu L; Cai W; Zhou C; Tian H; Wu B; Zhang J; Yue G; Hao Y
Front Med (Lausanne); 2024; 11():1362588. PubMed ID: 38523908
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Prediction model of adnexal masses with complex ultrasound morphology.
Wu Y; Miao K; Wang T; Xu C; Yao J; Dong X
Front Med (Lausanne); 2023; 10():1284495. PubMed ID: 38143444
[TBL] [Abstract][Full Text] [Related]
6. American college of radiology ovarian-adnexal reporting and data system ultrasound (O-RADS): Diagnostic performance and inter-reviewer agreement for ovarian masses in children.
Wang H; Wang L; An S; Ma Q; Tu Y; Shang N; Pan Y
Front Pediatr; 2023; 11():1091735. PubMed ID: 36969276
[TBL] [Abstract][Full Text] [Related]
7. Diagnosis of Ovarian Neoplasms Using Nomogram in Combination With Ultrasound Image-Based Radiomics Signature and Clinical Factors.
Qi L; Chen D; Li C; Li J; Wang J; Zhang C; Li X; Qiao G; Wu H; Zhang X; Ma W
Front Genet; 2021; 12():753948. PubMed ID: 34650603
[No Abstract] [Full Text] [Related]
8. Nomogram Combining Radiomics With the American College of Radiology Thyroid Imaging Reporting and Data System Can Improve Predictive Performance for Malignant Thyroid Nodules.
Huang X; Wu Z; Zhou A; Min X; Qi Q; Zhang C; Chen S; Xu P
Front Oncol; 2021; 11():737847. PubMed ID: 34722287
[TBL] [Abstract][Full Text] [Related]
9. Dual-modal radiomics nomogram based on contrast-enhanced ultrasound to improve differential diagnostic accuracy and reduce unnecessary biopsy rate in ACR TI-RADS 4-5 thyroid nodules.
Ren JY; Lv WZ; Wang L; Zhang W; Ma YY; Huang YZ; Peng YX; Lin JJ; Cui XW
Cancer Imaging; 2024 Jan; 24(1):17. PubMed ID: 38263209
[TBL] [Abstract][Full Text] [Related]
10. An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses.
Li C; Qiao G; Li J; Qi L; Wei X; Zhang T; Li X; Deng S; Wei X; Ma W
Front Oncol; 2022; 12():847805. PubMed ID: 35311142
[TBL] [Abstract][Full Text] [Related]
11. Development and Validation of a Nomogram Based on Multimodality Ultrasonography Images for Differentiating Malignant from Benign American College of Radiology Thyroid Imaging, Reporting and Data System (TI-RADS) 3-5 Thyroid Nodules.
Pang L; Yang X; Zhang P; Ding L; Yuan J; Liu H; Liu J; Gong X; Yu M; Luo W
Ultrasound Med Biol; 2024 Apr; 50(4):557-563. PubMed ID: 38262884
[TBL] [Abstract][Full Text] [Related]
12. O-RADS MRI scoring system: key points for correct application in inexperienced hands.
Cabedo L; Sebastià C; Munmany M; Fusté P; Gaba L; Saco A; Rodriguez A; Paño B; Nicolau C
Insights Imaging; 2024 Apr; 15(1):107. PubMed ID: 38609573
[TBL] [Abstract][Full Text] [Related]
13. O-RADS combined with contrast-enhanced ultrasound in risk stratification of adnexal masses.
Shi Y; Li H; Wu X; Li X; Yang M
J Ovarian Res; 2023 Aug; 16(1):153. PubMed ID: 37537697
[TBL] [Abstract][Full Text] [Related]
14. Predicting Breast Cancer in Breast Imaging Reporting and Data System (BI-RADS) Ultrasound Category 4 or 5 Lesions: A Nomogram Combining Radiomics and BI-RADS.
Luo WQ; Huang QX; Huang XW; Hu HT; Zeng FQ; Wang W
Sci Rep; 2019 Aug; 9(1):11921. PubMed ID: 31417138
[TBL] [Abstract][Full Text] [Related]
15. Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid ultrasound morphology.
Moro F; Vagni M; Tran HE; Bernardini F; Mascilini F; Ciccarone F; Nero C; Giannarelli D; Boldrini L; Fagotti A; Scambia G; Valentin L; Testa AC
Ultrasound Obstet Gynecol; 2024 May; ():. PubMed ID: 38748935
[TBL] [Abstract][Full Text] [Related]
16. A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma.
Lu J; Jiang N; Zhang Y; Li D
Front Oncol; 2023; 13():979437. PubMed ID: 36937433
[TBL] [Abstract][Full Text] [Related]
17. Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers.
Lo Gullo R; Daimiel I; Rossi Saccarelli C; Bitencourt A; Gibbs P; Fox MJ; Thakur SB; Martinez DF; Jochelson MS; Morris EA; Pinker K
Eur Radiol; 2020 Dec; 30(12):6721-6731. PubMed ID: 32594207
[TBL] [Abstract][Full Text] [Related]
18. Reliability of ultrasound ovarian-adnexal reporting and data system amongst less experienced readers before and after training.
Katlariwala P; Wilson MP; Pi Y; Chahal BS; Croutze R; Patel D; Patel V; Low G
World J Radiol; 2022 Sep; 14(9):319-328. PubMed ID: 36186517
[TBL] [Abstract][Full Text] [Related]
19. Performance of IOTA Simple Rules, Simple Rules risk assessment, ADNEX model and O-RADS in differentiating between benign and malignant adnexal lesions in North American women.
Hiett AK; Sonek JD; Guy M; Reid TJ
Ultrasound Obstet Gynecol; 2022 May; 59(5):668-676. PubMed ID: 34533862
[TBL] [Abstract][Full Text] [Related]
20. A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening.
Liu H; Chen Y; Zhang Y; Wang L; Luo R; Wu H; Wu C; Zhang H; Tan W; Yin H; Wang D
Eur Radiol; 2021 Aug; 31(8):5902-5912. PubMed ID: 33496829
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
