152 related articles for article (PubMed ID: 37768941)
1. Enhanced and unenhanced: Radiomics models for discriminating between benign and malignant cystic renal masses on CT images: A multi-center study.
Huang L; Feng W; Lin W; Chen J; Peng S; Du X; Li X; Liu T; Ye Y
PLoS One; 2023; 18(9):e0292110. PubMed ID: 37768941
[TBL] [Abstract][Full Text] [Related]
2. Cystic renal mass screening: machine-learning-based radiomics on unenhanced computed tomography.
Huang L; Ye Y; Chen J; Feng W; Peng S; Du X; Li X; Song Z; Liu T
Diagn Interv Radiol; 2024 Jan; ():. PubMed ID: 38164893
[TBL] [Abstract][Full Text] [Related]
3. Discriminating malignant and benign clinical T1 renal masses on computed tomography: A pragmatic radiomics and machine learning approach.
Uhlig J; Biggemann L; Nietert MM; Beißbarth T; Lotz J; Kim HS; Trojan L; Uhlig A
Medicine (Baltimore); 2020 Apr; 99(16):e19725. PubMed ID: 32311963
[TBL] [Abstract][Full Text] [Related]
4. Two nomograms based on radiomics models using triphasic CT for differentiation of adrenal lipid-poor benign lesions and metastases in a cancer population: an exploratory study.
Wang G; Kang B; Cui J; Deng Y; Zhao Y; Ji C; Wang X
Eur Radiol; 2023 Mar; 33(3):1873-1883. PubMed ID: 36264313
[TBL] [Abstract][Full Text] [Related]
5. Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT.
Coy H; Hsieh K; Wu W; Nagarajan MB; Young JR; Douek ML; Brown MS; Scalzo F; Raman SS
Abdom Radiol (NY); 2019 Jun; 44(6):2009-2020. PubMed ID: 30778739
[TBL] [Abstract][Full Text] [Related]
6. Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation.
Wentland AL; Yamashita R; Kino A; Pandit P; Shen L; Brooke Jeffrey R; Rubin D; Kamaya A
Abdom Radiol (NY); 2023 Feb; 48(2):642-648. PubMed ID: 36370180
[TBL] [Abstract][Full Text] [Related]
7. Differentiation of retroperitoneal paragangliomas and schwannomas based on computed tomography radiomics.
Cao Y; Wang Z; Ren J; Liu W; Da H; Yang X; Bao H
Sci Rep; 2023 Jun; 13(1):9253. PubMed ID: 37286581
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Building Radiomics Models Based on Triple-Phase CT Images Combining Clinical Features for Discriminating the Risk Rating in Gastrointestinal Stromal Tumors.
Shao M; Niu Z; He L; Fang Z; He J; Xie Z; Cheng G; Wang J
Front Oncol; 2021; 11():737302. PubMed ID: 34950578
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of Radiomics Models Based on Computed Tomography for Distinguishing Between Benign and Malignant Thyroid Nodules.
Kong D; Zhang J; Shan W; Duan S; Guo L
J Comput Assist Tomogr; 2022 Nov-Dec 01; 46(6):978-985. PubMed ID: 35759774
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Enhanced CT-based texture analysis and radiomics score for differentiation of pleomorphic adenoma, basal cell adenoma, and Warthin tumor of the parotid gland.
Chen F; Ge Y; Li S; Liu M; Wu J; Liu Y
Dentomaxillofac Radiol; 2023 Jan; 52(2):20220009. PubMed ID: 36367128
[TBL] [Abstract][Full Text] [Related]
13. A CT-based radiomics nomogram for differentiation of benign and malignant small renal masses (≤4 cm).
Feng S; Gong M; Zhou D; Yuan R; Kong J; Jiang F; Zhang L; Chen W; Li Y
Transl Oncol; 2023 Mar; 29():101627. PubMed ID: 36731307
[TBL] [Abstract][Full Text] [Related]
14. CT-based radiomics for differentiating peripherally located pulmonary sclerosing pneumocytoma from carcinoid.
Zhang Y; Yang X; Bi F; Wen L; Niu Y; Yang Y; Lin H; Yu X
Med Phys; 2024 Jun; 51(6):4219-4230. PubMed ID: 38507783
[TBL] [Abstract][Full Text] [Related]
15. Computed Tomography-Based Radiomics Model to Predict Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study.
Li J; Wu X; Mao N; Zheng G; Zhang H; Mou Y; Jia C; Mi J; Song X
Front Endocrinol (Lausanne); 2021; 12():741698. PubMed ID: 34745008
[TBL] [Abstract][Full Text] [Related]
16. Differentiation of pulmonary sclerosing pneumocytoma from solid malignant pulmonary nodules by radiomic analysis on multiphasic CT.
Ni XQ; Yin HK; Fan GH; Shi D; Xu L; Jin D
J Appl Clin Med Phys; 2021 Feb; 22(2):158-164. PubMed ID: 33369106
[TBL] [Abstract][Full Text] [Related]
17. Differentiation of Clear Cell and Non-clear-cell Renal Cell Carcinoma through CT-based Radiomics Models and Nomogram.
Cheng D; Abudikeranmu Y; Tuerdi B
Curr Med Imaging; 2023; 19(9):1005-1017. PubMed ID: 36411581
[TBL] [Abstract][Full Text] [Related]
18. CT-Based Radiomics Models for Differentiation of Benign and Malignant Thyroid Nodules: A Multicenter Development-and-Validation.
Lin S; Gao M; Yang Z; Yu R; Dai Z; Jiang C; Yao Y; Xu T; Chen J; Huang K; Lin D
AJR Am J Roentgenol; 2024 May; ():. PubMed ID: 38691415
[No Abstract] [Full Text] [Related]
19. Multi-Phase CT-Based Radiomics Nomogram for Discrimination Between Pancreatic Serous Cystic Neoplasm From Mucinous Cystic Neoplasm.
Gao J; Han F; Wang X; Duan S; Zhang J
Front Oncol; 2021; 11():699812. PubMed ID: 34926238
[TBL] [Abstract][Full Text] [Related]
20. Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors.
Yu Q; Wang A; Gu J; Li Q; Ning Y; Peng J; Lv F; Zhang X
Front Oncol; 2022; 12():913898. PubMed ID: 35847942
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]