387 related articles for article (PubMed ID: 35723716)
1. Differentiation of benign from malignant solid renal lesions with MRI-based radiomics and machine learning.
Massa'a RN; Stoeckl EM; Lubner MG; Smith D; Mao L; Shapiro DD; Abel EJ; Wentland AL
Abdom Radiol (NY); 2022 Aug; 47(8):2896-2904. PubMed ID: 35723716
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis.
Erdim C; Yardimci AH; Bektas CT; Kocak B; Koca SB; Demir H; Kilickesmez O
Acad Radiol; 2020 Oct; 27(10):1422-1429. PubMed ID: 32014404
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Characterization of solid renal neoplasms using MRI-based quantitative radiomics features.
Said D; Hectors SJ; Wilck E; Rosen A; Stocker D; Bane O; Beksaç AT; Lewis S; Badani K; Taouli B
Abdom Radiol (NY); 2020 Sep; 45(9):2840-2850. PubMed ID: 32333073
[TBL] [Abstract][Full Text] [Related]
6. Characterization of renal masses with MRI-based radiomics: assessment of inter-package and inter-observer reproducibility in a prospective pilot study.
Al-Mubarak H; Bane O; Gillingham N; Kyriakakos C; Abboud G; Cuevas J; Gonzalez J; Meilika K; Horowitz A; Huang HV; Daza J; Fauveau V; Badani K; Viswanath SE; Taouli B; Lewis S
Abdom Radiol (NY); 2024 Mar; ():. PubMed ID: 38467854
[TBL] [Abstract][Full Text] [Related]
7. Differentiating Benign From Malignant Cystic Renal Masses: A Feasibility Study of Computed Tomography Texture-Based Machine Learning Algorithms.
Miskin N; Qin L; Silverman SG; Shinagare AB
J Comput Assist Tomogr; 2023 May-Jun 01; 47(3):376-381. PubMed ID: 37184999
[TBL] [Abstract][Full Text] [Related]
8. Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists.
Sun XY; Feng QX; Xu X; Zhang J; Zhu FP; Yang YH; Zhang YD
AJR Am J Roentgenol; 2020 Jan; 214(1):W44-W54. PubMed ID: 31553660
[No Abstract] [Full Text] [Related]
9. Machine Learning-Based Magnetic Resonance Radiomics Analysis for Predicting Low- and High-Grade Clear Cell Renal Cell Carcinoma.
Sim KC; Han NY; Cho Y; Sung DJ; Park BJ; Kim MJ; Han YE
J Comput Assist Tomogr; 2023 Nov-Dec 01; 47(6):873-881. PubMed ID: 37948361
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma.
Nassiri N; Maas M; Cacciamani G; Varghese B; Hwang D; Lei X; Aron M; Desai M; Oberai AA; Cen SY; Gill IS; Duddalwar VA
Eur Urol Focus; 2022 Jul; 8(4):988-994. PubMed ID: 34538748
[TBL] [Abstract][Full Text] [Related]
12. Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.
Zabihollahy F; Schieda N; Krishna S; Ukwatta E
Eur Radiol; 2020 Sep; 30(9):5183-5190. PubMed ID: 32350661
[TBL] [Abstract][Full Text] [Related]
13. Classification of MR-Detected Additional Lesions in Patients With Breast Cancer Using a Combination of Radiomics Analysis and Machine Learning.
Lee HJ; Nguyen AT; Ki SY; Lee JE; Do LN; Park MH; Lee JS; Kim HJ; Park I; Lim HS
Front Oncol; 2021; 11():744460. PubMed ID: 34926256
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
Wang X; Wan Q; Chen H; Li Y; Li X
Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
[TBL] [Abstract][Full Text] [Related]
16. Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions.
Zhang Q; Peng Y; Liu W; Bai J; Zheng J; Yang X; Zhou L
J Magn Reson Imaging; 2020 Aug; 52(2):596-607. PubMed ID: 32061014
[TBL] [Abstract][Full Text] [Related]
17. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
Lee HS; Hong H; Jung DC; Park S; Kim J
Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
[TBL] [Abstract][Full Text] [Related]
18. Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment.
Hao W; Gong J; Wang S; Zhu H; Zhao B; Peng W
Front Oncol; 2020; 10():531476. PubMed ID: 33194589
[TBL] [Abstract][Full Text] [Related]
19. Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning.
Le BD; Heo SH; Chung HS; Park I
Int J Med Inform; 2024 Jul; 187():105467. PubMed ID: 38678674
[TBL] [Abstract][Full Text] [Related]
20. Application of BOLD
Deng Y; Pan L; Xing W; Zhou Z; Chen J
Zhong Nan Da Xue Xue Bao Yi Xue Ban; 2021 Sept 28; 46(9):1010-1017. PubMed ID: 34707012
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
