247 related articles for article (PubMed ID: 32957949)
1. Three-dimensional CT texture analysis of anatomic liver segments can differentiate between low-grade and high-grade fibrosis.
Budai BK; Tóth A; Borsos P; Frank VG; Shariati S; Fejér B; Folhoffer A; Szalay F; Bérczi V; Kaposi PN
BMC Med Imaging; 2020 Sep; 20(1):108. PubMed ID: 32957949
[TBL] [Abstract][Full Text] [Related]
2. CT texture analysis of the liver for assessing hepatic fibrosis in patients with hepatitis C virus.
Lubner MG; Jones D; Kloke J; Said A; Pickhardt PJ
Br J Radiol; 2019 Jan; 92(1093):20180153. PubMed ID: 30182750
[TBL] [Abstract][Full Text] [Related]
3. Effective staging of fibrosis by the selected texture features of liver: Which one is better, CT or MR imaging?
Zhang X; Gao X; Liu BJ; Ma K; Yan W; Liling L; Yuhong H; Fujita H
Comput Med Imaging Graph; 2015 Dec; 46 Pt 2():227-36. PubMed ID: 26455963
[TBL] [Abstract][Full Text] [Related]
4. Feasibility of computed tomography texture analysis of hepatic fibrosis using dual-energy spectral detector computed tomography.
Choi B; Choi IY; Cha SH; Yeom SK; Chung HH; Lee SH; Cha J; Lee JH
Jpn J Radiol; 2020 Dec; 38(12):1179-1189. PubMed ID: 32666182
[TBL] [Abstract][Full Text] [Related]
5. Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade.
Bektas CT; Kocak B; Yardimci AH; Turkcanoglu MH; Yucetas U; Koca SB; Erdim C; Kilickesmez O
Eur Radiol; 2019 Mar; 29(3):1153-1163. PubMed ID: 30167812
[TBL] [Abstract][Full Text] [Related]
6. 2D and 3D texture analysis to predict lymphovascular invasion in lung adenocarcinoma.
Yang G; Nie P; Zhao L; Guo J; Xue W; Yan L; Cui J; Wang Z
Eur J Radiol; 2020 Aug; 129():109111. PubMed ID: 32559593
[TBL] [Abstract][Full Text] [Related]
7. Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis.
Naganawa S; Enooku K; Tateishi R; Akai H; Yasaka K; Shibahara J; Ushiku T; Abe O; Ohtomo K; Kiryu S
Eur Radiol; 2018 Jul; 28(7):3050-3058. PubMed ID: 29404772
[TBL] [Abstract][Full Text] [Related]
8. Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial frequency measurements--a work in progress.
Raman SP; Schroeder JL; Huang P; Chen Y; Coquia SF; Kawamoto S; Fishman EK
J Comput Assist Tomogr; 2015; 39(3):383-95. PubMed ID: 25700222
[TBL] [Abstract][Full Text] [Related]
9. Feasibility of ultrasound radiomics based models for classification of liver fibrosis due to Schistosoma japonicum infection.
Guo Z; Zhao M; Liu Z; Zheng J; Gong Y; Huang L; Xue J; Zhou X; Li S
PLoS Negl Trop Dis; 2024 Jun; 18(6):e0012235. PubMed ID: 38870200
[TBL] [Abstract][Full Text] [Related]
10. Texture analysis of the liver at MDCT for assessing hepatic fibrosis.
Lubner MG; Malecki K; Kloke J; Ganeshan B; Pickhardt PJ
Abdom Radiol (NY); 2017 Aug; 42(8):2069-2078. PubMed ID: 28314916
[TBL] [Abstract][Full Text] [Related]
11. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges.
Lubner MG; Smith AD; Sandrasegaran K; Sahani DV; Pickhardt PJ
Radiographics; 2017; 37(5):1483-1503. PubMed ID: 28898189
[TBL] [Abstract][Full Text] [Related]
12. Uric acid versus non-uric acid urinary stones: differentiation with single energy CT texture analysis.
Zhang GM; Sun H; Shi B; Xu M; Xue HD; Jin ZY
Clin Radiol; 2018 Sep; 73(9):792-799. PubMed ID: 29793721
[TBL] [Abstract][Full Text] [Related]
13. Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.
Nazari M; Shiri I; Hajianfar G; Oveisi N; Abdollahi H; Deevband MR; Oveisi M; Zaidi H
Radiol Med; 2020 Aug; 125(8):754-762. PubMed ID: 32193870
[TBL] [Abstract][Full Text] [Related]
14. Feasibility of using computed tomography texture analysis parameters as imaging biomarkers for predicting risk grade of gastrointestinal stromal tumors: comparison with visual inspection.
Choi IY; Yeom SK; Cha J; Cha SH; Lee SH; Chung HH; Lee CM; Choi J
Abdom Radiol (NY); 2019 Jul; 44(7):2346-2356. PubMed ID: 30923842
[TBL] [Abstract][Full Text] [Related]
15. Virtual monoenergetic imaging in rapid kVp-switching dual-energy CT (DECT) of the abdomen: impact on CT texture analysis.
Baliyan V; Kordbacheh H; Parameswaran B; Ganeshan B; Sahani D; Kambadakone A
Abdom Radiol (NY); 2018 Oct; 43(10):2693-2701. PubMed ID: 29541830
[TBL] [Abstract][Full Text] [Related]
16. Texture-based classification of different gastric tumors at contrast-enhanced CT.
Ba-Ssalamah A; Muin D; Schernthaner R; Kulinna-Cosentini C; Bastati N; Stift J; Gore R; Mayerhoefer ME
Eur J Radiol; 2013 Oct; 82(10):e537-43. PubMed ID: 23910996
[TBL] [Abstract][Full Text] [Related]
17. Hypervascular hepatic focal lesions on dynamic contrast-enhanced CT: preliminary data from arterial phase scans texture analysis for classification.
Song S; Li Z; Niu L; Zhou X; Wang G; Gao Y; Wang J; Liu F; Sui Q; Jiao L; Lu J
Clin Radiol; 2019 Aug; 74(8):653.e11-653.e18. PubMed ID: 31208725
[TBL] [Abstract][Full Text] [Related]
18. Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures.
Valentinitsch A; Trebeschi S; Kaesmacher J; Lorenz C; Löffler MT; Zimmer C; Baum T; Kirschke JS
Osteoporos Int; 2019 Jun; 30(6):1275-1285. PubMed ID: 30830261
[TBL] [Abstract][Full Text] [Related]
19. Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment.
Gatos I; Tsantis S; Spiliopoulos S; Karnabatidis D; Theotokas I; Zoumpoulis P; Loupas T; Hazle JD; Kagadis GC
Med Phys; 2019 May; 46(5):2298-2309. PubMed ID: 30929260
[TBL] [Abstract][Full Text] [Related]
20. CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach.
Taguchi N; Oda S; Yokota Y; Yamamura S; Imuta M; Tsuchigame T; Nagayama Y; Kidoh M; Nakaura T; Shiraishi S; Funama Y; Shinriki S; Miyamoto Y; Baba H; Yamashita Y
Eur J Radiol; 2019 Sep; 118():38-43. PubMed ID: 31439256
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
