160 related articles for article (PubMed ID: 31120779)
1. Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data.
He L; Li H; Dudley JA; Maloney TC; Brady SL; Somasundaram E; Trout AT; Dillman JR
AJR Am J Roentgenol; 2019 Sep; 213(3):592-601. PubMed ID: 31120779
[No Abstract] [Full Text] [Related]
2. DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults.
Li H; He L; Dudley JA; Maloney TC; Somasundaram E; Brady SL; Parikh NA; Dillman JR
Pediatr Radiol; 2021 Mar; 51(3):392-402. PubMed ID: 33048183
[TBL] [Abstract][Full Text] [Related]
3. Machine Learning Diagnosis of Small-Bowel Crohn Disease Using T2-Weighted MRI Radiomic and Clinical Data.
Liu RX; Li H; Towbin AJ; Ata NA; Smith EA; Tkach JA; Denson LA; He L; Dillman JR
AJR Am J Roentgenol; 2024 Jan; 222(1):e2329812. PubMed ID: 37530398
[No Abstract] [Full Text] [Related]
4. A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging.
Peng A; Dai H; Duan H; Chen Y; Huang J; Zhou L; Chen L
Eur J Radiol; 2020 Apr; 125():108892. PubMed ID: 32087466
[TBL] [Abstract][Full Text] [Related]
5. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
Yu Y; He Z; Ouyang J; Tan Y; Chen Y; Gu Y; Mao L; Ren W; Wang J; Lin L; Wu Z; Liu J; Ou Q; Hu Q; Li A; Chen K; Li C; Lu N; Li X; Su F; Liu Q; Xie C; Yao H
EBioMedicine; 2021 Jul; 69():103460. PubMed ID: 34233259
[TBL] [Abstract][Full Text] [Related]
6. Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer.
Abdollahi H; Mofid B; Shiri I; Razzaghdoust A; Saadipoor A; Mahdavi A; Galandooz HM; Mahdavi SR
Radiol Med; 2019 Jun; 124(6):555-567. PubMed ID: 30607868
[TBL] [Abstract][Full Text] [Related]
7. Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology.
Schawkat K; Ciritsis A; von Ulmenstein S; Honcharova-Biletska H; Jüngst C; Weber A; Gubler C; Mertens J; Reiner CS
Eur Radiol; 2020 Aug; 30(8):4675-4685. PubMed ID: 32270315
[TBL] [Abstract][Full Text] [Related]
8. Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance.
Gitto S; Bologna M; Corino VDA; Emili I; Albano D; Messina C; Armiraglio E; Parafioriti A; Luzzati A; Mainardi L; Sconfienza LM
Radiol Med; 2022 May; 127(5):518-525. PubMed ID: 35320464
[TBL] [Abstract][Full Text] [Related]
9. Hepatocellular carcinoma pathologic grade prediction using radiomics and machine learning models of gadoxetic acid-enhanced MRI: a two-center study.
Han YE; Cho Y; Kim MJ; Park BJ; Sung DJ; Han NY; Sim KC; Park YS; Park BN
Abdom Radiol (NY); 2023 Jan; 48(1):244-256. PubMed ID: 36131163
[TBL] [Abstract][Full Text] [Related]
10. Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers.
Cepeda S; Arrese I; García-García S; Velasco-Casares M; Escudero-Caro T; Zamora T; Sarabia R
World Neurosurg; 2021 Feb; 146():e1147-e1159. PubMed ID: 33259973
[TBL] [Abstract][Full Text] [Related]
11. Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.
Patel M; Zhan J; Natarajan K; Flintham R; Davies N; Sanghera P; Grist J; Duddalwar V; Peet A; Sawlani V
Clin Radiol; 2021 Aug; 76(8):628.e17-628.e27. PubMed ID: 33941364
[TBL] [Abstract][Full Text] [Related]
12. Differentiation of Pelvic Osteosarcoma and Ewing Sarcoma Using Radiomic Analysis Based on T2-Weighted Images and Contrast-Enhanced T1-Weighted Images.
Dai Y; Yin P; Mao N; Sun C; Wu J; Cheng G; Hong N
Biomed Res Int; 2020; 2020():9078603. PubMed ID: 32462033
[TBL] [Abstract][Full Text] [Related]
13. Improving Noninvasive Classification of Molecular Subtypes of Adult Gliomas With Diffusion-Weighted MR Imaging: An Externally Validated Machine Learning Algorithm.
Guo Y; Ma Z; Pei D; Duan W; Guo Y; Liu Z; Guan F; Wang Z; Xing A; Guo Z; Luo L; Wang W; Yu B; Zhou J; Ji Y; Yan D; Cheng J; Liu X; Yan J; Zhang Z
J Magn Reson Imaging; 2023 Oct; 58(4):1234-1242. PubMed ID: 36727433
[TBL] [Abstract][Full Text] [Related]
14. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma.
Huang ZS; Xiao X; Li XD; Mo HZ; He WL; Deng YH; Lu LJ; Wu YK; Liu H
J Magn Reson Imaging; 2021 Nov; 54(5):1541-1550. PubMed ID: 34085336
[TBL] [Abstract][Full Text] [Related]
15. Magnetic Resonance Elastography of the Liver: Qualitative and Quantitative Comparison of Gradient Echo and Spin Echo Echoplanar Imaging Sequences.
Wagner M; Besa C; Bou Ayache J; Yasar TK; Bane O; Fung M; Ehman RL; Taouli B
Invest Radiol; 2016 Sep; 51(9):575-81. PubMed ID: 26982699
[TBL] [Abstract][Full Text] [Related]
16. Magnetic resonance elastography SE-EPI vs GRE sequences at 3T in a pediatric population with liver disease.
Calle-Toro JS; Serai SD; Hartung EA; Goldberg DJ; Bolster BD; Darge K; Anupindi SA
Abdom Radiol (NY); 2019 Mar; 44(3):894-902. PubMed ID: 30600386
[TBL] [Abstract][Full Text] [Related]
17. Predicting BRAFV600E mutations in papillary thyroid carcinoma using six machine learning algorithms based on ultrasound elastography.
Agyekum EA; Wang YG; Xu FJ; Akortia D; Ren YZ; Chambers KH; Wang X; Taupa JO; Qian XQ
Sci Rep; 2023 Aug; 13(1):12604. PubMed ID: 37537230
[TBL] [Abstract][Full Text] [Related]
18. Feasibility and agreement of stiffness measurements using gradient-echo and spin-echo MR elastography sequences in unselected patients undergoing liver MRI.
Cunha GM; Glaser KJ; Bergman A; Luz RP; de Figueiredo EH; Lobo Lopes FPP
Br J Radiol; 2018 Jul; 91(1087):20180126. PubMed ID: 29718694
[TBL] [Abstract][Full Text] [Related]
19. Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images.
Song LL; Chen SJ; Chen W; Shi Z; Wang XD; Song LN; Chen DS
BMC Med Imaging; 2021 Mar; 21(1):54. PubMed ID: 33743615
[TBL] [Abstract][Full Text] [Related]
20. Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis.
Nakamoto T; Takahashi W; Haga A; Takahashi S; Kiryu S; Nawa K; Ohta T; Ozaki S; Nozawa Y; Tanaka S; Mukasa A; Nakagawa K
Sci Rep; 2019 Dec; 9(1):19411. PubMed ID: 31857632
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]