153 related articles for article (PubMed ID: 37948373)
41. Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation.
Bae S; An C; Ahn SS; Kim H; Han K; Kim SW; Park JE; Kim HS; Lee SK
Sci Rep; 2020 Jul; 10(1):12110. PubMed ID: 32694637
[TBL] [Abstract][Full Text] [Related]
42. An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and
Zhang L; Yao R; Gao J; Tan D; Yang X; Wen M; Wang J; Xie X; Liao R; Tang Y; Chen S; Li Y
Front Oncol; 2021; 11():732704. PubMed ID: 34527594
[TBL] [Abstract][Full Text] [Related]
43. [Application of Automated Machine Learning Based on Radiomics Features of T2WI and RS-EPI DWI to Predict Preoperative T Staging of Rectal Cancer].
Wen DG; Hu SX; Li ZL; Deng XB; Tian C; Li X; Wang XR; Leng Q; Xia CC
Sichuan Da Xue Xue Bao Yi Xue Ban; 2021 Jul; 52(4):698-705. PubMed ID: 34323052
[TBL] [Abstract][Full Text] [Related]
44. 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]
45. Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models.
Kickingereder P; Burth S; Wick A; Götz M; Eidel O; Schlemmer HP; Maier-Hein KH; Wick W; Bendszus M; Radbruch A; Bonekamp D
Radiology; 2016 Sep; 280(3):880-9. PubMed ID: 27326665
[TBL] [Abstract][Full Text] [Related]
46. 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]
47. Radiogenomics to characterize the immune-related prognostic signature associated with biological functions in glioblastoma.
Liu D; Chen J; Ge H; Yan Z; Luo B; Hu X; Yang K; Liu Y; Liu H; Zhang W
Eur Radiol; 2023 Jan; 33(1):209-220. PubMed ID: 35881182
[TBL] [Abstract][Full Text] [Related]
48. Radiomics analysis of T1WI and T2WI magnetic resonance images to differentiate between IgG4-related ophthalmic disease and orbital MALT lymphoma.
Shao Y; Chen Y; Chen S; Wei R
BMC Ophthalmol; 2023 Jun; 23(1):288. PubMed ID: 37353736
[TBL] [Abstract][Full Text] [Related]
49. Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans.
Qureshi SA; Hussain L; Ibrar U; Alabdulkreem E; Nour MK; Alqahtani MS; Nafie FM; Mohamed A; Mohammed GP; Duong TQ
Sci Rep; 2023 Feb; 13(1):3291. PubMed ID: 36841898
[TBL] [Abstract][Full Text] [Related]
50. Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.
Inano R; Oishi N; Kunieda T; Arakawa Y; Yamao Y; Shibata S; Kikuchi T; Fukuyama H; Miyamoto S
Neuroimage Clin; 2014; 5():396-407. PubMed ID: 25180159
[TBL] [Abstract][Full Text] [Related]
51. Preoperative Assessment of MRI-Invisible Early-Stage Endometrial Cancer With MRI-Based Radiomics Analysis.
Jiang X; Song J; Zhang A; Cheng W; Duan S; Liu X; Chen T
J Magn Reson Imaging; 2023 Jul; 58(1):247-255. PubMed ID: 36259352
[TBL] [Abstract][Full Text] [Related]
52. Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis.
Liang M; Cai Z; Zhang H; Huang C; Meng Y; Zhao L; Li D; Ma X; Zhao X
Acad Radiol; 2019 Nov; 26(11):1495-1504. PubMed ID: 30711405
[TBL] [Abstract][Full Text] [Related]
53. MRI features predict p53 status in lower-grade gliomas via a machine-learning approach.
Li Y; Qian Z; Xu K; Wang K; Fan X; Li S; Jiang T; Liu X; Wang Y
Neuroimage Clin; 2018; 17():306-311. PubMed ID: 29527478
[TBL] [Abstract][Full Text] [Related]
54. Glioblastoma and primary central nervous system lymphoma: Preoperative differentiation by using MRI-based 3D texture analysis.
Xiao DD; Yan PF; Wang YX; Osman MS; Zhao HY
Clin Neurol Neurosurg; 2018 Oct; 173():84-90. PubMed ID: 30092408
[TBL] [Abstract][Full Text] [Related]
55. A radiomics nomogram based on multiparametric MRI might stratify glioblastoma patients according to survival.
Zhang X; Lu H; Tian Q; Feng N; Yin L; Xu X; Du P; Liu Y
Eur Radiol; 2019 Oct; 29(10):5528-5538. PubMed ID: 30847586
[TBL] [Abstract][Full Text] [Related]
56. Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.
Zheng F; Zhang L; Chen H; Zang Y; Chen X; Li Y
J Radiat Res; 2024 May; 65(3):350-359. PubMed ID: 38650477
[TBL] [Abstract][Full Text] [Related]
57. Radiomics-Based Machine Learning to Predict Recurrence in Glioma Patients Using Magnetic Resonance Imaging.
Hu G; Hu X; Yang K; Yu Y; Jiang Z; Liu Y; Liu D; Hu X; Xiao H; Zou Y; You Y; Liu H; Chen J
J Comput Assist Tomogr; 2023 Jan-Feb 01; 47(1):129-135. PubMed ID: 36194851
[TBL] [Abstract][Full Text] [Related]
58. Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature.
Li Y; Liu X; Qian Z; Sun Z; Xu K; Wang K; Fan X; Zhang Z; Li S; Wang Y; Jiang T
Eur Radiol; 2018 Jul; 28(7):2960-2968. PubMed ID: 29404769
[TBL] [Abstract][Full Text] [Related]
59. An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach.
Zhang L; Liu X; Xu X; Liu W; Jia Y; Chen W; Fu X; Li Q; Sun X; Zhang Y; Shu S; Zhang X; Xiang R; Chen H; Sun P; Geng D; Yu Z; Liu J; Wang J
Eur J Radiol; 2023 Jan; 158():110639. PubMed ID: 36463703
[TBL] [Abstract][Full Text] [Related]
60. Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI.
Kim Y; Cho HH; Kim ST; Park H; Nam D; Kong DS
Neuroradiology; 2018 Dec; 60(12):1297-1305. PubMed ID: 30232517
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
