311 related articles for article (PubMed ID: 38486342)
1. Artificial intelligence-based MRI radiomics and radiogenomics in glioma.
Fan H; Luo Y; Gu F; Tian B; Xiong Y; Wu G; Nie X; Yu J; Tong J; Liao X
Cancer Imaging; 2024 Mar; 24(1):36. PubMed ID: 38486342
[TBL] [Abstract][Full Text] [Related]
2. MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift.
Habib A; Jovanovich N; Hoppe M; Ak M; Mamindla P; R Colen R; Zinn PO
J Clin Med; 2021 Apr; 10(7):. PubMed ID: 33915813
[TBL] [Abstract][Full Text] [Related]
3. A Review of Radiomics and Deep Predictive Modeling in Glioma Characterization.
Gore S; Chougule T; Jagtap J; Saini J; Ingalhalikar M
Acad Radiol; 2021 Nov; 28(11):1599-1621. PubMed ID: 32660755
[TBL] [Abstract][Full Text] [Related]
4. Radiomics and radiogenomics in gliomas: a contemporary update.
Singh G; Manjila S; Sakla N; True A; Wardeh AH; Beig N; Vaysberg A; Matthews J; Prasanna P; Spektor V
Br J Cancer; 2021 Aug; 125(5):641-657. PubMed ID: 33958734
[TBL] [Abstract][Full Text] [Related]
5. Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.
Kocak B; Durmaz ES; Ates E; Sel I; Turgut Gunes S; Kaya OK; Zeynalova A; Kilickesmez O
Eur Radiol; 2020 Feb; 30(2):877-886. PubMed ID: 31691122
[TBL] [Abstract][Full Text] [Related]
6. Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.
Jang K; Russo C; Di Ieva A
Neuroradiology; 2020 Jul; 62(7):771-790. PubMed ID: 32249351
[TBL] [Abstract][Full Text] [Related]
7. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma.
Kim M; Jung SY; Park JE; Jo Y; Park SY; Nam SJ; Kim JH; Kim HS
Eur Radiol; 2020 Apr; 30(4):2142-2151. PubMed ID: 31828414
[TBL] [Abstract][Full Text] [Related]
8. Comprehensive quantitative radiogenomic evaluation reveals novel radiomic subtypes with distinct immune pattern in glioma.
Sun Y; Zhang Y; Gan J; Zhou H; Guo S; Wang X; Zhang C; Zheng W; Zhao X; Li X; Wang L; Ning S
Comput Biol Med; 2024 Jul; 177():108636. PubMed ID: 38810473
[TBL] [Abstract][Full Text] [Related]
9. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.
Buchlak QD; Esmaili N; Leveque JC; Bennett C; Farrokhi F; Piccardi M
J Clin Neurosci; 2021 Jul; 89():177-198. PubMed ID: 34119265
[TBL] [Abstract][Full Text] [Related]
10. Noninvasive Determination of
Bhandari AP; Liong R; Koppen J; Murthy SV; Lasocki A
AJNR Am J Neuroradiol; 2021 Jan; 42(1):94-101. PubMed ID: 33243896
[TBL] [Abstract][Full Text] [Related]
11. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas.
Li G; Li L; Li Y; Qian Z; Wu F; He Y; Jiang H; Li R; Wang D; Zhai Y; Wang Z; Jiang T; Zhang J; Zhang W
Brain; 2022 Apr; 145(3):1151-1161. PubMed ID: 35136934
[TBL] [Abstract][Full Text] [Related]
12. Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.
Park YW; Choi YS; Ahn SS; Chang JH; Kim SH; Lee SK
Korean J Radiol; 2019 Sep; 20(9):1381-1389. PubMed ID: 31464116
[TBL] [Abstract][Full Text] [Related]
13. MRI-based radiomics signature and clinical factor for predicting H3K27M mutation in pediatric high-grade gliomas located in the midline of the brain.
Wu C; Zheng H; Li J; Zhang Y; Duan S; Li Y; Wang D
Eur Radiol; 2022 Mar; 32(3):1813-1822. PubMed ID: 34655310
[TBL] [Abstract][Full Text] [Related]
14. Reverse Engineering Glioma Radiomics to Conventional Neuroimaging.
Kinoshita M; Kanemura Y; Narita Y; Kishima H
Neurol Med Chir (Tokyo); 2021 Sep; 61(9):505-514. PubMed ID: 34373429
[TBL] [Abstract][Full Text] [Related]
15. Machine Learning-Based Radiomics for Molecular Subtyping of Gliomas.
Lu CF; Hsu FT; Hsieh KL; Kao YJ; Cheng SJ; Hsu JB; Tsai PH; Chen RJ; Huang CC; Yen Y; Chen CY
Clin Cancer Res; 2018 Sep; 24(18):4429-4436. PubMed ID: 29789422
[No Abstract] [Full Text] [Related]
16. 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]
17. Imaging phenotypes from MRI for the prediction of glioma immune subtypes from RNA sequencing: A multicenter study.
Duan J; Zhang Z; Chen Y; Zhao Y; Sun Q; Wang W; Zheng H; Liang D; Cheng J; Yan J; Li ZC
Mol Oncol; 2023 Apr; 17(4):629-646. PubMed ID: 36688633
[TBL] [Abstract][Full Text] [Related]
18. Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI.
Le NQK; Hung TNK; Do DT; Lam LHT; Dang LH; Huynh TT
Comput Biol Med; 2021 May; 132():104320. PubMed ID: 33735760
[TBL] [Abstract][Full Text] [Related]
19. Glioma radiogenomics and artificial intelligence: road to precision cancer medicine.
Mahajan A; Sahu A; Ashtekar R; Kulkarni T; Shukla S; Agarwal U; Bhattacharya K
Clin Radiol; 2023 Feb; 78(2):137-149. PubMed ID: 36241568
[TBL] [Abstract][Full Text] [Related]
20. Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives.
Crombé A; Spinnato P; Italiano A; Brisse HJ; Feydy A; Fadli D; Kind M
Diagn Interv Imaging; 2023 Dec; 104(12):567-583. PubMed ID: 37802753
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]