158 related articles for article (PubMed ID: 38137489)
21. 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]
22. Glioma grading prediction using multiparametric magnetic resonance imaging-based radiomics combined with proton magnetic resonance spectroscopy and diffusion tensor imaging.
Lin K; Cidan W; Qi Y; Wang X
Med Phys; 2022 Jul; 49(7):4419-4429. PubMed ID: 35366379
[TBL] [Abstract][Full Text] [Related]
23. Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas.
Park CJ; Han K; Kim H; Ahn SS; Choi YS; Park YW; Chang JH; Kim SH; Jain R; Lee SK
Eur Radiol; 2020 Dec; 30(12):6464-6474. PubMed ID: 32740813
[TBL] [Abstract][Full Text] [Related]
24. An MRI-based radiomics nomogram in predicting histologic grade of non-muscle-invasive bladder cancer.
Li L; Zhang J; Zhe X; Chang H; Tang M; Lei X; Zhang L; Zhang X
Front Oncol; 2023; 13():1025972. PubMed ID: 37007156
[TBL] [Abstract][Full Text] [Related]
25. The development of a combined clinico-radiomics model for predicting post-operative recurrence in atypical meningiomas: a multicenter study.
Ren L; Chen J; Deng J; Qing X; Cheng H; Wang D; Ji J; Chen H; Juratli TA; Wakimoto H; Gong Y; Hua L
J Neurooncol; 2024 Jan; 166(1):59-71. PubMed ID: 38146046
[TBL] [Abstract][Full Text] [Related]
26. Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study.
Feng XL; Wang SZ; Chen HH; Huang YX; Xin YK; Zhang T; Cheng DL; Mao L; Li XL; Liu CX; Hu YC; Wang W; Cui GB; Nan HY
Lung Cancer; 2022 Apr; 166():150-160. PubMed ID: 35287067
[TBL] [Abstract][Full Text] [Related]
27. Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas.
Khanna O; Fathi Kazerooni A; Farrell CJ; Baldassari MP; Alexander TD; Karsy M; Greenberger BA; Garcia JA; Sako C; Evans JJ; Judy KD; Andrews DW; Flanders AE; Sharan AD; Dicker AP; Shi W; Davatzikos C
Neurosurgery; 2021 Oct; 89(5):928-936. PubMed ID: 34460921
[TBL] [Abstract][Full Text] [Related]
28. Accuracy of Radiomics-Based Feature Analysis on Multiparametric Magnetic Resonance Images for Noninvasive Meningioma Grading.
Laukamp KR; Shakirin G; Baeßler B; Thiele F; Zopfs D; Große Hokamp N; Timmer M; Kabbasch C; Perkuhn M; Borggrefe J
World Neurosurg; 2019 Dec; 132():e366-e390. PubMed ID: 31476455
[TBL] [Abstract][Full Text] [Related]
29. Preoperative Prediction of Meningioma Subtype by Constructing a Clinical-Radiomics Model Nomogram Based on Magnetic Resonance Imaging.
Han T; Liu X; Xu Z; Geng Y; Zhang B; Deng L; Jing M; Zhou J
World Neurosurg; 2024 Jan; 181():e203-e213. PubMed ID: 37813337
[TBL] [Abstract][Full Text] [Related]
30. Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters.
Wang Y; Wang L; Qin B; Hu X; Xiao W; Tong Z; Li S; Jing Y; Li L; Zhang Y
Front Neurosci; 2023; 17():1157858. PubMed ID: 37113160
[TBL] [Abstract][Full Text] [Related]
31. Machine learning models combining computed tomography semantic features and selected clinical variables for accurate prediction of the pathological grade of bladder cancer.
Deng Z; Dong W; Xiong S; Jin D; Zhou H; Zhang L; Xie L; Deng Y; Xu R; Fan B
Front Oncol; 2023; 13():1166245. PubMed ID: 37223680
[TBL] [Abstract][Full Text] [Related]
32. Predicting peritumoral edema development after gamma knife radiosurgery of meningiomas using machine learning methods: a multicenter study.
Li X; Lu Y; Liu L; Wang D; Zhao Y; Mei N; Geng D; Ma X; Zheng W; Duan S; Wu PY; Wen H; Tan Y; Sun X; Sun S; Li Z; Yu T; Yin B
Eur Radiol; 2023 Dec; 33(12):8912-8924. PubMed ID: 37498381
[TBL] [Abstract][Full Text] [Related]
33. A deep learning radiomics model for preoperative grading in meningioma.
Zhu Y; Man C; Gong L; Dong D; Yu X; Wang S; Fang M; Wang S; Fang X; Chen X; Tian J
Eur J Radiol; 2019 Jul; 116():128-134. PubMed ID: 31153553
[TBL] [Abstract][Full Text] [Related]
34. Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature.
Zhang L; Zhe X; Tang M; Zhang J; Ren J; Zhang X; Li L
Contrast Media Mol Imaging; 2021; 2021():7830909. PubMed ID: 35024015
[TBL] [Abstract][Full Text] [Related]
35. 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]
36. Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics.
Liu J; Wu Y; Jia W; Han M; Chen Y; Li J; Wu B; Yin S; Zhang X; Chen J; Yu P; Luo H; Tu J; Zhou F; Cheng X; Yi Y
Front Neurosci; 2023; 17():1110579. PubMed ID: 37214402
[TBL] [Abstract][Full Text] [Related]
37. A Clinical Semantic and Radiomics Nomogram for Predicting Brain Invasion in WHO Grade II Meningioma Based on Tumor and Tumor-to-Brain Interface Features.
Li N; Mo Y; Huang C; Han K; He M; Wang X; Wen J; Yang S; Wu H; Dong F; Sun F; Li Y; Yu Y; Zhang M; Guan X; Xu X
Front Oncol; 2021; 11():752158. PubMed ID: 34745982
[TBL] [Abstract][Full Text] [Related]
38. A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study.
Zhang J; Yao K; Liu P; Liu Z; Han T; Zhao Z; Cao Y; Zhang G; Zhang J; Tian J; Zhou J
EBioMedicine; 2020 Aug; 58():102933. PubMed ID: 32739863
[TBL] [Abstract][Full Text] [Related]
39. MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study.
Xie J; Yang Y; Jiang Z; Zhang K; Zhang X; Lin Y; Shen Y; Jia X; Liu H; Yang S; Jiang Y; Ma L
Front Physiol; 2023; 14():1281506. PubMed ID: 38235385
[No Abstract] [Full Text] [Related]
40. MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer.
Li H; Chen XL; Liu H; Lu T; Li ZL
Front Oncol; 2022; 12():1087882. PubMed ID: 36686763
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
