378 related articles for article (PubMed ID: 32725335)
1. Utilization of radiomics to predict long-term outcome of magnetic resonance-guided focused ultrasound ablation therapy in adenomyosis.
Li Z; Zhang J; Song Y; Yin X; Chen A; Tang N; Prince MR; Yang G; Wang H
Eur Radiol; 2021 Jan; 31(1):392-402. PubMed ID: 32725335
[TBL] [Abstract][Full Text] [Related]
2. Nonenhanced MRI-based radiomics model for preoperative prediction of nonperfused volume ratio for high-intensity focused ultrasound ablation of uterine leiomyomas.
Zheng Y; Chen L; Liu M; Wu J; Yu R; Lv F
Int J Hyperthermia; 2021; 38(1):1349-1358. PubMed ID: 34486913
[TBL] [Abstract][Full Text] [Related]
3. Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters.
Zhou Y; Zhang J; Li C; Chen J; Lv F; Deng Y; Chen S; Du Y; Li F
Biomed Eng Online; 2023 Dec; 22(1):123. PubMed ID: 38093245
[TBL] [Abstract][Full Text] [Related]
4. Prediction of Ablation Rate for High-Intensity Focused Ultrasound Therapy of Adenomyosis in MR Images Based on Multi-model Fusion.
Ying J; Jing X; Gao F; Cheng J; Fu L; Yang H
J Imaging Inform Med; 2024 Mar; ():. PubMed ID: 38441701
[TBL] [Abstract][Full Text] [Related]
5. A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI.
Wang T; Gong J; Li Q; Chu C; Shen W; Peng W; Gu Y; Li W
Eur Radiol; 2021 Aug; 31(8):6125-6135. PubMed ID: 33486606
[TBL] [Abstract][Full Text] [Related]
6. Prediction using T2-weighted magnetic resonance imaging-based radiomics of residual uterine myoma regrowth after high-intensity focused ultrasound ablation.
Zhou Y; Zhang J; Chen J; Yang C; Gong C; Li C; Li F
Ultrasound Obstet Gynecol; 2022 Nov; 60(5):681-692. PubMed ID: 36054291
[TBL] [Abstract][Full Text] [Related]
7. MR imaging of thymomas: a combined radiomics nomogram to predict histologic subtypes.
Xiao G; Hu YC; Ren JL; Qin P; Han JC; Qu XY; Rong WC; Yan WQ; Tian Q; Han Y; Wang WP; Wang SM; Ma J; Wang W; Cui GB
Eur Radiol; 2021 Jan; 31(1):447-457. PubMed ID: 32700020
[TBL] [Abstract][Full Text] [Related]
8. Magnetic Resonance Imaging-guided Focused Ultrasound Surgery in a Swine Adenomyosis Model.
Zhang Q; Chen Y; Lai M; Li Y; Li Q; Fu C; Yao Z; Zhang J
Acad Radiol; 2023 Sep; 30 Suppl 2():S220-S226. PubMed ID: 36624022
[TBL] [Abstract][Full Text] [Related]
9. Radiomics analysis from magnetic resonance imaging in predicting the grade of nonfunctioning pancreatic neuroendocrine tumors: a multicenter study.
Zhu HB; Zhu HT; Jiang L; Nie P; Hu J; Tang W; Zhang XY; Li XT; Yao Q; Sun YS
Eur Radiol; 2024 Jan; 34(1):90-102. PubMed ID: 37552258
[TBL] [Abstract][Full Text] [Related]
10. Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma.
Wang G; He L; Yuan C; Huang Y; Liu Z; Liang C
Eur J Radiol; 2018 Jan; 98():100-106. PubMed ID: 29279146
[TBL] [Abstract][Full Text] [Related]
11. Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images.
Jin X; Ai Y; Zhang J; Zhu H; Jin J; Teng Y; Chen B; Xie C
Eur Radiol; 2020 Jul; 30(7):4117-4124. PubMed ID: 32078013
[TBL] [Abstract][Full Text] [Related]
12. Effectiveness of Magnetic Resonance-guided Focused Ultrasound Surgery (MRgFUS) in the uterine adenomyosis treatment: technical approach and MRI evaluation.
Ferrari F; Arrigoni F; Miccoli A; Mascaretti S; Fascetti E; Mascaretti G; Barile A; Masciocchi C
Radiol Med; 2016 Feb; 121(2):153-61. PubMed ID: 26349572
[TBL] [Abstract][Full Text] [Related]
13. Prediction of Clinical Outcome for High-Intensity Focused Ultrasound Ablation of Uterine Leiomyomas Using Multiparametric MRI Radiomics-Based Machine Leaning Model.
Zheng Y; Chen L; Liu M; Wu J; Yu R; Lv F
Front Oncol; 2021; 11():618604. PubMed ID: 34567999
[TBL] [Abstract][Full Text] [Related]
14. A clinical-radiomics model incorporating T2-weighted and diffusion-weighted magnetic resonance images predicts the existence of lymphovascular invasion / perineural invasion in patients with colorectal cancer.
Zhang K; Ren Y; Xu S; Lu W; Xie S; Qu J; Wang X; Shen B; Pang P; Cai X; Sun J
Med Phys; 2021 Sep; 48(9):4872-4882. PubMed ID: 34042185
[TBL] [Abstract][Full Text] [Related]
15. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Cui Y; Yang X; Shi Z; Yang Z; Du X; Zhao Z; Cheng X
Eur Radiol; 2019 Mar; 29(3):1211-1220. PubMed ID: 30128616
[TBL] [Abstract][Full Text] [Related]
16. A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery.
Tan Y; Zhang ST; Wei JW; Dong D; Wang XC; Yang GQ; Tian J; Zhang H
Eur Radiol; 2019 Jul; 29(7):3325-3337. PubMed ID: 30972543
[TBL] [Abstract][Full Text] [Related]
17. Early results of magnetic resonance-guided focused ultrasound surgery of adenomyosis: analysis of 20 cases.
Fukunishi H; Funaki K; Sawada K; Yamaguchi K; Maeda T; Kaji Y
J Minim Invasive Gynecol; 2008; 15(5):571-9. PubMed ID: 18657480
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Diagnostic Performance of 2D and 3D T2WI-Based Radiomics Features With Machine Learning Algorithms to Distinguish Solid Solitary Pulmonary Lesion.
Wan Q; Zhou J; Xia X; Hu J; Wang P; Peng Y; Zhang T; Sun J; Song Y; Yang G; Li X
Front Oncol; 2021; 11():683587. PubMed ID: 34868905
[TBL] [Abstract][Full Text] [Related]
20. Prediction of High-Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods.
Liu J; Zeng P; Guo W; Wang C; Geng Y; Lang N; Yuan H
J Magn Reson Imaging; 2021 Oct; 54(4):1303-1311. PubMed ID: 33979466
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
