577 related articles for article (PubMed ID: 33486606)
1. 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]
2. Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas.
Dai M; Liu Y; Hu Y; Li G; Zhang J; Xiao Z; Lv F
Eur Radiol; 2022 Nov; 32(11):7988-7997. PubMed ID: 35583712
[TBL] [Abstract][Full Text] [Related]
3. Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features.
Nakagawa M; Nakaura T; Namimoto T; Iyama Y; Kidoh M; Hirata K; Nagayama Y; Yuki H; Oda S; Utsunomiya D; Yamashita Y
Acad Radiol; 2019 Oct; 26(10):1390-1399. PubMed ID: 30661978
[TBL] [Abstract][Full Text] [Related]
4. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
Wang X; Wan Q; Chen H; Li Y; Li X
Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
[TBL] [Abstract][Full Text] [Related]
5. Differentiating Benign from Malignant Renal Tumors Using T2- and Diffusion-Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists.
Xu Q; Zhu Q; Liu H; Chang L; Duan S; Dou W; Li S; Ye J
J Magn Reson Imaging; 2022 Apr; 55(4):1251-1259. PubMed ID: 34462986
[TBL] [Abstract][Full Text] [Related]
6. Applying multisequence MRI radiomics of the primary tumor and lymph node to predict HPV-related p16 status in patients with oropharyngeal squamous cell carcinoma.
Li Q; Xu T; Gong J; Xiang S; Shen C; Zhou X; Hu C; Wu B; Lu X
Quant Imaging Med Surg; 2023 Apr; 13(4):2234-2247. PubMed ID: 37064405
[TBL] [Abstract][Full Text] [Related]
7. MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas.
Tsuchiya M; Masui T; Terauchi K; Yamada T; Katyayama M; Ichikawa S; Noda Y; Goshima S
Eur Radiol; 2022 Jun; 32(6):4090-4100. PubMed ID: 35044510
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. 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]
11. 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]
12. Using rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumors.
Chiappa V; Interlenghi M; Salvatore C; Bertolina F; Bogani G; Ditto A; Martinelli F; Castiglioni I; Raspagliesi F
Gynecol Oncol; 2021 Jun; 161(3):838-844. PubMed ID: 33867144
[TBL] [Abstract][Full Text] [Related]
13. Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation.
Ji X; Zhang J; Shi W; He D; Bao J; Wei X; Huang Y; Liu Y; Chen JC; Gao X; Tang Y; Xia W
Phys Eng Sci Med; 2021 Sep; 44(3):745-754. PubMed ID: 34075559
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Pretreatment Multiparametric MRI-Based Radiomics Analysis for the Diagnosis of Breast Phyllodes Tumors.
Ma X; Gong J; Hu F; Tang W; Gu Y; Peng W
J Magn Reson Imaging; 2023 Feb; 57(2):633-645. PubMed ID: 35657093
[TBL] [Abstract][Full Text] [Related]
16. [Application of MRI-based Radiomics Models in the Assessment of Hepatic Metastasis of Rectal Cancer].
Hu SX; Yang K; Wang XR; Wen DG; Xia CC; Li X; Li ZL
Sichuan Da Xue Xue Bao Yi Xue Ban; 2021 Mar; 52(2):311-318. PubMed ID: 33829708
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. [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]
19. MRI-based radiomics signature for identification of invisible basal cisterns changes in tuberculous meningitis: a preliminary multicenter study.
Ma Q; Yi Y; Liu T; Wen X; Shan F; Feng F; Yan Q; Shen J; Yang G; Shi Y
Eur Radiol; 2022 Dec; 32(12):8659-8669. PubMed ID: 35748898
[TBL] [Abstract][Full Text] [Related]
20. Utility of magnetic resonance imaging for differentiating malignant mesenchymal tumors of the uterus from T2-weighted hyperintense leiomyomas.
Matsuura K; Inoue K; Hoshino E; Yasuda M; Hasegawa K; Okada Y; Baba Y; Kozawa E
Jpn J Radiol; 2022 Apr; 40(4):385-395. PubMed ID: 34750737
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]