129 related articles for article (PubMed ID: 38061038)
1. Establishment of Noninvasive Prediction Models for the Diagnosis of Uterine Leiomyoma Subtypes.
Tamehisa T; Sato S; Sakai T; Maekawa R; Tanabe M; Ito K; Sugino N
Obstet Gynecol; 2024 Mar; 143(3):358-365. PubMed ID: 38061038
[TBL] [Abstract][Full Text] [Related]
2. MED12 mutations in uterine leiomyomas: prediction of volume reduction by gonadotropin-releasing hormone agonists.
Nagai K; Asano R; Sekiguchi F; Asai-Sato M; Miyagi Y; Miyagi E
Am J Obstet Gynecol; 2023 Feb; 228(2):207.e1-207.e9. PubMed ID: 36150519
[TBL] [Abstract][Full Text] [Related]
3. Development of a Model for the Prediction of Treatment Response of Uterine Leiomyomas after Uterine Artery Embolization.
Chung YJ; Kang SY; Chun HJ; Rha SE; Cho HH; Kim JH; Kim MR
Int J Med Sci; 2018; 15(14):1771-1777. PubMed ID: 30588202
[No Abstract] [Full Text] [Related]
4. 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]
5. Investigating the diagnostic value of quantitative parameters based on T2-weighted and contrast-enhanced MRI with psoas muscle and outer myometrium as internal references for differentiating uterine sarcomas from leiomyomas at 3T MRI.
Malek M; Rahmani M; Seyyed Ebrahimi SM; Tabibian E; Alidoosti A; Rahimifar P; Akhavan S; Gandomkar Z
Cancer Imaging; 2019 Apr; 19(1):20. PubMed ID: 30935419
[TBL] [Abstract][Full Text] [Related]
6. Uterine Artery Embolization of Uterine Leiomyomas: Predictive MRI Features of Volumetric Response.
Kurban LAS; Metwally H; Abdullah M; Kerban A; Oulhaj A; Alkoteesh JA
AJR Am J Roentgenol; 2021 Apr; 216(4):967-974. PubMed ID: 33594913
[No Abstract] [Full Text] [Related]
7. Explorative Investigation of Whole-Lesion Histogram MRI Metrics for Differentiating Uterine Leiomyomas and Leiomyosarcomas.
Gerges L; Popiolek D; Rosenkrantz AB
AJR Am J Roentgenol; 2018 May; 210(5):1172-1177. PubMed ID: 29547053
[TBL] [Abstract][Full Text] [Related]
8. Diagnostic Algorithm to Differentiate Benign Atypical Leiomyomas from Malignant Uterine Sarcomas with Diffusion-weighted MRI.
Abdel Wahab C; Jannot AS; Bonaffini PA; Bourillon C; Cornou C; Lefrère-Belda MA; Bats AS; Thomassin-Naggara I; Bellucci A; Reinhold C; Fournier LS
Radiology; 2020 Nov; 297(2):361-371. PubMed ID: 32930650
[TBL] [Abstract][Full Text] [Related]
9. Entropy of T2-weighted imaging combined with apparent diffusion coefficient in prediction of uterine leiomyoma volume response after uterine artery embolization.
Cao MQ; Suo ST; Zhang XB; Zhong YC; Zhuang ZG; Cheng JJ; Chi JC; Xu JR
Acad Radiol; 2014 Apr; 21(4):437-44. PubMed ID: 24594413
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Vitamin D as an effective treatment in human uterine leiomyomas independent of mediator complex subunit 12 mutation.
Corachán A; Trejo MG; Carbajo-García MC; Monleón J; Escrig J; Faus A; Pellicer A; Cervelló I; Ferrero H
Fertil Steril; 2021 Feb; 115(2):512-521. PubMed ID: 33036796
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Differentiating Uterine Sarcoma From Atypical Leiomyoma on Preoperative Magnetic Resonance Imaging Using Logistic Regression Classifier: Added Value of Diffusion-Weighted Imaging-Based Quantitative Parameters.
Kim H; Rha SE; Shin YR; Kim EH; Park SY; Lee SL; Lee A; Kim MR
Korean J Radiol; 2024 Jan; 25(1):43-54. PubMed ID: 38184768
[TBL] [Abstract][Full Text] [Related]
14. Analysis of
Lee M; Cheon K; Chae B; Hwang H; Kim HK; Chung YJ; Song JY; Cho HH; Kim JH; Kim MR
Int J Med Sci; 2018; 15(2):124-128. PubMed ID: 29333096
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. A multiparametric MRI-based machine learning to distinguish between uterine sarcoma and benign leiomyoma: comparison with
Nakagawa M; Nakaura T; Namimoto T; Iyama Y; Kidoh M; Hirata K; Nagayama Y; Oda S; Sakamoto F; Shiraishi S; Yamashita Y
Clin Radiol; 2019 Feb; 74(2):167.e1-167.e7. PubMed ID: 30471748
[TBL] [Abstract][Full Text] [Related]
17. Utility of Clinical Parameters and Multiparametric MRI as Predictive Factors for Differentiating Uterine Sarcoma From Atypical Leiomyoma.
Bi Q; Xiao Z; Lv F; Liu Y; Zou C; Shen Y
Acad Radiol; 2018 Aug; 25(8):993-1002. PubMed ID: 29422425
[TBL] [Abstract][Full Text] [Related]
18. Apparent diffusion coefficient of uterine leiomyoma as a predictor of the potential response to uterine artery embolization.
Lee MS; Kim MD; Jung DC; Lee M; Won JY; Park SI; Lee DY; Lee KH
J Vasc Interv Radiol; 2013 Sep; 24(9):1361-5. PubMed ID: 23891046
[TBL] [Abstract][Full Text] [Related]
19. Exomic landscape of MED12 mutation-negative and -positive uterine leiomyomas.
Mäkinen N; Vahteristo P; Bützow R; Sjöberg J; Aaltonen LA
Int J Cancer; 2014 Feb; 134(4):1008-12. PubMed ID: 23913526
[TBL] [Abstract][Full Text] [Related]
20. Magnetic Resonance Imaging Grading System for Preoperative Diagnosis of Leiomyomas and Uterine Smooth Muscle Tumors.
Suzuki Y; Wada S; Nakajima A; Fukushi Y; Hayashi M; Matsuda T; Asano R; Sakurai Y; Noguchi H; Shinohara T; Sato C; Fujino T
J Minim Invasive Gynecol; 2018; 25(3):507-513. PubMed ID: 29079462
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]