282 related articles for article (PubMed ID: 35583712)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. Voxel-based supervised machine learning of peripheral zone prostate cancer using noncontrast multiparametric MRI.
Gholizadeh N; Simpson J; Ramadan S; Denham J; Lau P; Siddique S; Dowling J; Welsh J; Chalup S; Greer PB
J Appl Clin Med Phys; 2020 Oct; 21(10):179-191. PubMed ID: 32770600
[TBL] [Abstract][Full Text] [Related]
8. Preoperative Prediction of Ki-67 Status in Breast Cancer with Multiparametric MRI Using Transfer Learning.
Liu W; Cheng Y; Liu Z; Liu C; Cattell R; Xie X; Wang Y; Yang X; Ye W; Liang C; Li J; Gao Y; Huang C; Liang C
Acad Radiol; 2021 Feb; 28(2):e44-e53. PubMed ID: 32278690
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters.
Malek M; Gity M; Alidoosti A; Oghabian Z; Rahimifar P; Seyed Ebrahimi SM; Tabibian E; Oghabian MA
Eur J Radiol; 2019 Jan; 110():203-211. PubMed ID: 30599861
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. Multiparametric MRI-based machine learning models for preoperatively predicting rectal adenoma with canceration.
Li P; Song G; Wu R; Li H; Zhang R; Zuo P; Li A
MAGMA; 2021 Oct; 34(5):707-716. PubMed ID: 33646452
[TBL] [Abstract][Full Text] [Related]
15. Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer.
Sun Z; Xia F; Lv W; Li J; Zou Y; Wu J
Am J Surg; 2024 Jun; 232():59-67. PubMed ID: 38272767
[TBL] [Abstract][Full Text] [Related]
16. [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]
17. Preoperative Extrapancreatic Extension Prediction in Patients with Pancreatic Cancer Using Multiparameter MRI and Machine Learning-Based Radiomics Model.
Xie N; Fan X; Xie H; Lu J; Yu L; Liu H; Wang H; Yin X; Li B
Acad Radiol; 2023 Jul; 30(7):1306-1316. PubMed ID: 36244870
[TBL] [Abstract][Full Text] [Related]
18. Preliminary utilization of radiomics in differentiating uterine sarcoma from atypical leiomyoma: Comparison on diagnostic efficacy of MRI features and radiomic features.
Xie H; Hu J; Zhang X; Ma S; Liu Y; Wang X
Eur J Radiol; 2019 Jun; 115():39-45. PubMed ID: 31084757
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Comparison of Dynamic Contrast-Enhanced MRI and Non-Mono-Exponential Model-Based Diffusion-Weighted Imaging for the Prediction of Prognostic Biomarkers and Molecular Subtypes of Breast Cancer Based on Radiomics.
Zhang L; Zhou XX; Liu L; Liu AY; Zhao WJ; Zhang HX; Zhu YM; Kuai ZX
J Magn Reson Imaging; 2023 Nov; 58(5):1590-1602. PubMed ID: 36661350
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
