Similar articles for PMID: 30599861

Year	Number of Results
1989	1
1990	2
1992	2
1993	1
1994	2
1996	2
1997	3
1998	1
1999	3
2002	4
2003	2
2004	2
2005	3
2007	4
2008	5
2009	7
2010	3
2011	4
2012	6
2013	12
2014	8
2015	15
2016	16
2017	18
2018	25
2019	32
2020	22
2021	23
2022	12
2023	2
2024	0

1

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. Malek M, et al. Eur J Radiol. 2019 Jan;110:203-211. doi: 10.1016/j.ejrad.2018.11.009. Epub 2018 Nov 13. Eur J Radiol. 2019. PMID: 30599861

2

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. Malek M, et al. Cancer Imaging. 2019 Apr 1;19(1):20. doi: 10.1186/s40644-019-0206-8. Cancer Imaging. 2019. PMID: 30935419 Free PMC article.

3

A multiparametric MRI-based machine learning to distinguish between uterine sarcoma and benign leiomyoma: comparison with ¹⁸F-FDG PET/CT.

Nakagawa M, Nakaura T, Namimoto T, Iyama Y, Kidoh M, Hirata K, Nagayama Y, Oda S, Sakamoto F, Shiraishi S, Yamashita Y. Nakagawa M, et al. Clin Radiol. 2019 Feb;74(2):167.e1-167.e7. doi: 10.1016/j.crad.2018.10.010. Epub 2018 Nov 22. Clin Radiol. 2019. PMID: 30471748

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. Abdel Wahab C, et al. Radiology. 2020 Nov;297(2):361-371. doi: 10.1148/radiol.2020191658. Epub 2020 Sep 15. Radiology. 2020. PMID: 32930650

5

Combined use of T2-weighted and diffusion-weighted 3-T MR imaging for differentiating uterine sarcomas from benign leiomyomas.

Namimoto T, Yamashita Y, Awai K, Nakaura T, Yanaga Y, Hirai T, Saito T, Katabuchi H. Namimoto T, et al. Eur Radiol. 2009 Nov;19(11):2756-64. doi: 10.1007/s00330-009-1471-x. Epub 2009 Jun 6. Eur Radiol. 2009. PMID: 19504102

6

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. Nakagawa M, et al. Acad Radiol. 2019 Oct;26(10):1390-1399. doi: 10.1016/j.acra.2018.11.014. Epub 2019 Jan 17. Acad Radiol. 2019. PMID: 30661978

7

Diagnostic value of 3 T MR spectroscopy, diffusion-weighted MRI, and apparent diffusion coefficient value for distinguishing benign from malignant myometrial tumours.

Rahimifar P, Hashemi H, Malek M, Ebrahimi S, Tabibian E, Alidoosti A, Mousavi A, Yarandi F. Rahimifar P, et al. Clin Radiol. 2019 Jul;74(7):571.e9-571.e18. doi: 10.1016/j.crad.2019.03.011. Epub 2019 Apr 20. Clin Radiol. 2019. PMID: 31010584

8

A Diagnostic Algorithm using Multi-parametric MRI to Differentiate Benign from Malignant Myometrial Tumors: Machine-Learning Method.

Malek M, Tabibian E, Rahimi Dehgolan M, Rahmani M, Akhavan S, Sheikh Hasani S, Nili F, Hashemi H. Malek M, et al. Sci Rep. 2020 May 4;10(1):7404. doi: 10.1038/s41598-020-64285-w. Sci Rep. 2020. PMID: 32366933 Free PMC article.

9

Assessment of MR Imaging as a Tool to Differentiate between the Major Histological Types of Uterine Sarcomas.

Sumi A, Terasaki H, Sanada S, Uchida M, Tomioka Y, Kamura T, Yano H, Abe T. Sumi A, et al. Magn Reson Med Sci. 2015;14(4):295-304. doi: 10.2463/mrms.2014-0023. Epub 2015 Jun 23. Magn Reson Med Sci. 2015. PMID: 26104072 Free article.

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. Dai M, et al. Eur Radiol. 2022 Nov;32(11):7988-7997. doi: 10.1007/s00330-022-08783-7. Epub 2022 May 18. Eur Radiol. 2022. PMID: 35583712

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