Similar articles for PMID: 28982791

Year	Number of Results
2015	1
2016	3
2017	4
2018	15
2019	25
2020	35
2021	28
2022	7
2023	2
2024	1

1

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches.

Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S, Gillies R, Gevaert O, Gatenby R. Zhou M, et al. AJNR Am J Neuroradiol. 2018 Feb;39(2):208-216. doi: 10.3174/ajnr.A5391. Epub 2017 Oct 5. AJNR Am J Neuroradiol. 2018. PMID: 28982791 Free PMC article. Review.

2

Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.

Jang K, Russo C, Di Ieva A. Jang K, et al. Neuroradiology. 2020 Jul;62(7):771-790. doi: 10.1007/s00234-020-02403-1. Epub 2020 Apr 6. Neuroradiology. 2020. PMID: 32249351 Review.

3

How to develop a meaningful radiomic signature for clinical use in oncologic patients.

Papanikolaou N, Matos C, Koh DM. Papanikolaou N, et al. Cancer Imaging. 2020 May 1;20(1):33. doi: 10.1186/s40644-020-00311-4. Cancer Imaging. 2020. PMID: 32357923 Free PMC article. Review.

4

Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.

Yu Y, He Z, Ouyang J, Tan Y, Chen Y, Gu Y, Mao L, Ren W, Wang J, Lin L, Wu Z, Liu J, Ou Q, Hu Q, Li A, Chen K, Li C, Lu N, Li X, Su F, Liu Q, Xie C, Yao H. Yu Y, et al. EBioMedicine. 2021 Jul;69:103460. doi: 10.1016/j.ebiom.2021.103460. Epub 2021 Jul 4. EBioMedicine. 2021. PMID: 34233259 Free PMC article. Clinical Trial.

5

State of the Art: Machine Learning Applications in Glioma Imaging.

Lotan E, Jain R, Razavian N, Fatterpekar GM, Lui YW. Lotan E, et al. AJR Am J Roentgenol. 2019 Jan;212(1):26-37. doi: 10.2214/AJR.18.20218. Epub 2018 Oct 17. AJR Am J Roentgenol. 2019. PMID: 30332296 Review.

6

Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning.

Li M, Wang H, Shang Z, Yang Z, Zhang Y, Wan H. Li M, et al. J Clin Neurosci. 2020 Aug;78:175-180. doi: 10.1016/j.jocn.2020.04.080. Epub 2020 Apr 23. J Clin Neurosci. 2020. PMID: 32336636

7

Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.

Hashido T, Saito S, Ishida T. Hashido T, et al. J Comput Assist Tomogr. 2021 Jul-Aug 01;45(4):606-613. doi: 10.1097/RCT.0000000000001180. J Comput Assist Tomogr. 2021. PMID: 34270479

8

Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type.

Kniep HC, Madesta F, Schneider T, Hanning U, Schönfeld MH, Schön G, Fiehler J, Gauer T, Werner R, Gellissen S. Kniep HC, et al. Radiology. 2019 Feb;290(2):479-487. doi: 10.1148/radiol.2018180946. Epub 2018 Dec 11. Radiology. 2019. PMID: 30526358

9

Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach.

Cao M, Suo S, Zhang X, Wang X, Xu J, Yang W, Zhou Y. Cao M, et al. Biomed Res Int. 2021 Jan 22;2021:1235314. doi: 10.1155/2021/1235314. eCollection 2021. Biomed Res Int. 2021. PMID: 33553421 Free PMC article.

10

Machine learning-based radiomic evaluation of treatment response prediction in glioblastoma.

Patel M, Zhan J, Natarajan K, Flintham R, Davies N, Sanghera P, Grist J, Duddalwar V, Peet A, Sawlani V. Patel M, et al. Clin Radiol. 2021 Aug;76(8):628.e17-628.e27. doi: 10.1016/j.crad.2021.03.019. Epub 2021 May 1. Clin Radiol. 2021. PMID: 33941364

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