Cited In for PMID: 23294985

Year	Number of Results
2012	1
2013	3
2014	8
2015	7
2016	9
2017	3
2018	7
2019	6
2020	3
2024	0

1

Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS.

Tiwari P, Kurhanewicz J, Madabhushi A. Tiwari P, et al. Med Image Anal. 2013 Feb;17(2):219-35. doi: 10.1016/j.media.2012.10.004. Epub 2012 Dec 13. Med Image Anal. 2013. PMID: 23294985 Free PMC article.

2

The role of radiomics in prostate cancer radiotherapy.

Delgadillo R, Ford JC, Abramowitz MC, Dal Pra A, Pollack A, Stoyanova R. Delgadillo R, et al. Strahlenther Onkol. 2020 Oct;196(10):900-912. doi: 10.1007/s00066-020-01679-9. Epub 2020 Aug 21. Strahlenther Onkol. 2020. PMID: 32821953 Free PMC article. Review.

3

Detection of Dominant Intra-prostatic Lesions in Patients With Prostate Cancer Using an Artificial Neural Network and MR Multi-modal Radiomics Analysis.

Bagher-Ebadian H, Janic B, Liu C, Pantelic M, Hearshen D, Elshaikh M, Movsas B, Chetty IJ, Wen N. Bagher-Ebadian H, et al. Front Oncol. 2019 Nov 26;9:1313. doi: 10.3389/fonc.2019.01313. eCollection 2019. Front Oncol. 2019. PMID: 31850209 Free PMC article.

4

Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer.

Merisaari H, Taimen P, Shiradkar R, Ettala O, Pesola M, Saunavaara J, Boström PJ, Madabhushi A, Aronen HJ, Jambor I. Merisaari H, et al. Magn Reson Med. 2020 Jun;83(6):2293-2309. doi: 10.1002/mrm.28058. Epub 2019 Nov 8. Magn Reson Med. 2020. PMID: 31703155 Free PMC article.

5

Semi-automatic classification of prostate cancer on multi-parametric MR imaging using a multi-channel 3D convolutional neural network.

Aldoj N, Lukas S, Dewey M, Penzkofer T. Aldoj N, et al. Eur Radiol. 2020 Feb;30(2):1243-1253. doi: 10.1007/s00330-019-06417-z. Epub 2019 Aug 29. Eur Radiol. 2020. PMID: 31468158

6

Patch-Based Label Fusion for Automatic Multi-Atlas-Based Prostate Segmentation in MR Images.

Yang X, Jani AB, Rossi PJ, Mao H, Curran WJ, Liu T. Yang X, et al. Proc SPIE Int Soc Opt Eng. 2016 Feb-Mar;9786:978621. doi: 10.1117/12.2216424. Epub 2016 Mar 18. Proc SPIE Int Soc Opt Eng. 2016. PMID: 31452561 Free PMC article.

7

Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization.

Toivonen J, Montoya Perez I, Movahedi P, Merisaari H, Pesola M, Taimen P, Boström PJ, Pohjankukka J, Kiviniemi A, Pahikkala T, Aronen HJ, Jambor I. Toivonen J, et al. PLoS One. 2019 Jul 8;14(7):e0217702. doi: 10.1371/journal.pone.0217702. eCollection 2019. PLoS One. 2019. PMID: 31283771 Free PMC article. Clinical Trial.

8

Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images.

Varghese B, Chen F, Hwang D, Palmer SL, De Castro Abreu AL, Ukimura O, Aron M, Aron M, Gill I, Duddalwar V, Pandey G. Varghese B, et al. Sci Rep. 2019 Feb 7;9(1):1570. doi: 10.1038/s41598-018-38381-x. Sci Rep. 2019. PMID: 30733585 Free PMC article.

9

Predicting Gleason Score of Prostate Cancer Patients Using Radiomic Analysis.

Chaddad A, Niazi T, Probst S, Bladou F, Anidjar M, Bahoric B. Chaddad A, et al. Front Oncol. 2018 Dec 18;8:630. doi: 10.3389/fonc.2018.00630. eCollection 2018. Front Oncol. 2018. PMID: 30619764 Free PMC article.

10

A Deep Learning-Based Approach for the Detection and Localization of Prostate Cancer in T2 Magnetic Resonance Images.

Alkadi R, Taher F, El-Baz A, Werghi N. Alkadi R, et al. J Digit Imaging. 2019 Oct;32(5):793-807. doi: 10.1007/s10278-018-0160-1. J Digit Imaging. 2019. PMID: 30506124 Free PMC article.

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