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Items: 1 to 20 of 92

1.

Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI.

Gaw N, Hawkins-Daarud A, Hu LS, Yoon H, Wang L, Xu Y, Jackson PR, Singleton KW, Baxter LC, Eschbacher J, Gonzales A, Nespodzany A, Smith K, Nakaji P, Mitchell JR, Wu T, Swanson KR, Li J.

Sci Rep. 2019 Jul 11;9(1):10063. doi: 10.1038/s41598-019-46296-4.

2.

Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning.

Hu LS, Yoon H, Eschbacher JM, Baxter LC, Dueck AC, Nespodzany A, Smith KA, Nakaji P, Xu Y, Wang L, Karis JP, Hawkins-Daarud AJ, Singleton KW, Jackson PR, Anderies BJ, Bendok BR, Zimmerman RS, Quarles C, Porter-Umphrey AB, Mrugala MM, Sharma A, Hoxworth JM, Sattur MG, Sanai N, Koulemberis PE, Krishna C, Mitchell JR, Wu T, Tran NL, Swanson KR, Li J.

AJNR Am J Neuroradiol. 2019 Mar;40(3):418-425. doi: 10.3174/ajnr.A5981. Epub 2019 Feb 28.

3.

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.

Hu LS, Ning S, Eschbacher JM, Gaw N, Dueck AC, Smith KA, Nakaji P, Plasencia J, Ranjbar S, Price SJ, Tran N, Loftus J, Jenkins R, O'Neill BP, Elmquist W, Baxter LC, Gao F, Frakes D, Karis JP, Zwart C, Swanson KR, Sarkaria J, Wu T, Mitchell JR, Li J.

PLoS One. 2015 Nov 24;10(11):e0141506. doi: 10.1371/journal.pone.0141506. eCollection 2015.

4.

Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

Nakagawa M, Nakaura T, Namimoto T, Kitajima M, Uetani H, Tateishi M, Oda S, Utsunomiya D, Makino K, Nakamura H, Mukasa A, Hirai T, Yamashita Y.

Eur J Radiol. 2018 Nov;108:147-154. doi: 10.1016/j.ejrad.2018.09.017. Epub 2018 Sep 14.

PMID:
30396648
5.

A self-tuned graph-based framework for localization and grading prostate cancer lesions: An initial evaluation based on multiparametric magnetic resonance imaging.

Chen W, Lin M, Gibson E, Bastian-Jordan M, Cool DW, Kassam Z, Liang H, Feng G, Ward AD, Chiu B.

Comput Biol Med. 2018 May 1;96:252-265. doi: 10.1016/j.compbiomed.2018.03.017. Epub 2018 Apr 3.

PMID:
29653354
6.

Combined unsupervised-supervised classification of multiparametric PET/MRI data: application to prostate cancer.

Gatidis S, Scharpf M, Martirosian P, Bezrukov I, Küstner T, Hennenlotter J, Kruck S, Kaufmann S, Schraml C, la Fougère C, Schwenzer NF, Schmidt H.

NMR Biomed. 2015 Jul;28(7):914-22. doi: 10.1002/nbm.3329. Epub 2015 May 26.

PMID:
26014883
7.

Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction.

Zhou M, Chaudhury B, Hall LO, Goldgof DB, Gillies RJ, Gatenby RA.

J Magn Reson Imaging. 2017 Jul;46(1):115-123. doi: 10.1002/jmri.25497. Epub 2016 Sep 28.

PMID:
27678245
8.

Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging.

Swinburne NC, Schefflein J, Sakai Y, Oermann EK, Titano JJ, Chen I, Tadayon S, Aggarwal A, Doshi A, Nael K.

Ann Transl Med. 2019 Jun;7(11):232. doi: 10.21037/atm.2018.08.05.

9.

Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

Macyszyn L, Akbari H, Pisapia JM, Da X, Attiah M, Pigrish V, Bi Y, Pal S, Davuluri RV, Roccograndi L, Dahmane N, Martinez-Lage M, Biros G, Wolf RL, Bilello M, O'Rourke DM, Davatzikos C.

Neuro Oncol. 2016 Mar;18(3):417-25. doi: 10.1093/neuonc/nov127. Epub 2015 Jul 16.

10.

Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features.

Chen Y, Li Z, Wu G, Yu J, Wang Y, Lv X, Ju X, Chen Z.

Int J Neurosci. 2018 Jul;128(7):608-618. doi: 10.1080/00207454.2017.1408613. Epub 2017 Dec 12.

PMID:
29183170
11.

Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach.

Rockne R, Rockhill JK, Mrugala M, Spence AM, Kalet I, Hendrickson K, Lai A, Cloughesy T, Alvord EC Jr, Swanson KR.

Phys Med Biol. 2010 Jun 21;55(12):3271-85. doi: 10.1088/0031-9155/55/12/001. Epub 2010 May 18.

12.

Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma.

Hu X, Wong KK, Young GS, Guo L, Wong ST.

J Magn Reson Imaging. 2011 Feb;33(2):296-305. doi: 10.1002/jmri.22432.

13.

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma.

Hu LS, Ning S, Eschbacher JM, Baxter LC, Gaw N, Ranjbar S, Plasencia J, Dueck AC, Peng S, Smith KA, Nakaji P, Karis JP, Quarles CC, Wu T, Loftus JC, Jenkins RB, Sicotte H, Kollmeyer TM, O'Neill BP, Elmquist W, Hoxworth JM, Frakes D, Sarkaria J, Swanson KR, Tran NL, Li J, Mitchell JR.

Neuro Oncol. 2017 Jan;19(1):128-137. doi: 10.1093/neuonc/now135. Epub 2016 Aug 8.

14.

Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine.

Perkuhn M, Stavrinou P, Thiele F, Shakirin G, Mohan M, Garmpis D, Kabbasch C, Borggrefe J.

Invest Radiol. 2018 Nov;53(11):647-654. doi: 10.1097/RLI.0000000000000484.

PMID:
29863600
15.

Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.

Wang J, Wu CJ, Bao ML, Zhang J, Wang XN, Zhang YD.

Eur Radiol. 2017 Oct;27(10):4082-4090. doi: 10.1007/s00330-017-4800-5. Epub 2017 Apr 3.

PMID:
28374077
16.

A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.

Tan L, Holland SK, Deshpande AK, Chen Y, Choo DI, Lu LJ.

Brain Behav. 2015 Oct 12;5(12):e00391. doi: 10.1002/brb3.391. eCollection 2015 Dec.

17.

Imaging biomarker analysis of advanced multiparametric MRI for glioma grading.

Vamvakas A, Williams SC, Theodorou K, Kapsalaki E, Fountas K, Kappas C, Vassiou K, Tsougos I.

Phys Med. 2019 Apr;60:188-198. doi: 10.1016/j.ejmp.2019.03.014. Epub 2019 Mar 23.

PMID:
30910431
18.

Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: A multicentre study.

Li ZC, Bai H, Sun Q, Li Q, Liu L, Zou Y, Chen Y, Liang C, Zheng H.

Eur Radiol. 2018 Sep;28(9):3640-3650. doi: 10.1007/s00330-017-5302-1. Epub 2018 Mar 21.

PMID:
29564594
19.

Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.

Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J; Alzheimer's Disease Neuroimaging Initiative.

Neuroimage. 2015 Jan 1;104:398-412. doi: 10.1016/j.neuroimage.2014.10.002. Epub 2014 Oct 12.

20.

Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme.

Fathi Kazerooni A, Mohseni M, Rezaei S, Bakhshandehpour G, Saligheh Rad H.

MAGMA. 2015 Feb;28(1):13-22. doi: 10.1007/s10334-014-0442-7. Epub 2014 Apr 2.

PMID:
24691860

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