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Items: 1 to 20 of 104

1.

Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation.

Chaddad A, Kucharczyk MJ, Daniel P, Sabri S, Jean-Claude BJ, Niazi T, Abdulkarim B.

Front Oncol. 2019 May 21;9:374. doi: 10.3389/fonc.2019.00374. eCollection 2019. Review.

2.

Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability.

Qiu Q, Duan J, Duan Z, Meng X, Ma C, Zhu J, Lu J, Liu T, Yin Y.

Quant Imaging Med Surg. 2019 Mar;9(3):453-464. doi: 10.21037/qims.2019.03.02.

3.

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges.

Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, Sun K, Li L, Li B, Wang M, Tian J.

Theranostics. 2019 Feb 12;9(5):1303-1322. doi: 10.7150/thno.30309. eCollection 2019. Review.

4.

Radiomics: the process and the challenges.

Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ.

Magn Reson Imaging. 2012 Nov;30(9):1234-48. doi: 10.1016/j.mri.2012.06.010. Epub 2012 Aug 13. Review.

5.

Radiomics: the facts and the challenges of image analysis.

Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, Bellomi M.

Eur Radiol Exp. 2018 Nov 14;2(1):36. doi: 10.1186/s41747-018-0068-z. Review.

6.

Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

Altazi BA, Zhang GG, Fernandez DC, Montejo ME, Hunt D, Werner J, Biagioli MC, Moros EG.

J Appl Clin Med Phys. 2017 Nov;18(6):32-48. doi: 10.1002/acm2.12170. Epub 2017 Sep 11.

7.

Robust Radiomics feature quantification using semiautomatic volumetric segmentation.

Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, Mitra S, Shankar BU, Kikinis R, Haibe-Kains B, Lambin P, Aerts HJ.

PLoS One. 2014 Jul 15;9(7):e102107. doi: 10.1371/journal.pone.0102107. eCollection 2014.

8.

Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature.

Li Y, Liu X, Qian Z, Sun Z, Xu K, Wang K, Fan X, Zhang Z, Li S, Wang Y, Jiang T.

Eur Radiol. 2018 Jul;28(7):2960-2968. doi: 10.1007/s00330-017-5267-0. Epub 2018 Feb 5.

PMID:
29404769
9.

Repeatability and Reproducibility of Radiomic Features: A Systematic Review.

Traverso A, Wee L, Dekker A, Gillies R.

Int J Radiat Oncol Biol Phys. 2018 Nov 15;102(4):1143-1158. doi: 10.1016/j.ijrobp.2018.05.053. Epub 2018 Jun 5.

10.

Radiomics in Oncological PET/CT: a Methodological Overview.

Ha S, Choi H, Paeng JC, Cheon GJ.

Nucl Med Mol Imaging. 2019 Feb;53(1):14-29. doi: 10.1007/s13139-019-00571-4. Epub 2019 Jan 15. Review.

PMID:
30828395
11.

Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software.

Lee M, Woo B, Kuo MD, Jamshidi N, Kim JH.

Korean J Radiol. 2017 May-Jun;18(3):498-509. doi: 10.3348/kjr.2017.18.3.498. Epub 2017 Apr 3.

12.

Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging.

Prasanna P, Karnawat A, Ismail M, Madabhushi A, Tiwari P.

J Med Imaging (Bellingham). 2019 Apr;6(2):024005. doi: 10.1117/1.JMI.6.2.024005. Epub 2019 May 7.

PMID:
31093517
13.

A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models.

Zinn PO, Singh SK, Kotrotsou A, Hassan I, Thomas G, Luedi MM, Elakkad A, Elshafeey N, Idris T, Mosley J, Gumin J, Fuller GN, de Groot JF, Baladandayuthapani V, Sulman EP, Kumar AJ, Sawaya R, Lang FF, Piwnica-Worms D, Colen RR.

Clin Cancer Res. 2018 Dec 15;24(24):6288-6299. doi: 10.1158/1078-0432.CCR-17-3420. Epub 2018 Jul 27.

PMID:
30054278
14.

Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.

Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P.

Eur Radiol. 2017 Oct;27(10):4188-4197. doi: 10.1007/s00330-016-4637-3. Epub 2016 Oct 24. Erratum in: Eur Radiol. 2017 Jun 12;:.

15.

Radiomics in gliomas: A promising assistance 
for glioma clinical research.

Leng Y, Wang X, Liao W, Cao Y.

Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2018 Apr 28;43(4):354-359. doi: 10.11817/j.issn.1672-7347.2018.04.004. Review.

16.

Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI-radiomic features.

Tixier F, Um H, Young RJ, Veeraraghavan H.

Med Phys. 2019 May 27. doi: 10.1002/mp.13624. [Epub ahead of print]

PMID:
31131906
17.

Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy.

Yang F, Ford JC, Dogan N, Padgett KR, Breto AL, Abramowitz MC, Dal Pra A, Pollack A, Stoyanova R.

Transl Androl Urol. 2018 Jun;7(3):445-458. doi: 10.21037/tau.2018.06.05. Review.

18.

Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.

Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J, Mak R, Aerts HJ.

Front Oncol. 2016 Mar 30;6:71. doi: 10.3389/fonc.2016.00071. eCollection 2016.

19.

Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme.

Chen X, Fang M, Dong D, Liu L, Xu X, Wei X, Jiang X, Qin L, Liu Z.

Acad Radiol. 2019 Jan 16. pii: S1076-6332(19)30009-1. doi: 10.1016/j.acra.2018.12.016. [Epub ahead of print]

PMID:
30660472
20.

[Radiomics: the process and applications in tumor research].

Li Q, Ye ZX.

Zhonghua Zhong Liu Za Zhi. 2018 Nov 23;40(11):801-804. doi: 10.3760/cma.j.issn.0253-3766.2018.11.001. Chinese.

PMID:
30481928

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