Editorial for "Texture Analysis of High b-value Diffusion-Weighted Imaging for Evaluating Consistency of Pituitary Macroadenomas".
Journal
Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
26
02
2020
accepted:
27
02
2020
pubmed:
11
3
2020
medline:
15
5
2021
entrez:
11
3
2020
Statut:
ppublish
Résumé
5 TECHNICAL EFFICACY: Stage 3 J. Magn. Reson. Imaging 2020;51:1514-1515.
Types de publication
Editorial
Langues
eng
Sous-ensembles de citation
IM
Pagination
1514-1515Informations de copyright
© 2020 International Society for Magnetic Resonance in Medicine.
Références
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Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology 2016;278(2):563-577.
Su C-Q, Zhang, X, Pan T, Chen, X-T, Chen W, Duan, S-F, Ji, J, Hu, W-X, Lu, S-S and Hong, X-N. Texture analysis of high b-value diffusion-weighted imaging for evaluating consistency of pituitary macroadenomas. J Magn Reson Imaging 2020. https://doi.org/10.1002/jmri.26941.
Yamamoto J, Kakeda S, Shimajiri S, et al. Tumor consistency of pituitary macroadenomas: Predictive analysis on the basis of imaging features with contrast-enhanced 3D FIESTA at 3T. Am J Neuroradiol 2014;35(2):297-303.
Rui W, Wu Y, Ma Z, et al. MR textural analysis on contrast enhanced 3D-SPACE images in assessment of consistency of pituitary macroadenoma. Eur J Radiol 2019;110:219-224.
Ugga L, Cuocolo R, Solari D, et al. Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning. Neuroradiology 2019;61:1365-1373.
Kocak B, Durmaz ES, Kadioglu P, et al. Predicting response to somatostatin analogues in acromegaly: Machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI. Eur Radiol 2019;29:2731-2739.
Wei L, Lin SA, Fan K, et al. Relationship between pituitary adenoma texture and collagen content revealed by comparative study of MRI and pathology analysis. Int J Clin Exp Med 2015;8(8):12898-12905.
Varghese BA, Cen SY, Hwang DH, et al. Texture analysis of imaging: What radiologists need to know. Am J Roentgenol 2019;212:520-528.
Buch K, Kuno H, Qureshi MM, Li B, Sakai O. Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model. J Appl Clin Med Phys 2018;19:253-264.