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196 related items for PubMed ID: 33333688

  • 1. [The value of conventional magnetic resonance imaging based radiomic model in predicting the texture of pituitary macroadenoma].
    Chen JM, Wan Q, Zhu HY, Ge YQ, Wu LL, Zhai J, Ding ZM.
    Zhonghua Yi Xue Za Zhi; 2020 Dec 08; 100(45):3626-3631. PubMed ID: 33333688
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  • 5. Application Value of Magnetic Resonance Radiomics and Clinical Nomograms in Evaluating the Sensitivity of Neoadjuvant Chemotherapy for Nasopharyngeal Carcinoma.
    Hu C, Zheng D, Cao X, Pang P, Fang Y, Lu T, Chen Y.
    Front Oncol; 2021 Dec 08; 11():740776. PubMed ID: 34790570
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  • 6. Texture Analysis of High b-Value Diffusion-Weighted Imaging for Evaluating Consistency of Pituitary Macroadenomas.
    Su CQ, Zhang X, Pan T, Chen XT, Chen W, Duan SF, Ji J, Hu WX, Lu SS, Hong XN.
    J Magn Reson Imaging; 2020 May 08; 51(5):1507-1513. PubMed ID: 31769565
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  • 7. Radiomic Features on Multiparametric MRI for Preoperative Evaluation of Pituitary Macroadenomas Consistency: Preliminary Findings.
    Wan T, Wu C, Meng M, Liu T, Li C, Ma J, Qin Z.
    J Magn Reson Imaging; 2022 May 08; 55(5):1491-1503. PubMed ID: 34549842
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  • 11. Predicting response to somatostatin analogues in acromegaly: machine learning-based high-dimensional quantitative texture analysis on T2-weighted MRI.
    Kocak B, Durmaz ES, Kadioglu P, Polat Korkmaz O, Comunoglu N, Tanriover N, Kocer N, Islak C, Kizilkilic O.
    Eur Radiol; 2019 Jun 08; 29(6):2731-2739. PubMed ID: 30506213
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  • 12. Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma.
    Li Q, Liu YJ, Dong D, Bai X, Huang QB, Guo AT, Ye HY, Tian J, Wang HY.
    J Magn Reson Imaging; 2020 Nov 08; 52(5):1557-1566. PubMed ID: 32462799
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  • 17. Application of Enhanced T1WI of MRI Radiomics in Glioma Grading.
    Zhou H, Xu R, Mei H, Zhang L, Yu Q, Liu R, Fan B.
    Int J Clin Pract; 2022 Nov 08; 2022():3252574. PubMed ID: 35685548
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  • 18. Development and validation of an MRI-based radiomic signature for the preoperative prediction of treatment response in patients with invasive functional pituitary adenoma.
    Fan Y, Liu Z, Hou B, Li L, Liu X, Liu Z, Wang R, Lin Y, Feng F, Tian J, Feng M.
    Eur J Radiol; 2019 Dec 08; 121():108647. PubMed ID: 31561943
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  • 19. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma.
    Huang ZS, Xiao X, Li XD, Mo HZ, He WL, Deng YH, Lu LJ, Wu YK, Liu H.
    J Magn Reson Imaging; 2021 Nov 08; 54(5):1541-1550. PubMed ID: 34085336
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