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Journal Abstract Search

307 related items for PubMed ID: 38508934

  • 1. Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.
    Yimit Y, Yasin P, Tuersun A, Wang J, Wang X, Huang C, Abudoubari S, Chen X, Ibrahim I, Nijiati P, Wang Y, Zou X, Nijiati M.
    Acad Radiol; 2024 Aug; 31(8):3384-3396. PubMed ID: 38508934
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  • 2. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
    Wang X, Wan Q, Chen H, Li Y, Li X.
    Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
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  • 5. Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study.
    Li Z, Zhang J, Zhong Q, Feng Z, Shi Y, Xu L, Zhang R, Yu F, Lv B, Yang T, Huang C, Cui F, Chen F.
    Eur Radiol; 2023 Mar; 33(3):1835-1843. PubMed ID: 36282309
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  • 6. Multiparametric MRI-Based Radiomics Signature with Machine Learning for Preoperative Prediction of Prognosis Stratification in Pediatric Medulloblastoma.
    Luo Y, Zhuang Y, Zhang S, Wang J, Teng S, Zeng H.
    Acad Radiol; 2024 Apr; 31(4):1629-1642. PubMed ID: 37643930
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  • 9. Differentiation Between Ependymoma and Medulloblastoma in Children with Radiomics Approach.
    Dong J, Li L, Liang S, Zhao S, Zhang B, Meng Y, Zhang Y, Li S.
    Acad Radiol; 2021 Mar; 28(3):318-327. PubMed ID: 32222329
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  • 10. Radiomics Analysis of Multiparametric MRI for Prediction of Synchronous Lung Metastases in Osteosarcoma.
    Luo Z, Li J, Liao Y, Liu R, Shen X, Chen W.
    Front Oncol; 2022 Mar; 12():802234. PubMed ID: 35273911
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  • 14. Machine-learning approach to predict molecular subgroups of medulloblastoma using multiparametric MRI-based tumor radiomics.
    Saju AC, Chatterjee A, Sahu A, Gupta T, Krishnatry R, Mokal S, Sahay A, Epari S, Prasad M, Chinnaswamy G, Agarwal JP, Goda JS.
    Br J Radiol; 2022 Jun 01; 95(1134):20211359. PubMed ID: 35262407
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  • 15. Magnetic resonance imaging based on radiomics for differentiating T1-category nasopharyngeal carcinoma from nasopharyngeal lymphoid hyperplasia: a multicenter study.
    Cheng J, Su W, Wang Y, Zhan Y, Wang Y, Yan S, Yuan Y, Chen L, Wei Z, Zhang S, Gao X, Tang Z.
    Jpn J Radiol; 2024 Jul 01; 42(7):709-719. PubMed ID: 38409300
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  • 16. Radiomics analysis of T1WI and T2WI magnetic resonance images to differentiate between IgG4-related ophthalmic disease and orbital MALT lymphoma.
    Shao Y, Chen Y, Chen S, Wei R.
    BMC Ophthalmol; 2023 Jun 23; 23(1):288. PubMed ID: 37353736
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  • 17. Prediction of High-Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods.
    Liu J, Zeng P, Guo W, Wang C, Geng Y, Lang N, Yuan H.
    J Magn Reson Imaging; 2021 Oct 23; 54(4):1303-1311. PubMed ID: 33979466
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  • 18. Glioblastoma and Solitary Brain Metastasis: Differentiation by Integrating Demographic-MRI and Deep-Learning Radiomics Signatures.
    Zhang Y, Zhang H, Zhang H, Ouyang Y, Su R, Yang W, Huang B.
    J Magn Reson Imaging; 2024 Sep 23; 60(3):909-920. PubMed ID: 37955154
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  • 20. Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning.
    Wang Y, Han Q, Wen B, Yang B, Zhang C, Song Y, Zhang L, Xian J.
    Eur Radiol; 2024 Aug 30. PubMed ID: 39210161
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