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295 related items for PubMed ID: 34705087

  • 1. Combined model based on enhanced CT texture features in liver metastasis prediction of high-risk gastrointestinal stromal tumors.
    Zheng J, Xia Y, Xu A, Weng X, Wang X, Jiang H, Li Q, Li F.
    Abdom Radiol (NY); 2022 Jan; 47(1):85-93. PubMed ID: 34705087
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  • 2. [Performance of the Combined Model Based on Both Clinicopathological and CT Texture Features in Predicting Liver Metastasis of High-risk Gastrointestinal Stromal Tumors].
    Zheng J, Wang X, Xia Y, Jiang HT.
    Zhongguo Yi Xue Ke Xue Yuan Xue Bao; 2022 Feb; 44(1):53-59. PubMed ID: 35300765
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  • 4. Development and validation of a nomogram based on CT images and 3D texture analysis for preoperative prediction of the malignant potential in gastrointestinal stromal tumors.
    Ren C, Wang S, Zhang S.
    Cancer Imaging; 2020 Jan 13; 20(1):5. PubMed ID: 31931874
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  • 6. Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors.
    Chu H, Pang P, He J, Zhang D, Zhang M, Qiu Y, Li X, Lei P, Fan B, Xu R.
    Sci Rep; 2021 Jun 08; 11(1):12009. PubMed ID: 34103619
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  • 9. A novel CT-based radiomic nomogram for predicting the recurrence and metastasis of gastric stromal tumors.
    Ao W, Cheng G, Lin B, Yang R, Liu X, Zhou S, Wang W, Fang Z, Tian F, Yang G, Wang J.
    Am J Cancer Res; 2021 Jun 08; 11(6):3123-3134. PubMed ID: 34249449
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  • 10. Prediction of high Ki-67 proliferation index of gastrointestinal stromal tumors based on CT at non-contrast-enhanced and different contrast-enhanced phases.
    Xie Z, Suo S, Zhang W, Zhang Q, Dai Y, Song Y, Li X, Zhou Y.
    Eur Radiol; 2024 Apr 08; 34(4):2223-2232. PubMed ID: 37773213
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  • 11. CT texture analysis can be a potential tool to differentiate gastrointestinal stromal tumors without KIT exon 11 mutation.
    Xu F, Ma X, Wang Y, Tian Y, Tang W, Wang M, Wei R, Zhao X.
    Eur J Radiol; 2018 Oct 08; 107():90-97. PubMed ID: 30292279
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  • 12. Radiomics signatures based on contrast-enhanced CT for preoperative prediction of the Ki-67 proliferation state in gastrointestinal stromal tumors.
    Liu M, Bian J.
    Jpn J Radiol; 2023 Jul 08; 41(7):741-751. PubMed ID: 36652141
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  • 16. Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study.
    Liu B, Liu H, Zhang L, Song Y, Yang S, Zheng Z, Zhao J, Hou F, Zhang J.
    Diagn Interv Radiol; 2022 Jan 08; 28(1):29-38. PubMed ID: 35142612
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  • 17. Feasibility of using computed tomography texture analysis parameters as imaging biomarkers for predicting risk grade of gastrointestinal stromal tumors: comparison with visual inspection.
    Choi IY, Yeom SK, Cha J, Cha SH, Lee SH, Chung HH, Lee CM, Choi J.
    Abdom Radiol (NY); 2019 Jul 08; 44(7):2346-2356. PubMed ID: 30923842
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