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Journal Abstract Search

1126 related items for PubMed ID: 30711405

  • 21. A radiomics method based on MR FS-T2WI sequence for diagnosing of autosomal dominant polycystic kidney disease progression.
    Cong L, Hua QQ, Huang ZQ, Ma QL, Wang XM, Huang CC, Xu JX, Ma T.
    Eur Rev Med Pharmacol Sci; 2021 Sep; 25(18):5769-5780. PubMed ID: 34604968
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  • 24. Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics models.
    Li Z, Ma X, Shen F, Lu H, Xia Y, Lu J.
    BMC Med Imaging; 2021 Feb 16; 21(1):30. PubMed ID: 33593304
    [Abstract] [Full Text] [Related]

  • 25. Soft Tissue Sarcomas: Preoperative Predictive Histopathological Grading Based on Radiomics of MRI.
    Zhang Y, Zhu Y, Shi X, Tao J, Cui J, Dai Y, Zheng M, Wang S.
    Acad Radiol; 2019 Sep 16; 26(9):1262-1268. PubMed ID: 30377057
    [Abstract] [Full Text] [Related]

  • 26. 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 16; 54(4):1303-1311. PubMed ID: 33979466
    [Abstract] [Full Text] [Related]

  • 27. Radiomics based on T2-weighted and diffusion-weighted MR imaging for preoperative prediction of tumor deposits in rectal cancer.
    Sun Z, Xia F, Lv W, Li J, Zou Y, Wu J.
    Am J Surg; 2024 Jun 16; 232():59-67. PubMed ID: 38272767
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  • 28. External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study.
    Wei Q, Chen Z, Tang Y, Chen W, Zhong L, Mao L, Hu S, Wu Y, Deng K, Yang W, Liu X.
    Eur Radiol; 2023 Mar 16; 33(3):1906-1917. PubMed ID: 36355199
    [Abstract] [Full Text] [Related]

  • 29. MRI T2WI-based radiomics combined with KRAS gene mutation constructed models for predicting liver metastasis in rectal cancer.
    Ma J, Nie X, Kong X, Xiao L, Liu H, Shi S, Wu Y, Li N, Hu L, Li X.
    BMC Med Imaging; 2024 Oct 04; 24(1):262. PubMed ID: 39367333
    [Abstract] [Full Text] [Related]

  • 30. The feasibility of MRI-based radiomics model in presurgical evaluation of tumor budding in locally advanced rectal cancer.
    Li Z, Chen F, Zhang S, Ma X, Xia Y, Shen F, Lu Y, Shao C.
    Abdom Radiol (NY); 2022 Jan 04; 47(1):56-65. PubMed ID: 34673995
    [Abstract] [Full Text] [Related]

  • 31. Biparametric magnetic resonance imaging-based radiomics features for prediction of lymphovascular invasion in rectal cancer.
    Tong P, Sun D, Chen G, Ni J, Li Y.
    BMC Cancer; 2023 Jan 18; 23(1):61. PubMed ID: 36650498
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  • 32. [Radiomics-based prediction of microsatellite instability in stage Ⅱ and Ⅲ rectal cancer patients based on T2WI MRI and diffusion-weighted imaging].
    Xiang S, Zheng LB, Zhu L, Gao Y, Wang DS, Liu SL, Zhang S, Wang TY, Lu Y.
    Zhonghua Wai Ke Za Zhi; 2023 Sep 01; 61(9):782-787. PubMed ID: 37491171
    [Abstract] [Full Text] [Related]

  • 33. 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 01; 31(8):3384-3396. PubMed ID: 38508934
    [Abstract] [Full Text] [Related]

  • 34. Radiomics-Based Pretherapeutic Prediction of Non-response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer.
    Zhou X, Yi Y, Liu Z, Cao W, Lai B, Sun K, Li L, Zhou Z, Feng Y, Tian J.
    Ann Surg Oncol; 2019 Jun 01; 26(6):1676-1684. PubMed ID: 30887373
    [Abstract] [Full Text] [Related]

  • 35. Comparative assessment of the capability of machine learning-based radiomic models for predicting omental metastasis in locally advanced gastric cancer.
    Wu A, Luo L, Zeng Q, Wu C, Shu X, Huang P, Wang Z, Hu T, Feng Z, Tu Y, Zhu Y, Cao Y, Li Z.
    Sci Rep; 2024 Jul 13; 14(1):16208. PubMed ID: 39003337
    [Abstract] [Full Text] [Related]

  • 36. Whole-liver enhanced CT radiomics analysis to predict metachronous liver metastases after rectal cancer surgery.
    Liang M, Ma X, Wang L, Li D, Wang S, Zhang H, Zhao X.
    Cancer Imaging; 2022 Sep 11; 22(1):50. PubMed ID: 36089623
    [Abstract] [Full Text] [Related]

  • 37. Diagnostic value of a radiomics model based on CT and MRI for prediction of lateral lymph node metastasis of rectal cancer.
    Yang H, Jiang P, Dong L, Li P, Sun Y, Zhu S.
    Updates Surg; 2023 Dec 11; 75(8):2225-2234. PubMed ID: 37556079
    [Abstract] [Full Text] [Related]

  • 38. Radiomics Based on T2-Weighted Imaging and Apparent Diffusion Coefficient Images for Preoperative Evaluation of Lymph Node Metastasis in Rectal Cancer Patients.
    Li C, Yin J.
    Front Oncol; 2021 Dec 11; 11():671354. PubMed ID: 34041033
    [Abstract] [Full Text] [Related]

  • 39. Association of Pathological Features and Multiparametric MRI-Based Radiomics With TP53-Mutated Prostate Cancer.
    Chen R, Zhou B, Liu W, Gan H, Liu X, Zhou L.
    J Magn Reson Imaging; 2024 Sep 11; 60(3):1134-1145. PubMed ID: 38153859
    [Abstract] [Full Text] [Related]

  • 40. Epithelial salivary gland tumors: Utility of radiomics analysis based on diffusion-weighted imaging for differentiation of benign from malignant tumors.
    Shao S, Mao N, Liu W, Cui J, Xue X, Cheng J, Zheng N, Wang B.
    J Xray Sci Technol; 2020 Sep 11; 28(4):799-808. PubMed ID: 32538891
    [Abstract] [Full Text] [Related]

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