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991 related items for PubMed ID: 35945526

  • 1. Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma.
    Xu A, Chu X, Zhang S, Zheng J, Shi D, Lv S, Li F, Weng X.
    BMC Cancer; 2022 Aug 10; 22(1):872. PubMed ID: 35945526
    [Abstract] [Full Text] [Related]

  • 2. Development of a CT radiomics nomogram for preoperative prediction of Ki-67 index in pancreatic ductal adenocarcinoma: a two-center retrospective study.
    Li Q, Song Z, Li X, Zhang D, Yu J, Li Z, Huang J, Su K, Liu Q, Zhang X, Tang Z.
    Eur Radiol; 2024 May 10; 34(5):2934-2943. PubMed ID: 37938382
    [Abstract] [Full Text] [Related]

  • 3. Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma.
    Wu J, Fang Q, Yao J, Ge L, Hu L, Wang Z, Jin G.
    Front Oncol; 2022 May 10; 12():979358. PubMed ID: 36276108
    [Abstract] [Full Text] [Related]

  • 4. Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma.
    Guo Y, Wu J, Wang Y, Jin Y.
    Diagnostics (Basel); 2022 Dec 12; 12(12):. PubMed ID: 36553137
    [Abstract] [Full Text] [Related]

  • 5. Radiomics Nomogram Based on Dual-Sequence MRI for Assessing Ki-67 Expression in Breast Cancer.
    Zhang L, Shen M, Zhang D, He X, Du Q, Liu N, Huang X.
    J Magn Reson Imaging; 2024 Sep 12; 60(3):1203-1212. PubMed ID: 38088478
    [Abstract] [Full Text] [Related]

  • 6. Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.
    Chen W, Lin G, Chen Y, Cheng F, Li X, Ding J, Zhong Y, Kong C, Chen M, Xia S, Lu C, Ji J.
    BMC Cancer; 2024 Apr 05; 24(1):418. PubMed ID: 38580939
    [Abstract] [Full Text] [Related]

  • 7. Multiparametric MRI-based radiomics nomogram for predicting the hormone receptor status of HER2-positive breast cancer.
    Sang L, Liu Z, Huang C, Xu J, Wang H.
    Clin Radiol; 2024 Jan 05; 79(1):60-66. PubMed ID: 37838543
    [Abstract] [Full Text] [Related]

  • 8. Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer.
    Zheng Z, Xu F, Gu Z, Yan Y, Xu T, Liu S, Yao X.
    Front Oncol; 2021 Jan 05; 11():619893. PubMed ID: 34055600
    [Abstract] [Full Text] [Related]

  • 9. Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma.
    Zhang J, Wang G, Ren J, Yang Z, Li D, Cui Y, Yang X.
    Eur Radiol; 2022 Jun 05; 32(6):4079-4089. PubMed ID: 35050415
    [Abstract] [Full Text] [Related]

  • 10. Nomogram Utilizing ABVS Radiomics and Clinical Factors for Predicting ≤ 3 Positive Axillary Lymph Nodes in HR+ /HER2- Breast Cancer with 1-2 Positive Sentinel Nodes.
    Hu B, Xu Y, Gong H, Tang L, Wang L, Li H.
    Acad Radiol; 2024 Jul 05; 31(7):2684-2694. PubMed ID: 38383259
    [Abstract] [Full Text] [Related]

  • 11. Preoperative Prediction of Perineural Invasion Status of Rectal Cancer Based on Radiomics Nomogram of Multiparametric Magnetic Resonance Imaging.
    Zhang Y, Peng J, Liu J, Ma Y, Shu Z.
    Front Oncol; 2022 Jul 05; 12():828904. PubMed ID: 35480114
    [Abstract] [Full Text] [Related]

  • 12. A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors.
    Xu X, Wang H, Du P, Zhang F, Li S, Zhang Z, Yuan J, Liang Z, Zhang X, Guo Y, Liu Y, Lu H.
    J Magn Reson Imaging; 2019 Dec 05; 50(6):1893-1904. PubMed ID: 30980695
    [Abstract] [Full Text] [Related]

  • 13. Prediction of lymphovascular invasion in invasive breast cancer based on clinical-MRI radiomics features.
    Zhang C, Zhou P, Li R, Li Z, Ouyang A.
    BMC Med Imaging; 2024 Oct 16; 24(1):277. PubMed ID: 39415127
    [Abstract] [Full Text] [Related]

  • 14. The use of mammography-based radiomics nomograms for the preoperative prediction of the histological grade of invasive ductal carcinoma.
    Rong XC, Kang YH, Shi GF, Ren JL, Liu YH, Li ZG, Yang G.
    J Cancer Res Clin Oncol; 2023 Oct 16; 149(13):11635-11645. PubMed ID: 37405478
    [Abstract] [Full Text] [Related]

  • 15. Development and Validation of a Computed Tomography-Based Radiomics Nomogram for the Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma.
    Mou Y, Han X, Li J, Yu P, Wang C, Song Z, Wang X, Zhang M, Zhang H, Mao N, Song X.
    Acad Radiol; 2024 May 16; 31(5):1805-1817. PubMed ID: 38071100
    [Abstract] [Full Text] [Related]

  • 16. Preoperative Prediction of Lymph Node Metastasis of Pancreatic Ductal Adenocarcinoma Based on a Radiomics Nomogram of Dual-Parametric MRI Imaging.
    Shi L, Wang L, Wu C, Wei Y, Zhang Y, Chen J.
    Front Oncol; 2022 May 16; 12():927077. PubMed ID: 35875061
    [Abstract] [Full Text] [Related]

  • 17. Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer.
    Zhang X, Yang Z, Cui W, Zheng C, Li H, Li Y, Lu L, Mao J, Zeng W, Yang X, Zheng J, Shen J.
    Eur Radiol; 2021 Aug 16; 31(8):5924-5939. PubMed ID: 33569620
    [Abstract] [Full Text] [Related]

  • 18. A pretreatment multiparametric MRI-based radiomics-clinical machine learning model for predicting radiation-induced temporal lobe injury in patients with nasopharyngeal carcinoma.
    Wang L, Qiu T, Zhou J, Zhu Y, Sun B, Yang G, Huang S, Wu L, He X.
    Head Neck; 2024 Sep 16; 46(9):2132-2144. PubMed ID: 38887926
    [Abstract] [Full Text] [Related]

  • 19. Multiphases DCE-MRI Radiomics Nomogram for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer.
    Ma Q, Lu X, Chen Q, Gong H, Lei J.
    Acad Radiol; 2024 Dec 16; 31(12):4743-4758. PubMed ID: 39107190
    [Abstract] [Full Text] [Related]

  • 20. A nomogram based on pretreatment CT radiomics features for predicting complete response to chemoradiotherapy in patients with esophageal squamous cell cancer.
    Luo HS, Huang SF, Xu HY, Li XY, Wu SX, Wu DH.
    Radiat Oncol; 2020 Oct 29; 15(1):249. PubMed ID: 33121507
    [Abstract] [Full Text] [Related]

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