BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

206 related articles for article (PubMed ID: 32879076)

  • 1. Radiomics features of ascending and descending nasopharyngeal carcinoma.
    Yao J; Yang P; Zhao L; Jin H; Xie X; Yang J; Lou F; Zhang R; Xu Z; Chen C
    Zhong Nan Da Xue Xue Bao Yi Xue Ban; 2020 Jul; 45(7):819-826. PubMed ID: 32879076
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma.
    Wang G; He L; Yuan C; Huang Y; Liu Z; Liang C
    Eur J Radiol; 2018 Jan; 98():100-106. PubMed ID: 29279146
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images.
    Du D; Feng H; Lv W; Ashrafinia S; Yuan Q; Wang Q; Yang W; Feng Q; Chen W; Rahmim A; Lu L
    Mol Imaging Biol; 2020 Jun; 22(3):730-738. PubMed ID: 31338709
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Prediction of Changes in Tumor Regression during Radiotherapy for Nasopharyngeal Carcinoma by Using the Computed Tomography-Based Radiomics.
    Yang Y; Wu J; Mai W; Li H
    Contrast Media Mol Imaging; 2022; 2022():3417480. PubMed ID: 36226269
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.
    Yin P; Mao N; Zhao C; Wu J; Sun C; Chen L; Hong N
    Eur Radiol; 2019 Apr; 29(4):1841-1847. PubMed ID: 30280245
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT.
    Lv W; Yuan Q; Wang Q; Ma J; Jiang J; Yang W; Feng Q; Chen W; Rahmim A; Lu L
    Eur Radiol; 2018 Aug; 28(8):3245-3254. PubMed ID: 29520429
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma.
    Zhang B; He X; Ouyang F; Gu D; Dong Y; Zhang L; Mo X; Huang W; Tian J; Zhang S
    Cancer Lett; 2017 Sep; 403():21-27. PubMed ID: 28610955
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A Clinical-Radiomics Nomogram Based on Computed Tomography for Predicting Risk of Local Recurrence After Radiotherapy in Nasopharyngeal Carcinoma.
    Zhu C; Huang H; Liu X; Chen H; Jiang H; Liao C; Pang Q; Dang J; Liu P; Lu H
    Front Oncol; 2021; 11():637687. PubMed ID: 33816279
    [No Abstract]   [Full Text] [Related]  

  • 9. Pretreatment Prediction of Adaptive Radiation Therapy Eligibility Using MRI-Based Radiomics for Advanced Nasopharyngeal Carcinoma Patients.
    Yu TT; Lam SK; To LH; Tse KY; Cheng NY; Fan YN; Lo CL; Or KW; Chan ML; Hui KC; Chan FC; Hui WM; Ngai LK; Lee FK; Au KH; Yip CW; Zhang Y; Cai J
    Front Oncol; 2019; 9():1050. PubMed ID: 31681588
    [No Abstract]   [Full Text] [Related]  

  • 10. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics.
    Mao B; Ma J; Duan S; Xia Y; Tao Y; Zhang L
    Eur Radiol; 2021 Jul; 31(7):4576-4586. PubMed ID: 33447862
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Evaluating the HER-2 status of breast cancer using mammography radiomics features.
    Zhou J; Tan H; Bai Y; Li J; Lu Q; Chen R; Zhang M; Feng Q; Wang M
    Eur J Radiol; 2019 Dec; 121():108718. PubMed ID: 31711023
    [TBL] [Abstract][Full Text] [Related]  

  • 12. [Value of the application of enhanced CT radiomics and machine learning in preoperative prediction of microvascular invasion in hepatocellular carcinoma].
    Yu YX; Hu CH; Wang XM; Fan YF; Hu MJ; Shi C; Hu S; Zhu M; Zhang Y
    Zhonghua Yi Xue Za Zhi; 2021 May; 101(17):1239-1245. PubMed ID: 34865392
    [No Abstract]   [Full Text] [Related]  

  • 13. Cervical spine osteoradionecrosis or bone metastasis after radiotherapy for nasopharyngeal carcinoma? The MRI-based radiomics for characterization.
    Zhong X; Li L; Jiang H; Yin J; Lu B; Han W; Li J; Zhang J
    BMC Med Imaging; 2020 Sep; 20(1):104. PubMed ID: 32873238
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Differentiating nontuberculous mycobacterium pulmonary disease from pulmonary tuberculosis through the analysis of the cavity features in CT images using radiomics.
    Yan Q; Wang W; Zhao W; Zuo L; Wang D; Chai X; Cui J
    BMC Pulm Med; 2022 Jan; 22(1):4. PubMed ID: 34991543
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on precontrast magnetic resonance images.
    Wu J; Liu A; Cui J; Chen A; Song Q; Xie L
    BMC Med Imaging; 2019 Mar; 19(1):23. PubMed ID: 30866850
    [TBL] [Abstract][Full Text] [Related]  

  • 16. The Usefulness of Pretreatment MR-Based Radiomics on Early Response of Neoadjuvant Chemotherapy in Patients With Locally Advanced Nasopharyngeal Carcinoma.
    Yongfeng P; Chuner J; Lei W; Fengqin Y; Zhimin Y; Zhenfu F; Haitao J; Yangming J; Fangzheng W
    Oncol Res; 2021 Mar; 28(6):605-613. PubMed ID: 33523792
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in
    Zhang Y; Cheng C; Liu Z; Wang L; Pan G; Sun G; Chang Y; Zuo C; Yang X
    Med Phys; 2019 Oct; 46(10):4520-4530. PubMed ID: 31348535
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Radiomics-based intracranial thrombus features on preoperative noncontrast CT predicts successful recanalization of mechanical thrombectomy in acute ischemic stroke.
    Xiong X; Wang J; Ke J; Hong R; Jiang S; Ye J; Hu C
    Quant Imaging Med Surg; 2023 Feb; 13(2):682-694. PubMed ID: 36819277
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer.
    Gu Q; Feng Z; Liang Q; Li M; Deng J; Ma M; Wang W; Liu J; Liu P; Rong P
    Eur J Radiol; 2019 Sep; 118():32-37. PubMed ID: 31439255
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine Learning-Based Analysis of Magnetic Resonance Radiomics for the Classification of Gliosarcoma and Glioblastoma.
    Qian Z; Zhang L; Hu J; Chen S; Chen H; Shen H; Zheng F; Zang Y; Chen X
    Front Oncol; 2021; 11():699789. PubMed ID: 34490097
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.