These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

149 related articles for article (PubMed ID: 38291499)

  • 1. MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma.
    Chen D; Zhang R; Huang X; Ji C; Xia W; Qi Y; Yang X; Lin L; Wang J; Cheng H; Tang W; Yu J; Hoon DSB; Zhang J; Gao X; Yao Y
    Biomark Res; 2024 Jan; 12(1):14. PubMed ID: 38291499
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study.
    Sun R; Limkin EJ; Vakalopoulou M; Dercle L; Champiat S; Han SR; Verlingue L; Brandao D; Lancia A; Ammari S; Hollebecque A; Scoazec JY; Marabelle A; Massard C; Soria JC; Robert C; Paragios N; Deutsch E; Ferté C
    Lancet Oncol; 2018 Sep; 19(9):1180-1191. PubMed ID: 30120041
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Absolute quantification of tumor-infiltrating immune cells in high-grade glioma identifies prognostic and radiomics values.
    Kim AR; Choi KS; Kim MS; Kim KM; Kang H; Kim S; Chowdhury T; Yu HJ; Lee CE; Lee JH; Lee ST; Won JK; Kim JW; Kim YH; Kim TM; Park SH; Choi SH; Shin EC; Park CK
    Cancer Immunol Immunother; 2021 Jul; 70(7):1995-2008. PubMed ID: 33416947
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Improving survival prediction of high-grade glioma via machine learning techniques based on MRI radiomic, genetic and clinical risk factors.
    Tan Y; Mu W; Wang XC; Yang GQ; Gillies RJ; Zhang H
    Eur J Radiol; 2019 Nov; 120():108609. PubMed ID: 31606714
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
    Yu Y; He Z; Ouyang J; Tan Y; Chen Y; Gu Y; Mao L; Ren W; Wang J; Lin L; Wu Z; Liu J; Ou Q; Hu Q; Li A; Chen K; Li C; Lu N; Li X; Su F; Liu Q; Xie C; Yao H
    EBioMedicine; 2021 Jul; 69():103460. PubMed ID: 34233259
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study.
    Cui S; Tang T; Su Q; Wang Y; Shu Z; Yang W; Gong X
    Cancer Imaging; 2021 Mar; 21(1):26. PubMed ID: 33750453
    [TBL] [Abstract][Full Text] [Related]  

  • 7. MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study.
    Tan R; Sui C; Wang C; Zhu T
    Front Oncol; 2024; 14():1401977. PubMed ID: 38803534
    [TBL] [Abstract][Full Text] [Related]  

  • 8. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas.
    Li G; Li L; Li Y; Qian Z; Wu F; He Y; Jiang H; Li R; Wang D; Zhai Y; Wang Z; Jiang T; Zhang J; Zhang W
    Brain; 2022 Apr; 145(3):1151-1161. PubMed ID: 35136934
    [TBL] [Abstract][Full Text] [Related]  

  • 9. MR-Based Radiomics Predicts CDK6 Expression and Prognostic Value in High-grade Glioma.
    Sun C; Jiang C; Wang X; Ma S; Zhang D; Jia W
    Acad Radiol; 2024 Dec; 31(12):5141-5153. PubMed ID: 38964985
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Assessing treatment outcomes of chemoimmunotherapy in extensive-stage small cell lung cancer: an integrated clinical and radiomics approach.
    Zhao J; He Y; Yang X; Tian P; Zeng L; Huang K; Zhao J; Zhou J; Zhu Y; Wang Q; Chen M; Li W; Gao Y; Zhang Y; Xia Y
    J Immunother Cancer; 2023 Sep; 11(9):. PubMed ID: 37730276
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Radiomics Nomogram Building From Multiparametric MRI to Predict Grade in Patients With Glioma: A Cohort Study.
    Wang Q; Li Q; Mi R; Ye H; Zhang H; Chen B; Li Y; Huang G; Xia J
    J Magn Reson Imaging; 2019 Mar; 49(3):825-833. PubMed ID: 30260592
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response.
    Han X; Guo Y; Ye H; Chen Z; Hu Q; Wei X; Liu Z; Liang C
    Breast Cancer Res; 2024 Jan; 26(1):18. PubMed ID: 38287356
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics.
    Chen W; Wang S; Dong D; Gao X; Zhou K; Li J; Lv B; Li H; Wu X; Fang M; Tian J; Xu M
    Front Oncol; 2019; 9():1265. PubMed ID: 31824847
    [No Abstract]   [Full Text] [Related]  

  • 14. MRI-based multiregional radiomics for predicting lymph nodes status and prognosis in patients with resectable rectal cancer.
    Li H; Chen XL; Liu H; Lu T; Li ZL
    Front Oncol; 2022; 12():1087882. PubMed ID: 36686763
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Noninvasive Evaluation of the Notch Signaling Pathway via Radiomic Signatures Based on Multiparametric MRI in Association With Biological Functions of Patients With Glioma: A Multi-institutional Study.
    Shen N; Lv W; Li S; Liu D; Xie Y; Zhang J; Zhang J; Jiang J; Jiang R; Zhu W
    J Magn Reson Imaging; 2023 Mar; 57(3):884-896. PubMed ID: 35929909
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Development and Validation of a Preoperative Magnetic Resonance Imaging Radiomics-Based Signature to Predict Axillary Lymph Node Metastasis and Disease-Free Survival in Patients With Early-Stage Breast Cancer.
    Yu Y; Tan Y; Xie C; Hu Q; Ouyang J; Chen Y; Gu Y; Li A; Lu N; He Z; Yang Y; Chen K; Ma J; Li C; Ma M; Li X; Zhang R; Zhong H; Ou Q; Zhang Y; He Y; Li G; Wu Z; Su F; Song E; Yao H
    JAMA Netw Open; 2020 Dec; 3(12):e2028086. PubMed ID: 33289845
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas.
    Liu X; Li Y; Qian Z; Sun Z; Xu K; Wang K; Liu S; Fan X; Li S; Zhang Z; Jiang T; Wang Y
    Neuroimage Clin; 2018; 20():1070-1077. PubMed ID: 30366279
    [TBL] [Abstract][Full Text] [Related]  

  • 18. BTK Expression Level Prediction and the High-Grade Glioma Prognosis Using Radiomic Machine Learning Models.
    Jiang C; Sun C; Wang X; Ma S; Jia W; Zhang D
    J Imaging Inform Med; 2024 Aug; 37(4):1359-1374. PubMed ID: 38381384
    [TBL] [Abstract][Full Text] [Related]  

  • 19. [Prediction of platinum-based chemotherapy sensitivity for epithelial ovarian cancer by multi-sequence MRI-based radiomic nomogram].
    Mao MM; Li HM; Shi J; Qiu QS; Feng F
    Zhonghua Yi Xue Za Zhi; 2022 Jan; 102(3):201-208. PubMed ID: 35042289
    [No Abstract]   [Full Text] [Related]  

  • 20. MRI-based Machine Learning Radiomics Can Predict CSF1R Expression Level and Prognosis in High-grade Gliomas.
    Lai Y; Wu Y; Chen X; Gu W; Zhou G; Weng M
    J Imaging Inform Med; 2024 Feb; 37(1):209-229. PubMed ID: 38343263
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.