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 *

551 related articles for article (PubMed ID: 33827490)

  • 1. Predicting cell invasion in breast tumor microenvironment from radiological imaging phenotypes.
    Arefan D; Hausler RM; Sumkin JH; Sun M; Wu S
    BMC Cancer; 2021 Apr; 21(1):370. PubMed ID: 33827490
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction.
    Han X; Gong Z; Guo Y; Tang W; Wei X
    Eur J Radiol; 2024 Jun; 175():111441. PubMed ID: 38537607
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. MRI-based radiomic models to predict surgical margin status and infer tumor immune microenvironment in breast cancer patients with breast-conserving surgery: a multicenter validation study.
    Ma J; Chen K; Li S; Zhu L; Yu Y; Li J; Ma J; Ouyang J; Wu Z; Tan Y; He Z; Liu H; Pan Z; Li H; Liu Q; Song E
    Eur Radiol; 2024 Mar; 34(3):1774-1789. PubMed ID: 37658888
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI.
    Liu C; Ding J; Spuhler K; Gao Y; Serrano Sosa M; Moriarty M; Hussain S; He X; Liang C; Huang C
    J Magn Reson Imaging; 2019 Jan; 49(1):131-140. PubMed ID: 30171822
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics.
    Liu Z; Feng B; Li C; Chen Y; Chen Q; Li X; Guan J; Chen X; Cui E; Li R; Li Z; Long W
    J Magn Reson Imaging; 2019 Sep; 50(3):847-857. PubMed ID: 30773770
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging.
    Parekh VS; Jacobs MA
    Breast Cancer Res Treat; 2020 Apr; 180(2):407-421. PubMed ID: 32020435
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI.
    Lee JY; Lee KS; Seo BK; Cho KR; Woo OH; Song SE; Kim EK; Lee HY; Kim JS; Cha J
    Eur Radiol; 2022 Jan; 32(1):650-660. PubMed ID: 34226990
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study.
    Yu Y; Ren W; He Z; Chen Y; Tan Y; Mao L; Ouyang W; Lu N; Ouyang J; Chen K; Li C; Zhang R; Wu Z; Su F; Wang Z; Hu Q; Xie C; Yao H
    Breast Cancer Res; 2023 Nov; 25(1):132. PubMed ID: 37915093
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Magnetic resonance imaging-based radiomics was used to evaluate the level of prognosis-related immune cell infiltration in breast cancer tumor microenvironment.
    Qian H; Ren X; Xu M; Fang Z; Zhang R; Bu Y; Zhou C
    BMC Med Imaging; 2024 Feb; 24(1):31. PubMed ID: 38308230
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.
    Choi YS; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Jain R; Lee SK
    Eur Radiol; 2020 Jul; 30(7):3834-3842. PubMed ID: 32162004
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Predicting disease recurrence in breast cancer patients using machine learning models with clinical and radiomic characteristics: a retrospective study.
    Azeroual S; Ben-Bouazza FE; Naqi A; Sebihi R
    J Egypt Natl Canc Inst; 2024 Jun; 36(1):20. PubMed ID: 38853190
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Profiling regulatory T lymphocytes within the tumor microenvironment of breast cancer via radiomics.
    Jiang W; Wu R; Yang T; Yu S; Xing W
    Cancer Med; 2023 Dec; 12(24):21861-21872. PubMed ID: 38083903
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer.
    Shayesteh S; Nazari M; Salahshour A; Sandoughdaran S; Hajianfar G; Khateri M; Yaghobi Joybari A; Jozian F; Fatehi Feyzabad SH; Arabi H; Shiri I; Zaidi H
    Med Phys; 2021 Jul; 48(7):3691-3701. PubMed ID: 33894058
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI for Predicting Lymphovascular Invasion in Invasive Breast Cancer.
    Zheng H; Jian L; Li L; Liu W; Chen W
    Acad Radiol; 2024 May; 31(5):1762-1772. PubMed ID: 38092588
    [TBL] [Abstract][Full Text] [Related]  

  • 19. "Real-world" radiomics from multi-vendor MRI: an original retrospective study on the prediction of nodal status and disease survival in breast cancer, as an exemplar to promote discussion of the wider issues.
    Doran SJ; Kumar S; Orton M; d'Arcy J; Kwaks F; O'Flynn E; Ahmed Z; Downey K; Dowsett M; Turner N; Messiou C; Koh DM
    Cancer Imaging; 2021 May; 21(1):37. PubMed ID: 34016188
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.
    Cain EH; Saha A; Harowicz MR; Marks JR; Marcom PK; Mazurowski MA
    Breast Cancer Res Treat; 2019 Jan; 173(2):455-463. PubMed ID: 30328048
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 28.