BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

312 related articles for article (PubMed ID: 36604679)

  • 1. A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients.
    Liu S; Du S; Gao S; Teng Y; Jin F; Zhang L
    BMC Cancer; 2023 Jan; 23(1):15. PubMed ID: 36604679
    [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. Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI Predicts Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy.
    Guo L; Du S; Gao S; Zhao R; Huang G; Jin F; Teng Y; Zhang L
    Cancers (Basel); 2022 Jul; 14(14):. PubMed ID: 35884576
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Noninvasive prediction of node-positive breast cancer response to presurgical neoadjuvant chemotherapy therapy based on machine learning of axillary lymph node ultrasound.
    Zhang H; Cao W; Liu L; Meng Z; Sun N; Meng Y; Fei J
    J Transl Med; 2023 May; 21(1):337. PubMed ID: 37211604
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Radiomics Based on Dynamic Contrast-Enhanced MRI to Early Predict Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Therapy.
    Zeng Q; Ke M; Zhong L; Zhou Y; Zhu X; He C; Liu L
    Acad Radiol; 2023 Aug; 30(8):1638-1647. PubMed ID: 36564256
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Radiomic features of axillary lymph nodes based on pharmacokinetic modeling DCE-MRI allow preoperative diagnosis of their metastatic status in breast cancer.
    Luo HB; Liu YY; Wang CH; Qing HM; Wang M; Zhang X; Chen XY; Xu GH; Zhou P; Ren J
    PLoS One; 2021; 16(3):e0247074. PubMed ID: 33647031
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Non-invasive prediction model of axillary lymph node status in patients with early-stage breast cancer: a feasibility study based on dynamic contrast-enhanced-MRI radiomics.
    Chen W; Lin G; Kong C; Wu X; Hu Y; Chen M; Xia S; Lu C; Xu M; Ji J
    Br J Radiol; 2024 Feb; 97(1154):439-450. PubMed ID: 38308028
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer.
    Zhang B; Yu Y; Mao Y; Wang H; Lv M; Su X; Wang Y; Li Z; Zhang Z; Bian T; Wang Q
    Acad Radiol; 2024 Mar; 31(3):800-811. PubMed ID: 37914627
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer.
    Li Y; Fan Y; Xu D; Li Y; Zhong Z; Pan H; Huang B; Xie X; Yang Y; Liu B
    Front Oncol; 2022; 12():1041142. PubMed ID: 36686755
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study.
    Gu J; Tong T; Xu D; Cheng F; Fang C; He C; Wang J; Wang B; Yang X; Wang K; Tian J; Jiang T
    Cancer; 2023 Feb; 129(3):356-366. PubMed ID: 36401611
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Attention-based Deep Learning for the Preoperative Differentiation of Axillary Lymph Node Metastasis in Breast Cancer on DCE-MRI.
    Gao J; Zhong X; Li W; Li Q; Shao H; Wang Z; Dai Y; Ma H; Shi Y; Zhang H; Duan S; Zhang K; Yang P; Zhao F; Zhang H; Xie H; Mao N
    J Magn Reson Imaging; 2023 Jun; 57(6):1842-1853. PubMed ID: 36219519
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Prediction of the number of metastatic axillary lymph nodes in breast cancer by radiomic signature based on dynamic contrast-enhanced MRI.
    Li L; Yu T; Sun J; Jiang S; Liu D; Wang X; Zhang J
    Acta Radiol; 2022 Aug; 63(8):1014-1022. PubMed ID: 34162234
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An MRI-based Scoring System for Preoperative Prediction of Axillary Response to Neoadjuvant Chemotherapy in Node-Positive Breast Cancer: A Multicenter Retrospective Study.
    Huang X; Shi Z; Mai J; Liu C; Liu C; Chen S; Lu H; Li Y; He B; Li J; Cun H; Han C; Chen X; Liang C; Liu Z
    Acad Radiol; 2023 Jul; 30(7):1257-1269. PubMed ID: 36280517
    [TBL] [Abstract][Full Text] [Related]  

  • 16. The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer.
    Wang J; Tian C; Zheng BJ; Zhang J; Jiao DC; Qu JR; Liu ZZ
    BMC Cancer; 2024 May; 24(1):549. PubMed ID: 38693523
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE-MRI Radiomics Nomogram.
    Liu Y; Li X; Zhu L; Zhao Z; Wang T; Zhang X; Cai B; Li L; Ma M; Ma X; Ming J
    Contrast Media Mol Imaging; 2022; 2022():6729473. PubMed ID: 36051932
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity.
    Zhang X; Teng X; Zhang J; Lai Q; Cai J
    Breast Cancer Res; 2024 May; 26(1):77. PubMed ID: 38745321
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. A two-center study of a combined nomogram based on mammography and MRI to predict ALN metastasis in breast cancer.
    Hua Y; Peng Q; Han J; Fei J; Sun A
    Magn Reson Imaging; 2024 Jul; 110():128-137. PubMed ID: 38631535
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.