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 *

410 related articles for article (PubMed ID: 30848041)

  • 41. Simultaneous bilateral breast and high-resolution axillary MRI of patients with breast cancer: preliminary results.
    Luciani A; Dao TH; Lapeyre M; Schwarzinger M; Debaecque C; Lantieri L; Revelon G; Bouanane M; Kobeiter H; Rahmouni A
    AJR Am J Roentgenol; 2004 Apr; 182(4):1059-67. PubMed ID: 15039188
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Improving lymph node characterization in staging malignant lymphoma using first-order ADC texture analysis from whole-body diffusion-weighted MRI.
    De Paepe KN; De Keyzer F; Wolter P; Bechter O; Dierickx D; Janssens A; Verhoef G; Oyen R; Vandecaveye V
    J Magn Reson Imaging; 2018 Oct; 48(4):897-906. PubMed ID: 29656584
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Computer-aided prediction model for axillary lymph node metastasis in breast cancer using tumor morphological and textural features on ultrasound.
    Moon WK; Chen IL; Yi A; Bae MS; Shin SU; Chang RF
    Comput Methods Programs Biomed; 2018 Aug; 162():129-137. PubMed ID: 29903479
    [TBL] [Abstract][Full Text] [Related]  

  • 44. MRI radiomics and biological correlations for predicting axillary lymph node burden in early-stage breast cancer.
    Hong M; Fan S; Xu Z; Fang Z; Ling K; Lai P; Han C; Chen Z; Hou J; Liang Y; Zhou C; Wang J; Chen X; Huang Y; Xu M
    J Transl Med; 2024 Sep; 22(1):826. PubMed ID: 39243024
    [TBL] [Abstract][Full Text] [Related]  

  • 45. Adding contrast-enhanced ultrasound markers to conventional axillary ultrasound improves specificity for predicting axillary lymph node metastasis in patients with breast cancer.
    Du LW; Liu HL; Gong HY; Ling LJ; Wang S; Li CY; Zong M
    Br J Radiol; 2021 Feb; 94(1118):20200874. PubMed ID: 32976019
    [TBL] [Abstract][Full Text] [Related]  

  • 46. Intra- and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer.
    Jiang W; Meng R; Cheng Y; Wang H; Han T; Qu N; Yu T; Hou Y; Xu S
    J Magn Reson Imaging; 2024 Feb; 59(2):613-625. PubMed ID: 37199241
    [TBL] [Abstract][Full Text] [Related]  

  • 47. Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer.
    Li X; Yang L; Jiao X
    Acad Radiol; 2023 Jul; 30(7):1281-1287. PubMed ID: 36376154
    [TBL] [Abstract][Full Text] [Related]  

  • 48. 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]  

  • 49. Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer.
    Han L; Zhu Y; Liu Z; Yu T; He C; Jiang W; Kan Y; Dong D; Tian J; Luo Y
    Eur Radiol; 2019 Jul; 29(7):3820-3829. PubMed ID: 30701328
    [TBL] [Abstract][Full Text] [Related]  

  • 50. MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.
    Guo YJ; Yin R; Zhang Q; Han JQ; Dou ZX; Wang PB; Lu H; Liu PF; Chen JJ; Ma WJ
    J Magn Reson Imaging; 2024 Oct; 60(4):1352-1364. PubMed ID: 38205712
    [TBL] [Abstract][Full Text] [Related]  

  • 51. Assessing the performance of benign and malignant breast lesion classification with bilateral TIC differentiation and other effective features in DCE-MRI.
    Li H; Sun H; Liu S; Zhang W; Arukalam FM; Ma H; Qian W
    J Magn Reson Imaging; 2019 Aug; 50(2):465-473. PubMed ID: 30688398
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Diagnostic accuracy of pre-operative breast magnetic resonance imaging (MRI) in predicting axillary lymph node metastasis: variations in intrinsic subtypes, and strategy to improve negative predictive value-an analysis of 2473 invasive breast cancer patients.
    Chen ST; Lai HW; Chang JH; Liao CY; Wen TC; Wu WP; Wu HK; Lin YJ; Chang YJ; Chen ST; Chen DR; Huang HI; Hung CL
    Breast Cancer; 2023 Nov; 30(6):976-985. PubMed ID: 37500823
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset.
    Ha R; Chang P; Karcich J; Mutasa S; Fardanesh R; Wynn RT; Liu MZ; Jambawalikar S
    J Digit Imaging; 2018 Dec; 31(6):851-856. PubMed ID: 29696472
    [TBL] [Abstract][Full Text] [Related]  

  • 54. Intravoxel incoherent motion diffusion-weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer-aided diagnosis.
    Song SE; Cho KR; Seo BK; Woo OH; Park KH; Son YH; Grimm R
    J Magn Reson Imaging; 2019 Jan; 49(1):118-130. PubMed ID: 30238533
    [TBL] [Abstract][Full Text] [Related]  

  • 55. 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]  

  • 56. DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers.
    Fan M; Cheng H; Zhang P; Gao X; Zhang J; Shao G; Li L
    J Magn Reson Imaging; 2018 Jul; 48(1):237-247. PubMed ID: 29219225
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Whole-tumor histogram models based on quantitative maps from synthetic MRI for predicting axillary lymph node status in invasive ductal breast cancer.
    Zeng F; Yang Z; Tang X; Lin L; Lin H; Wu Y; Wang Z; Chen M; Chen L; Chen L; Wu PY; Wang C; Xue Y
    Eur J Radiol; 2024 Mar; 172():111325. PubMed ID: 38262156
    [TBL] [Abstract][Full Text] [Related]  

  • 58. A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.
    Cai R; Deng L; Zhang H; Zhang H; Wu Q
    Radiat Oncol; 2024 May; 19(1):63. PubMed ID: 38802938
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Noninvasive nodal staging in patients with breast cancer using gadofosveset-enhanced magnetic resonance imaging: a feasibility study.
    Schipper RJ; Smidt ML; van Roozendaal LM; Castro CJ; de Vries B; Heuts EM; Keymeulen KB; Wildberger JE; Lobbes MB; Beets-Tan RG
    Invest Radiol; 2013 Mar; 48(3):134-9. PubMed ID: 23262788
    [TBL] [Abstract][Full Text] [Related]  

  • 60. Added Value of MRI for Invasive Breast Cancer including the Entire Axilla for Evaluation of High-Level or Advanced Axillary Lymph Node Metastasis in the Post-ACOSOG Z0011 Trial Era.
    Byon JH; Park YV; Yoon JH; Moon HJ; Kim EK; Kim MJ; You JK
    Radiology; 2021 Jul; 300(1):46-54. PubMed ID: 33904772
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 21.