BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

520 related articles for article (PubMed ID: 28712700)

  • 41. Texture analysis using machine learning-based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy.
    Eun NL; Kang D; Son EJ; Youk JH; Kim JA; Gweon HM
    Eur Radiol; 2021 Sep; 31(9):6916-6928. PubMed ID: 33693994
    [TBL] [Abstract][Full Text] [Related]  

  • 42. Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy.
    Xiong Q; Zhou X; Liu Z; Lei C; Yang C; Yang M; Zhang L; Zhu T; Zhuang X; Liang C; Liu Z; Tian J; Wang K
    Clin Transl Oncol; 2020 Jan; 22(1):50-59. PubMed ID: 30977048
    [TBL] [Abstract][Full Text] [Related]  

  • 43. Texture Analysis with 3.0-T MRI for Association of Response to Neoadjuvant Chemotherapy in Breast Cancer.
    Eun NL; Kang D; Son EJ; Park JS; Youk JH; Kim JA; Gweon HM
    Radiology; 2020 Jan; 294(1):31-41. PubMed ID: 31769740
    [TBL] [Abstract][Full Text] [Related]  

  • 44. 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]  

  • 45. Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study.
    Liu Z; Li Z; Qu J; Zhang R; Zhou X; Li L; Sun K; Tang Z; Jiang H; Li H; Xiong Q; Ding Y; Zhao X; Wang K; Liu Z; Tian J
    Clin Cancer Res; 2019 Jun; 25(12):3538-3547. PubMed ID: 30842125
    [TBL] [Abstract][Full Text] [Related]  

  • 46. 3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients.
    Militello C; Rundo L; Dimarco M; Orlando A; Woitek R; D'Angelo I; Russo G; Bartolotta TV
    Acad Radiol; 2022 Jun; 29(6):830-840. PubMed ID: 34600805
    [TBL] [Abstract][Full Text] [Related]  

  • 47. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI.
    Chen W; Giger ML; Bick U; Newstead GM
    Med Phys; 2006 Aug; 33(8):2878-87. PubMed ID: 16964864
    [TBL] [Abstract][Full Text] [Related]  

  • 48. Identifying Triple-Negative Breast Cancer Using Background Parenchymal Enhancement Heterogeneity on Dynamic Contrast-Enhanced MRI: A Pilot Radiomics Study.
    Wang J; Kato F; Oyama-Manabe N; Li R; Cui Y; Tha KK; Yamashita H; Kudo K; Shirato H
    PLoS One; 2015; 10(11):e0143308. PubMed ID: 26600392
    [TBL] [Abstract][Full Text] [Related]  

  • 49. Prediction of pathological complete response to neoadjuvant chemotherapy in patients with breast cancer using a combination of contrast-enhanced ultrasound and dynamic contrast-enhanced magnetic resonance imaging.
    Han X; Yang H; Jin S; Sun Y; Zhang H; Shan M; Cheng W
    Cancer Med; 2023 Jan; 12(2):1389-1398. PubMed ID: 35822639
    [TBL] [Abstract][Full Text] [Related]  

  • 50. Effect of breast cancer phenotype on diagnostic performance of MRI in the prediction to response to neoadjuvant treatment.
    Bufi E; Belli P; Di Matteo M; Terribile D; Franceschini G; Nardone L; Petrone G; Bonomo L
    Eur J Radiol; 2014 Sep; 83(9):1631-8. PubMed ID: 24938669
    [TBL] [Abstract][Full Text] [Related]  

  • 51. Quantification of breast tumor heterogeneity for ER status, HER2 status, and TN molecular subtype evaluation on DCE-MRI.
    Chang RF; Chen HH; Chang YC; Huang CS; Chen JH; Lo CM
    Magn Reson Imaging; 2016 Jul; 34(6):809-819. PubMed ID: 26968141
    [TBL] [Abstract][Full Text] [Related]  

  • 52. Using quantitative image analysis to classify axillary lymph nodes on breast MRI: a new application for the Z 0011 Era.
    Schacht DV; Drukker K; Pak I; Abe H; Giger ML
    Eur J Radiol; 2015 Mar; 84(3):392-397. PubMed ID: 25547328
    [TBL] [Abstract][Full Text] [Related]  

  • 53. Imaging Phenotypes of Breast Cancer Heterogeneity in Preoperative Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) Scans Predict 10-Year Recurrence.
    Chitalia RD; Rowland J; McDonald ES; Pantalone L; Cohen EA; Gastounioti A; Feldman M; Schnall M; Conant E; Kontos D
    Clin Cancer Res; 2020 Feb; 26(4):862-869. PubMed ID: 31732521
    [TBL] [Abstract][Full Text] [Related]  

  • 54. Combining Dynamic Contrast-Enhanced Magnetic Resonance Imaging and Apparent Diffusion Coefficient Maps for a Radiomics Nomogram to Predict Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients.
    Chen X; Chen X; Yang J; Li Y; Fan W; Yang Z
    J Comput Assist Tomogr; 2020; 44(2):275-283. PubMed ID: 32004189
    [TBL] [Abstract][Full Text] [Related]  

  • 55. Radiomics Analysis Based on Automatic Image Segmentation of DCE-MRI for Predicting Triple-Negative and Nontriple-Negative Breast Cancer.
    Ma M; Gan L; Jiang Y; Qin N; Li C; Zhang Y; Wang X
    Comput Math Methods Med; 2021; 2021():2140465. PubMed ID: 34422088
    [TBL] [Abstract][Full Text] [Related]  

  • 56. A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer.
    McAnena P; Moloney BM; Browne R; O'Halloran N; Walsh L; Walsh S; Sheppard D; Sweeney KJ; Kerin MJ; Lowery AJ
    BMC Med Imaging; 2022 Dec; 22(1):225. PubMed ID: 36564734
    [TBL] [Abstract][Full Text] [Related]  

  • 57. Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results.
    Abramson RG; Li X; Hoyt TL; Su PF; Arlinghaus LR; Wilson KJ; Abramson VG; Chakravarthy AB; Yankeelov TE
    Magn Reson Imaging; 2013 Nov; 31(9):1457-64. PubMed ID: 23954320
    [TBL] [Abstract][Full Text] [Related]  

  • 58. Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation.
    Wu J; Sun X; Wang J; Cui Y; Kato F; Shirato H; Ikeda DM; Li R
    J Magn Reson Imaging; 2017 Oct; 46(4):1017-1027. PubMed ID: 28177554
    [TBL] [Abstract][Full Text] [Related]  

  • 59. Treatment Response Evaluation of Breast Cancer after Neoadjuvant Chemotherapy and Usefulness of the Imaging Parameters of MRI and PET/CT.
    An YY; Kim SH; Kang BJ; Lee AW
    J Korean Med Sci; 2015 Jun; 30(6):808-15. PubMed ID: 26028936
    [TBL] [Abstract][Full Text] [Related]  

  • 60. Effective factors to raise diagnostic performance of breast MRI for diagnosing pathologic complete response in breast cancer patients after neoadjuvant chemotherapy.
    Choi BB; Kim SH
    Acta Radiol; 2015 Jul; 56(7):790-7. PubMed ID: 24951616
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 26.