BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

170 related articles for article (PubMed ID: 37329614)

  • 1. Basing on the machine learning model to analyse the coronary calcification score and the coronary flow reserve score to evaluate the degree of coronary artery stenosis.
    Zhang Y; Liu P; Tang LJ; Lin PM; Li R; Luo HR; Luo P
    Comput Biol Med; 2023 Sep; 163():107130. PubMed ID: 37329614
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Using a machine learning-based risk prediction model to analyze the coronary artery calcification score and predict coronary heart disease and risk assessment.
    Huang Y; Ren Y; Yang H; Ding Y; Liu Y; Yang Y; Mao A; Yang T; Wang Y; Xiao F; He Q; Zhang Y
    Comput Biol Med; 2022 Dec; 151(Pt B):106297. PubMed ID: 36435054
    [TBL] [Abstract][Full Text] [Related]  

  • 3. [The prognostic evaluation of coronary artery calcification score and CT fractional flow reserve for patients with stable coronary artery disease].
    Zhou F; Zhang RQ; Guo X; Zhang LJ
    Zhonghua Yi Xue Za Zhi; 2024 Jun; 104(22):2051-2058. PubMed ID: 38858215
    [No Abstract]   [Full Text] [Related]  

  • 4. Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry.
    Tesche C; Otani K; De Cecco CN; Coenen A; De Geer J; Kruk M; Kim YH; Albrecht MH; Baumann S; Renker M; Bayer RR; Duguay TM; Litwin SE; Varga-Szemes A; Steinberg DH; Yang DH; Kepka C; Persson A; Nieman K; Schoepf UJ
    JACC Cardiovasc Imaging; 2020 Mar; 13(3):760-770. PubMed ID: 31422141
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Effect of 320-Row Computed Tomography Acquisition Technology on Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve Based on Machine Learning: Systolic and Diastolic Scan Acquisition.
    Yang F; Shi K; Chen Y; Yin Y; Zhao Y; Zhang T
    J Comput Assist Tomogr; 2023 Mar-Apr 01; 47(2):205-211. PubMed ID: 36877750
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation.
    Hae H; Kang SJ; Kim WJ; Choi SY; Lee JG; Bae Y; Cho H; Yang DH; Kang JW; Lim TH; Lee CH; Kang DY; Lee PH; Ahn JM; Park DW; Lee SW; Kim YH; Lee CW; Park SW; Park SJ
    PLoS Med; 2018 Nov; 15(11):e1002693. PubMed ID: 30422987
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in patients before liver transplantation using CT-FFR machine learning algorithm.
    Schuessler M; Saner F; Al-Rashid F; Schlosser T
    Eur Radiol; 2022 Dec; 32(12):8761-8768. PubMed ID: 35729425
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Impact of machine-learning-based coronary computed tomography angiography-derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.
    Brandt V; Schoepf UJ; Aquino GJ; Bekeredjian R; Varga-Szemes A; Emrich T; Bayer RR; Schwarz F; Kroencke TJ; Tesche C; Decker JA
    Eur Radiol; 2022 Sep; 32(9):6008-6016. PubMed ID: 35359166
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Impact of coronary calcium score and lesion characteristics on the diagnostic performance of machine-learning-based computed tomography-derived fractional flow reserve.
    Koo HJ; Kang JW; Kang SJ; Kweon J; Lee JG; Ahn JM; Park DW; Lee SW; Lee CW; Park SW; Park SJ; Kim YH; Yang DH
    Eur Heart J Cardiovasc Imaging; 2021 Aug; 22(9):998-1006. PubMed ID: 33842953
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography.
    Renker M; Baumann S; Hamm CW; Tesche C; Kim WK; Savage RH; Coenen A; Nieman K; De Geer J; Persson A; Kruk M; Kepka C; Yang DH; Schoepf UJ
    J Cardiovasc Comput Tomogr; 2021; 15(6):492-498. PubMed ID: 34119471
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia.
    von Knebel Doeberitz PL; De Cecco CN; Schoepf UJ; Duguay TM; Albrecht MH; van Assen M; Bauer MJ; Savage RH; Pannell JT; De Santis D; Johnson AA; Varga-Szemes A; Bayer RR; Schönberg SO; Nance JW; Tesche C
    Eur Radiol; 2019 May; 29(5):2378-2387. PubMed ID: 30523456
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The effect of coronary calcification on diagnostic performance of machine learning-based CT-FFR: a Chinese multicenter study.
    Di Jiang M; Zhang XL; Liu H; Tang CX; Li JH; Wang YN; Xu PP; Zhou CS; Zhou F; Lu MJ; Zhang JY; Yu MM; Hou Y; Zheng MW; Zhang B; Zhang DM; Yi Y; Xu L; Hu XH; Yang J; Lu GM; Ni QQ; Zhang LJ
    Eur Radiol; 2021 Mar; 31(3):1482-1493. PubMed ID: 32929641
    [TBL] [Abstract][Full Text] [Related]  

  • 13. The differences between patients with nonalcoholic fatty liver disease (NAFLD) and those without NAFLD, as well as predictors of functional coronary artery ischemia in patients with NAFLD.
    Li WJ; Xu HW
    Clin Cardiol; 2024 Feb; 47(2):e24205. PubMed ID: 38108229
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials.
    Nous FMA; Budde RPJ; Lubbers MM; Yamasaki Y; Kardys I; Bruning TA; Akkerhuis JM; Kofflard MJM; Kietselaer B; Galema TW; Nieman K
    Eur Radiol; 2020 Jul; 30(7):3692-3701. PubMed ID: 32166492
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Diagnostic performance of machine-learning-based computed fractional flow reserve (FFR) derived from coronary computed tomography angiography for the assessment of myocardial ischemia verified by invasive FFR.
    Hu X; Yang M; Han L; Du Y
    Int J Cardiovasc Imaging; 2018 Dec; 34(12):1987-1996. PubMed ID: 30062537
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A meta-analysis comparing the diagnostic performance of computed tomography-derived fractional flow reserve and coronary computed tomography angiography at different levels of coronary artery calcium score.
    Ma Z; Tu C; Zhang B; Zhang D; Song X; Zhang H
    Eur Radiol; 2024 Feb; ():. PubMed ID: 38334761
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Evaluation of Significant Coronary Artery Disease Based on CT Fractional Flow Reserve and Plaque Characteristics Using Random Forest Analysis in Machine Learning.
    Kawasaki T; Kidoh M; Kido T; Sueta D; Fujimoto S; Kumamaru KK; Uetani T; Tanabe Y; Ueda T; Sakabe D; Oda S; Yamashiro T; Tsujita K; Kato S; Yuki H; Utsunomiya D
    Acad Radiol; 2020 Dec; 27(12):1700-1708. PubMed ID: 32057618
    [TBL] [Abstract][Full Text] [Related]  

  • 18. [Value of fractional flow reserve derived from coronary computed tomographic angiography and plaque quantitative analysis in predicting adverse outcomes of non-obstructive coronary heart disease].
    Liu J; Wu Y; Huang H; Wang P; Wu Q; Qiao H
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Jun; 35(6):615-619. PubMed ID: 37366128
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Influence of diabetes mellitus on the diagnostic performance of machine learning-based coronary CT angiography-derived fractional flow reserve: a multicenter study.
    Xue Y; Zheng MW; Hou Y; Zhou F; Li JH; Wang YN; Liu CY; Zhou CS; Zhang JY; Yu MM; Zhang B; Zhang DM; Yi Y; Xu L; Hu XH; Lu GM; Tang CX; Zhang LJ
    Eur Radiol; 2022 Jun; 32(6):3778-3789. PubMed ID: 35020012
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Noninvasive diagnosis of ischemia-causing coronary stenosis using CT angiography: diagnostic value of transluminal attenuation gradient and fractional flow reserve computed from coronary CT angiography compared to invasively measured fractional flow reserve.
    Yoon YE; Choi JH; Kim JH; Park KW; Doh JH; Kim YJ; Koo BK; Min JK; Erglis A; Gwon HC; Choe YH; Choi DJ; Kim HS; Oh BH; Park YB
    JACC Cardiovasc Imaging; 2012 Nov; 5(11):1088-96. PubMed ID: 23153908
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.