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 *

283 related articles for article (PubMed ID: 35290509)

  • 1. Automatic coronary plaque detection, classification, and stenosis grading using deep learning and radiomics on computed tomography angiography images: a multi-center multi-vendor study.
    Jin X; Li Y; Yan F; Liu Y; Zhang X; Li T; Yang L; Chen H
    Eur Radiol; 2022 Aug; 32(8):5276-5286. PubMed ID: 35290509
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography.
    Zreik M; van Hamersvelt RW; Wolterink JM; Leiner T; Viergever MA; Isgum I
    IEEE Trans Med Imaging; 2019 Jul; 38(7):1588-1598. PubMed ID: 30507498
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography.
    Li Y; Wu Y; He J; Jiang W; Wang J; Peng Y; Jia Y; Xiong T; Jia K; Yi Z; Chen M
    Eur Radiol; 2022 Sep; 32(9):6037-6045. PubMed ID: 35394183
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis.
    Han D; Liu J; Sun Z; Cui Y; He Y; Yang Z
    Comput Methods Programs Biomed; 2020 Nov; 196():105651. PubMed ID: 32712571
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Radiomics-Based Precision Phenotyping Identifies Unstable Coronary Plaques From Computed Tomography Angiography.
    Lin A; Kolossváry M; Cadet S; McElhinney P; Goeller M; Han D; Yuvaraj J; Nerlekar N; Slomka PJ; Marwan M; Nicholls SJ; Achenbach S; Maurovich-Horvat P; Wong DTL; Dey D
    JACC Cardiovasc Imaging; 2022 May; 15(5):859-871. PubMed ID: 35512957
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Performance of an Artificial Intelligence-based Application for the Detection of Plaque-based Stenosis on Monoenergetic Coronary CT Angiography: Validation by Invasive Coronary Angiography.
    Yi Y; Xu C; Guo N; Sun J; Lu X; Yu S; Wang Y; Vembar M; Jin Z; Wang Y
    Acad Radiol; 2022 Apr; 29 Suppl 4():S49-S58. PubMed ID: 34895831
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Predicting coronary plaque progression with conventional plaque parameters and radiomics features derived from coronary CT angiography.
    Feng C; Chen R; Dong S; Deng W; Lin S; Zhu X; Liu W; Xu Y; Li X; Zhu Y
    Eur Radiol; 2023 Dec; 33(12):8513-8520. PubMed ID: 37460800
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Maximization of the usage of coronary CTA derived plaque information using a machine learning based algorithm to improve risk stratification; insights from the CONFIRM registry.
    van Rosendael AR; Maliakal G; Kolli KK; Beecy A; Al'Aref SJ; Dwivedi A; Singh G; Panday M; Kumar A; Ma X; Achenbach S; Al-Mallah MH; Andreini D; Bax JJ; Berman DS; Budoff MJ; Cademartiri F; Callister TQ; Chang HJ; Chinnaiyan K; Chow BJW; Cury RC; DeLago A; Feuchtner G; Hadamitzky M; Hausleiter J; Kaufmann PA; Kim YJ; Leipsic JA; Maffei E; Marques H; Pontone G; Raff GL; Rubinshtein R; Shaw LJ; Villines TC; Gransar H; Lu Y; Jones EC; Peña JM; Lin FY; Min JK
    J Cardiovasc Comput Tomogr; 2018; 12(3):204-209. PubMed ID: 29753765
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology.
    de Graaf MA; Broersen A; Kitslaar PH; Roos CJ; Dijkstra J; Lelieveldt BP; Jukema JW; Schalij MJ; Delgado V; Bax JJ; Reiber JH; Scholte AJ
    Int J Cardiovasc Imaging; 2013 Jun; 29(5):1177-90. PubMed ID: 23417447
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Coronary Computed Tomography Angiography-Specific Definitions of High-Risk Plaque Features Improve Detection of Acute Coronary Syndrome.
    Bittner DO; Mayrhofer T; Puchner SB; Lu MT; Maurovich-Horvat P; Ghemigian K; Kitslaar PH; Broersen A; Bamberg F; Truong QA; Schlett CL; Hoffmann U; Ferencik M
    Circ Cardiovasc Imaging; 2018 Aug; 11(8):e007657. PubMed ID: 30354493
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automated plaque classification using computed tomography angiography and Gabor transformations.
    Rajendra Acharya U; Meiburger KM; Wei Koh JE; Vicnesh J; Ciaccio EJ; Shu Lih O; Tan SK; Aman RRAR; Molinari F; Ng KH
    Artif Intell Med; 2019 Sep; 100():101724. PubMed ID: 31607348
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Context-aware deep network for coronary artery stenosis classification in coronary CT angiography.
    Wang X; Leng S; Lu Z; Huang S; Lee BH; Baskaran L; Yew MS; Teo L; Chan MY; Ngiam KY; Lee HK; Zhong L; Huang W
    Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38083399
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis.
    van Hamersvelt RW; Zreik M; Voskuil M; Viergever MA; Išgum I; Leiner T
    Eur Radiol; 2019 May; 29(5):2350-2359. PubMed ID: 30421020
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automatic prediction of acute coronary syndrome based on pericoronary adipose tissue and atherosclerotic plaques.
    Huang Y; Yang J; Hou Y; Sun Q; Ma S; Feng C; Shang J
    Comput Med Imaging Graph; 2023 Sep; 108():102264. PubMed ID: 37418789
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Diffuse coronary artery disease among other atherosclerotic plaque characteristics by coronary computed tomography angiography for predicting coronary vessel-specific ischemia by fractional flow reserve.
    Rizvi A; Hartaigh BÓ; Danad I; Han D; Lee JH; Gransar H; Szymonifka J; Lin FY; Min JK
    Atherosclerosis; 2017 Mar; 258():145-151. PubMed ID: 28168977
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Deep Learning Model for Coronary Angiography.
    Ling H; Chen B; Guan R; Xiao Y; Yan H; Chen Q; Bi L; Chen J; Feng X; Pang H; Song C
    J Cardiovasc Transl Res; 2023 Aug; 16(4):896-904. PubMed ID: 36928587
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result From the MACHINE Consortium.
    Coenen A; Kim YH; Kruk M; Tesche C; De Geer J; Kurata A; Lubbers ML; Daemen J; Itu L; Rapaka S; Sharma P; Schwemmer C; Persson A; Schoepf UJ; Kepka C; Hyun Yang D; Nieman K
    Circ Cardiovasc Imaging; 2018 Jun; 11(6):e007217. PubMed ID: 29914866
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Identification of pathology-confirmed vulnerable atherosclerotic lesions by coronary computed tomography angiography using radiomics analysis.
    Li XN; Yin WH; Sun Y; Kang H; Luo J; Chen K; Hou ZH; Gao Y; Ren XS; Yu YT; An YQ; Zhang Y; Wang HY; Lu B
    Eur Radiol; 2022 Jun; 32(6):4003-4013. PubMed ID: 35171348
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection.
    Paul JF; Rohnean A; Giroussens H; Pressat-Laffouilhere T; Wong T
    Diagn Interv Imaging; 2022 Jun; 103(6):316-323. PubMed ID: 35090845
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.