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 *

163 related articles for article (PubMed ID: 33689107)

  • 1. Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study.
    Nakai H; Fujimoto K; Yamashita R; Sato T; Someya Y; Taura K; Isoda H; Nakamoto Y
    Jpn J Radiol; 2021 Jul; 39(7):690-702. PubMed ID: 33689107
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?
    Yamada A; Oyama K; Fujita S; Yoshizawa E; Ichinohe F; Komatsu D; Fujinaga Y
    Int J Comput Assist Radiol Surg; 2019 Aug; 14(8):1295-1301. PubMed ID: 31054130
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study.
    Yasaka K; Akai H; Abe O; Kiryu S
    Radiology; 2018 Mar; 286(3):887-896. PubMed ID: 29059036
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study.
    Yamashita R; Mittendorf A; Zhu Z; Fowler KJ; Santillan CS; Sirlin CB; Bashir MR; Do RKG
    Abdom Radiol (NY); 2020 Jan; 45(1):24-35. PubMed ID: 31696269
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.
    Hamm CA; Wang CJ; Savic LJ; Ferrante M; Schobert I; Schlachter T; Lin M; Duncan JS; Weinreb JC; Chapiro J; Letzen B
    Eur Radiol; 2019 Jul; 29(7):3338-3347. PubMed ID: 31016442
    [TBL] [Abstract][Full Text] [Related]  

  • 6. High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules.
    Zhang T; Wang Y; Sun Y; Yuan M; Zhong Y; Li H; Yu T; Wang J
    Eur J Radiol; 2021 Aug; 141():109810. PubMed ID: 34102564
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Poorly versus moderately differentiated hepatocellular carcinoma: vascularity assessment by computed tomographic hepatic angiography in correlation with histologically counted number of unpaired arteries.
    Asayama Y; Yoshimitsu K; Irie H; Nishihara Y; Aishima S; Tajima T; Hirakawa M; Ishigami K; Kakihara D; Taketomi A; Honda H
    J Comput Assist Tomogr; 2007; 31(2):188-92. PubMed ID: 17414751
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Differentiation of distal ureteral stones and pelvic phleboliths using a convolutional neural network.
    Jendeberg J; Thunberg P; Lidén M
    Urolithiasis; 2021 Feb; 49(1):41-49. PubMed ID: 32107579
    [TBL] [Abstract][Full Text] [Related]  

  • 9. LI-RADS-CEUS - Proposal for a Contrast-Enhanced Ultrasound Algorithm for the Diagnosis of Hepatocellular Carcinoma in High-Risk Populations.
    Schellhaas B; Wildner D; Pfeifer L; Goertz RS; Hagel A; Neurath MF; Strobel D
    Ultraschall Med; 2016 Dec; 37(6):627-634. PubMed ID: 27486793
    [No Abstract]   [Full Text] [Related]  

  • 10. Differentiation of mass-forming intrahepatic cholangiocarcinoma from poorly differentiated hepatocellular carcinoma: based on the multivariate analysis of contrast-enhanced computed tomography findings.
    Zhao YJ; Chen WX; Wu DS; Zhang WY; Zheng LR
    Abdom Radiol (NY); 2016 May; 41(5):978-89. PubMed ID: 27193795
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography.
    Cao SE; Zhang LQ; Kuang SC; Shi WQ; Hu B; Xie SD; Chen YN; Liu H; Chen SM; Jiang T; Ye M; Zhang HX; Wang J
    World J Gastroenterol; 2020 Jul; 26(25):3660-3672. PubMed ID: 32742134
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Grading of hepatocellular carcinoma based on diffusion weighted images with multiple b-values using convolutional neural networks.
    Zhou W; Wang G; Xie G; Zhang L
    Med Phys; 2019 Sep; 46(9):3951-3960. PubMed ID: 31169907
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.
    Li S; Jiang H; Pang W
    Comput Biol Med; 2017 May; 84():156-167. PubMed ID: 28365546
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on multi-phase CT scans.
    Ponnoprat D; Inkeaw P; Chaijaruwanich J; Traisathit P; Sripan P; Inmutto N; Na Chiangmai W; Pongnikorn D; Chitapanarux I
    Med Biol Eng Comput; 2020 Oct; 58(10):2497-2515. PubMed ID: 32794015
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets.
    Yang CJ; Wang CK; Fang YD; Wang JY; Su FC; Tsai HM; Lin YJ; Tsai HW; Yeh LR
    PLoS One; 2021; 16(8):e0255605. PubMed ID: 34375365
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Application of classification tree and neural network algorithms to the identification of serological liver marker profiles for the diagnosis of hepatocellular carcinoma.
    Poon TC; Chan AT; Zee B; Ho SK; Mok TS; Leung TW; Johnson PJ
    Oncology; 2001; 61(4):275-83. PubMed ID: 11721174
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Combined hepatocellular and cholangiocarcinoma (biphenotypic) tumors: imaging features and diagnostic accuracy of contrast-enhanced CT and MRI.
    Fowler KJ; Sheybani A; Parker RA; Doherty S; M Brunt E; Chapman WC; Menias CO
    AJR Am J Roentgenol; 2013 Aug; 201(2):332-9. PubMed ID: 23883213
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Distinguishing intrahepatic mass-forming biliary carcinomas from hepatocellular carcinoma by computed tomography and magnetic resonance imaging using the Bayesian method: a bi-center study.
    Ichikawa S; Isoda H; Shimizu T; Tamada D; Taura K; Togashi K; Onishi H; Motosugi U
    Eur Radiol; 2020 Nov; 30(11):5992-6002. PubMed ID: 32500195
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automatic detection and classification of rib fractures based on patients' CT images and clinical information via convolutional neural network.
    Zhou QQ; Tang W; Wang J; Hu ZC; Xia ZY; Zhang R; Fan X; Yong W; Yin X; Zhang B; Zhang H
    Eur Radiol; 2021 Jun; 31(6):3815-3825. PubMed ID: 33201278
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A Deep Convolutional Neural Network With Performance Comparable to Radiologists for Differentiating Between Spinal Schwannoma and Meningioma.
    Maki S; Furuya T; Horikoshi T; Yokota H; Mori Y; Ota J; Kawasaki Y; Miyamoto T; Norimoto M; Okimatsu S; Shiga Y; Inage K; Orita S; Takahashi H; Suyari H; Uno T; Ohtori S
    Spine (Phila Pa 1976); 2020 May; 45(10):694-700. PubMed ID: 31809468
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.