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 *

139 related articles for article (PubMed ID: 36343408)

  • 1. Multi-task deep learning based on T2-Weighted Images for predicting Muscular-Invasive Bladder Cancer.
    Zou Y; Cai L; Chen C; Shao Q; Fu X; Yu J; Wang L; Chen Z; Yang X; Yuan B; Liu P; Lu Q
    Comput Biol Med; 2022 Dec; 151(Pt A):106219. PubMed ID: 36343408
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A novel predict method for muscular invasion of bladder cancer based on 3D mp-MRI feature fusion.
    Yu J; Cai L; Chen C; Zou Y; Xiao Y; Fu X; Wang L; Yang X; Liu P; Lu Q; Sun X; Shao Q
    Phys Med Biol; 2024 Feb; 69(5):. PubMed ID: 38306973
    [No Abstract]   [Full Text] [Related]  

  • 3. Prospective Assessment of Vesical Imaging Reporting and Data System (VI-RADS) and Its Clinical Impact on the Management of High-risk Non-muscle-invasive Bladder Cancer Patients Candidate for Repeated Transurethral Resection.
    Del Giudice F; Barchetti G; De Berardinis E; Pecoraro M; Salvo V; Simone G; Sciarra A; Leonardo C; Gallucci M; Catalano C; Catto JWF; Panebianco V
    Eur Urol; 2020 Jan; 77(1):101-109. PubMed ID: 31699526
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Quantitative Identification of Nonmuscle-Invasive and Muscle-Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis.
    Xu X; Zhang X; Tian Q; Wang H; Cui LB; Li S; Tang X; Li B; Dolz J; Ayed IB; Liang Z; Yuan J; Du P; Lu H; Liu Y
    J Magn Reson Imaging; 2019 May; 49(5):1489-1498. PubMed ID: 30252978
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning.
    Li J; Qiu Z; Cao K; Deng L; Zhang W; Xie C; Yang S; Yue P; Zhong J; Lyu J; Huang X; Zhang K; Zou Y; Huang B
    Comput Methods Programs Biomed; 2023 May; 233():107466. PubMed ID: 36907040
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study.
    Wang H; Xu X; Zhang X; Liu Y; Ouyang L; Du P; Li S; Tian Q; Ling J; Guo Y; Lu H
    Eur Radiol; 2020 Sep; 30(9):4816-4827. PubMed ID: 32318846
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Application of deep learning as a noninvasive tool to differentiate muscle-invasive bladder cancer and non-muscle-invasive bladder cancer with CT.
    Yang Y; Zou X; Wang Y; Ma X
    Eur J Radiol; 2021 Jun; 139():109666. PubMed ID: 33798819
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Preoperative prediction of muscular invasiveness of bladder cancer with radiomic features on conventional MRI and its high-order derivative maps.
    Xu X; Liu Y; Zhang X; Tian Q; Wu Y; Zhang G; Meng J; Yang Z; Lu H
    Abdom Radiol (NY); 2017 Jul; 42(7):1896-1905. PubMed ID: 28217825
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach.
    Zheng J; Kong J; Wu S; Li Y; Cai J; Yu H; Xie W; Qin H; Wu Z; Huang J; Lin T
    Cancer; 2019 Dec; 125(24):4388-4398. PubMed ID: 31469418
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Intravoxel incoherent motion diffusion-weighted imaging in assessing bladder cancer invasiveness and cell proliferation.
    Wang F; Wu LM; Hua XL; Zhao ZZ; Chen XX; Xu JR
    J Magn Reson Imaging; 2018 Apr; 47(4):1054-1060. PubMed ID: 28815808
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Clinical utility of the Vesical Imaging-Reporting and Data System for muscle-invasive bladder cancer between radiologists and urologists based on multiparametric MRI including 3D FSE T2-weighted acquisitions.
    Arita Y; Shigeta K; Akita H; Suzuki T; Kufukihara R; Kwee TC; Ishii R; Mikami S; Okuda S; Kikuchi E; Oya M; Jinzaki M
    Eur Radiol; 2021 Feb; 31(2):875-883. PubMed ID: 32829418
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Multiple directional DWI combined with T2WI in predicting muscle layer and Ki-67 correlation in bladder cancer in 3.0-T MRI.
    Zhang W; Zhang Z; Xiao W; Wang Y; Ye L; Wei Y; Luo M
    Cancer Med; 2023 May; 12(9):10462-10472. PubMed ID: 36916547
    [TBL] [Abstract][Full Text] [Related]  

  • 13. An evaluation of morphological and functional multi-parametric MRI sequences in classifying non-muscle and muscle invasive bladder cancer.
    Panebianco V; De Berardinis E; Barchetti G; Simone G; Leonardo C; Grompone MD; Del Monte M; Carano D; Gallucci M; Catto J; Catalano C
    Eur Radiol; 2017 Sep; 27(9):3759-3766. PubMed ID: 28181054
    [TBL] [Abstract][Full Text] [Related]  

  • 14. A novel pathway to detect muscle-invasive bladder cancer based on integrated clinical features and VI-RADS score on MRI: results of a prospective multicenter study.
    Bicchetti M; Simone G; Giannarini G; Girometti R; Briganti A; Brunocilla E; Cardone G; De Cobelli F; Gaudiano C; Del Giudice F; Flammia S; Leonardo C; Pecoraro M; Schiavina R; Catalano C; Panebianco V
    Radiol Med; 2022 Aug; 127(8):881-890. PubMed ID: 35763251
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides.
    Woerl AC; Eckstein M; Geiger J; Wagner DC; Daher T; Stenzel P; Fernandez A; Hartmann A; Wand M; Roth W; Foersch S
    Eur Urol; 2020 Aug; 78(2):256-264. PubMed ID: 32354610
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Evaluation of a fractional-order calculus diffusion model and bi-parametric VI-RADS for staging and grading bladder urothelial carcinoma.
    Feng C; Wang Y; Dan G; Zhong Z; Karaman MM; Li Z; Hu D; Zhou XJ
    Eur Radiol; 2022 Feb; 32(2):890-900. PubMed ID: 34342693
    [TBL] [Abstract][Full Text] [Related]  

  • 17. CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer.
    Chen W; Gong M; Zhou D; Zhang L; Kong J; Jiang F; Feng S; Yuan R
    Front Oncol; 2022; 12():1019749. PubMed ID: 36544709
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Multiparametric MRI of the bladder: inter-observer agreement and accuracy with the Vesical Imaging-Reporting and Data System (VI-RADS) at a single reference center.
    Barchetti G; Simone G; Ceravolo I; Salvo V; Campa R; Del Giudice F; De Berardinis E; Buccilli D; Catalano C; Gallucci M; Catto JWF; Panebianco V
    Eur Radiol; 2019 Oct; 29(10):5498-5506. PubMed ID: 30887202
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging-reporting and data system.
    Li J; Cao K; Lin H; Deng L; Yang S; Gao Y; Liang M; Lin C; Zhang W; Xie C; Zhang K; Luo J; Pan Z; Yue P; Zou Y; Huang B
    Eur Radiol; 2023 Apr; 33(4):2699-2709. PubMed ID: 36434397
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Diagnostic performance of diffusion-weighted MR imaging at 3.0 T in predicting muscle invasion in urinary bladder cancer: utility of evaluating the morphology of the reactive tumor stalk.
    Razik A; Das CJ; Sharma S; Seth A; Srivastava DN; Mathur S; Kumar R; Gupta AK
    Abdom Radiol (NY); 2018 Sep; 43(9):2431-2441. PubMed ID: 29392362
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.