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 *

120 related articles for article (PubMed ID: 36261505)

  • 1. Deep learning-based imaging reconstruction for MRI after neoadjuvant chemoradiotherapy for rectal cancer: effects on image quality and assessment of treatment response.
    Kim B; Lee CM; Jang JK; Kim J; Lim SB; Kim AY
    Abdom Radiol (NY); 2023 Jan; 48(1):201-210. PubMed ID: 36261505
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Ultra-High-Resolution T2-Weighted PROPELLER MRI of the Rectum With Deep Learning Reconstruction: Assessment of Image Quality and Diagnostic Performance.
    Matsumoto S; Tsuboyama T; Onishi H; Fukui H; Honda T; Wakayama T; Wang X; Matsui T; Nakamoto A; Ota T; Kiso K; Osawa K; Tomiyama N
    Invest Radiol; 2024 Jul; 59(7):479-488. PubMed ID: 37975732
    [TBL] [Abstract][Full Text] [Related]  

  • 3. How to Combine Diffusion-Weighted and T2-Weighted Imaging for MRI Assessment of Pathologic Complete Response to Neoadjuvant Chemoradiotherapy in Patients with Rectal Cancer?
    Jang JK; Lee CM; Park SH; Kim JH; Kim J; Lim SB; Yu CS; Kim JC
    Korean J Radiol; 2021 Sep; 22(9):1451-1461. PubMed ID: 34132075
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Predicting Rectal Cancer Response to Neoadjuvant Chemoradiotherapy Using Deep Learning of Diffusion Kurtosis MRI.
    Zhang XY; Wang L; Zhu HT; Li ZW; Ye M; Li XT; Shi YJ; Zhu HC; Sun YS
    Radiology; 2020 Jul; 296(1):56-64. PubMed ID: 32315264
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.
    Cingoz E; Ertas G; Kaval G; Azamat S; Karaman S; Kulle CB; Berker N; Cingöz M; Dagoglu Sakin N; Comert RG; Buyuk M; Kartal MGD
    Curr Med Imaging; 2024; 20():e15734056309748. PubMed ID: 38874041
    [TBL] [Abstract][Full Text] [Related]  

  • 6. [The value of MR T2WI signal intensity related parameters for predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer].
    Wan LJ; Zhang CD; Zhang HM; Meng YK; Ye F; Liu Y; Zhao XM; Zhou CW
    Zhonghua Zhong Liu Za Zhi; 2019 Nov; 41(11):837-843. PubMed ID: 31770851
    [No Abstract]   [Full Text] [Related]  

  • 7. Diagnostic performance of magnetic resonance to assess treatment response after neoadjuvant therapy in patients with locally advanced rectal cancer.
    Nahas SC; Nahas CSR; Cama GM; de Azambuja RL; Horvat N; Marques CFS; Menezes MR; Junior UR; Cecconello I
    Abdom Radiol (NY); 2019 Nov; 44(11):3632-3640. PubMed ID: 30663025
    [TBL] [Abstract][Full Text] [Related]  

  • 8. [A prediction model of pathological complete response in patients with locally advanced rectal cancer after PD-1 antibody combined with total neoadjuvant chemoradiotherapy based on MRI radiomics].
    Zhang XY; Zhu HT; Li XT; Li YJ; Li ZW; Wang WH; Wu AW; Sun YS; Zhang L
    Zhonghua Wei Chang Wai Ke Za Zhi; 2022 Mar; 25(3):228-234. PubMed ID: 35340172
    [No Abstract]   [Full Text] [Related]  

  • 9. The role of readout-segmented echo-planar imaging-based diffusion-weighted imaging in evaluating tumor response of locally advanced rectal cancer after neoadjuvant chemoradiotherapy.
    Yang L; Xia C; Liu D; Fang X; Pan X; Ma L; Wu B
    Acta Radiol; 2020 Sep; 61(9):1155-1164. PubMed ID: 31924105
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Image Quality Assessment of 2D
    Hausmann D; Liu J; Budjan J; Reichert M; Ong M; Meyer M; Smakic A; Grimm R; Strecker R; Schoenberg SO; Wang X; Attenberger UI
    Anticancer Res; 2018 Feb; 38(2):969-978. PubMed ID: 29374729
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging.
    Jang BS; Lim YJ; Song C; Jeon SH; Lee KW; Kang SB; Lee YJ; Kim JS
    Radiother Oncol; 2021 Aug; 161():183-190. PubMed ID: 34139211
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Feasibility of high-resolution magnetic resonance imaging of the liver using deep learning reconstruction based on the deep learning denoising technique.
    Tanabe M; Higashi M; Yonezawa T; Yamaguchi T; Iida E; Furukawa M; Okada M; Shinoda K; Ito K
    Magn Reson Imaging; 2021 Jul; 80():121-126. PubMed ID: 33971240
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Developing a prediction model based on MRI for pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
    Wan L; Zhang C; Zhao Q; Meng Y; Zou S; Yang Y; Liu Y; Jiang J; Ye F; Ouyang H; Zhao X; Zhang H
    Abdom Radiol (NY); 2019 Sep; 44(9):2978-2987. PubMed ID: 31327039
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Image Quality and Diagnostic Performance of Accelerated Shoulder MRI With Deep Learning-Based Reconstruction.
    Hahn S; Yi J; Lee HJ; Lee Y; Lim YJ; Bang JY; Kim H; Lee J
    AJR Am J Roentgenol; 2022 Mar; 218(3):506-516. PubMed ID: 34523950
    [No Abstract]   [Full Text] [Related]  

  • 15. MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy.
    Horvat N; Veeraraghavan H; Khan M; Blazic I; Zheng J; Capanu M; Sala E; Garcia-Aguilar J; Gollub MJ; Petkovska I
    Radiology; 2018 Jun; 287(3):833-843. PubMed ID: 29514017
    [TBL] [Abstract][Full Text] [Related]  

  • 16. [Application value of texture analysis of magnetic resonance images in prediction of neoadjuvant chemoradiotherapy efficacy for rectal cancer].
    Shu Z; Fang S; Ding Z; Mao D; Pang P; Gong X
    Zhonghua Wei Chang Wai Ke Za Zhi; 2018 Sep; 21(9):1051-1058. PubMed ID: 30269327
    [TBL] [Abstract][Full Text] [Related]  

  • 17. [Predictive value of combination of MRI tumor regression grade and apparent diffusion coefficient for pathological complete remission after neoadjuvant treatment of locally advanced rectal cancer].
    Xu N; Huang FC; Li WL; Luan X; Jiang YM; He B
    Zhonghua Wei Chang Wai Ke Za Zhi; 2021 Apr; 24(4):359-365. PubMed ID: 33878826
    [No Abstract]   [Full Text] [Related]  

  • 18. Preoperative concurrent chemoradiotherapy MRI characteristics favouring pathologic complete response in patients with rectal cancer: Usefulness of MR T2-stage as an ancillary finding for predicting pathologic complete response.
    Lee HJ; Chung WS; An JH; Kim JH
    J Med Imaging Radiat Oncol; 2021 Apr; 65(2):166-174. PubMed ID: 33319450
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Fast T2-Weighted Imaging With Deep Learning-Based Reconstruction: Evaluation of Image Quality and Diagnostic Performance in Patients Undergoing Radical Prostatectomy.
    Park JC; Park KJ; Park MY; Kim MH; Kim JK
    J Magn Reson Imaging; 2022 Jun; 55(6):1735-1744. PubMed ID: 34773449
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Diffusion-weighted imaging: Apparent diffusion coefficient histogram analysis for detecting pathologic complete response to chemoradiotherapy in locally advanced rectal cancer.
    Choi MH; Oh SN; Rha SE; Choi JI; Lee SH; Jang HS; Kim JG; Grimm R; Son Y
    J Magn Reson Imaging; 2016 Jul; 44(1):212-20. PubMed ID: 26666560
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.