BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

127 related articles for article (PubMed ID: 38514087)

  • 21. Impact of respiratory motion on
    Chen YH; Kan KY; Liu SH; Lin HH; Lue KH
    J Appl Clin Med Phys; 2023 Dec; 24(12):e14200. PubMed ID: 37937706
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Use of radiomics based on
    Zhou Y; Ma XL; Zhang T; Wang J; Zhang T; Tian R
    Eur J Nucl Med Mol Imaging; 2021 Aug; 48(9):2904-2913. PubMed ID: 33547553
    [TBL] [Abstract][Full Text] [Related]  

  • 23. An [
    Meng N; Feng P; Yu X; Wu Y; Fu F; Li Z; Luo Y; Tan H; Yuan J; Yang Y; Wang Z; Wang M
    Eur Radiol; 2024 Jan; 34(1):318-329. PubMed ID: 37530809
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts.
    Grahovac M; Spielvogel CP; Krajnc D; Ecsedi B; Traub-Weidinger T; Rasul S; Kluge K; Zhao M; Li X; Hacker M; Haug A; Papp L
    Eur J Nucl Med Mol Imaging; 2023 May; 50(6):1607-1620. PubMed ID: 36738311
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Prognostic value of pretreatment ¹⁸F-FDG PET/CT and human papillomavirus type 16 testing in locally advanced oropharyngeal squamous cell carcinoma.
    Cheng NM; Chang JT; Huang CG; Tsan DL; Ng SH; Wang HM; Liao CT; Lin CY; Hsu CL; Yen TC
    Eur J Nucl Med Mol Imaging; 2012 Nov; 39(11):1673-84. PubMed ID: 22854984
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Application of 18 F-fluorodeoxyglucose PET/CT radiomic features and machine learning to predict early recurrence of non-small cell lung cancer after curative-intent therapy.
    Park SB; Kim KU; Park YW; Hwang JH; Lim CH
    Nucl Med Commun; 2023 Feb; 44(2):161-168. PubMed ID: 36458424
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic [
    de Jesus FM; Yin Y; Mantzorou-Kyriaki E; Kahle XU; de Haas RJ; Yakar D; Glaudemans AWJM; Noordzij W; Kwee TC; Nijland M
    Eur J Nucl Med Mol Imaging; 2022 Apr; 49(5):1535-1543. PubMed ID: 34850248
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Development and validation of an
    Wang H; Zhao S; Li L; Tian R
    Eur Radiol; 2020 Oct; 30(10):5578-5587. PubMed ID: 32435928
    [TBL] [Abstract][Full Text] [Related]  

  • 29. External validation of an
    Mori M; Deantoni C; Olivieri M; Spezi E; Chiara A; Baroni S; Picchio M; Del Vecchio A; Di Muzio NG; Fiorino C; Dell'Oca I
    Eur J Nucl Med Mol Imaging; 2023 Apr; 50(5):1329-1336. PubMed ID: 36604325
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma.
    Bogowicz M; Riesterer O; Stark LS; Studer G; Unkelbach J; Guckenberger M; Tanadini-Lang S
    Acta Oncol; 2017 Nov; 56(11):1531-1536. PubMed ID: 28820287
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status.
    Suh CH; Lee KH; Choi YJ; Chung SR; Baek JH; Lee JH; Yun J; Ham S; Kim N
    Sci Rep; 2020 Oct; 10(1):17525. PubMed ID: 33067484
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma.
    Zhong J; Frood R; Brown P; Nelstrop H; Prestwich R; McDermott G; Currie S; Vaidyanathan S; Scarsbrook AF
    Clin Radiol; 2021 Jan; 76(1):78.e9-78.e17. PubMed ID: 33036778
    [TBL] [Abstract][Full Text] [Related]  

  • 33. A machine learning approach using
    Qi WX; Li S; Xiao J; Li H; Chen J; Zhao S
    Front Immunol; 2024; 15():1351750. PubMed ID: 38352868
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Robustness of Radiomic Features in [
    Lu L; Lv W; Jiang J; Ma J; Feng Q; Rahmim A; Chen W
    Mol Imaging Biol; 2016 Dec; 18(6):935-945. PubMed ID: 27324369
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Do 18F-FDG PET/CT parameters in oropharyngeal and oral cavity squamous cell carcinomas indicate HPV status?
    Kendi AT; Magliocca K; Corey A; Nickleach DC; Galt J; Higgins K; Beitler JJ; El-Deiry MW; Wadsworth JT; Hudgins PA; Saba NF; Schuster DM
    Clin Nucl Med; 2015 Mar; 40(3):e196-200. PubMed ID: 25608156
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Stacking Ensemble Learning-Based [
    Zhao S; Wang J; Jin C; Zhang X; Xue C; Zhou R; Zhong Y; Liu Y; He X; Zhou Y; Xu C; Zhang L; Qian W; Zhang H; Zhang X; Tian M
    J Nucl Med; 2023 Oct; 64(10):1603-1609. PubMed ID: 37500261
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Heterogeneity and irregularity of pretreatment
    Cheng NM; Fang YD; Tsan DL; Lee LY; Chang JT; Wang HM; Ng SH; Liao CT; Yang LY; Yen TC
    Oral Oncol; 2018 Mar; 78():156-162. PubMed ID: 29496044
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms.
    Shiri I; Maleki H; Hajianfar G; Abdollahi H; Ashrafinia S; Hatt M; Zaidi H; Oveisi M; Rahmim A
    Mol Imaging Biol; 2020 Aug; 22(4):1132-1148. PubMed ID: 32185618
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Machine learning-based CT texture analysis to predict HPV status in oropharyngeal squamous cell carcinoma: comparison of 2D and 3D segmentation.
    Ren J; Yuan Y; Qi M; Tao X
    Eur Radiol; 2020 Dec; 30(12):6858-6866. PubMed ID: 32591885
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images.
    Fujima N; Andreu-Arasa VC; Meibom SK; Mercier GA; Truong MT; Hirata K; Yasuda K; Kano S; Homma A; Kudo K; Sakai O
    BMC Cancer; 2021 Aug; 21(1):900. PubMed ID: 34362317
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.