BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

102 related articles for article (PubMed ID: 36786336)

  • 1. Quantification of tumorsphere migration with a physics-based machine learning method.
    Vong CK; Wang A; Dragunow M; Park TI; Shim V
    Cytometry A; 2023 Jun; 103(6):518-527. PubMed ID: 36786336
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A tumorsphere model of glioblastoma multiforme with intratumoral heterogeneity for quantitative analysis of cellular migration and drug response.
    Gudbergsson JM; Kostrikov S; Johnsen KB; Fliedner FP; Stolberg CB; Humle N; Hansen AE; Kristensen BW; Christiansen G; Kjær A; Andresen TL; Duroux M
    Exp Cell Res; 2019 Jun; 379(1):73-82. PubMed ID: 30922921
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Using deep learning to segment breast and fibroglandular tissue in MRI volumes.
    Dalmış MU; Litjens G; Holland K; Setio A; Mann R; Karssemeijer N; Gubern-Mérida A
    Med Phys; 2017 Feb; 44(2):533-546. PubMed ID: 28035663
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Semi-supervised learning for automatic segmentation of the knee from MRI with convolutional neural networks.
    Burton W; Myers C; Rullkoetter P
    Comput Methods Programs Biomed; 2020 Jun; 189():105328. PubMed ID: 31958580
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Open-source pipeline for automatic segmentation and microstructural analysis of murine knee subchondral bone.
    Mahdi H; Hardisty M; Fullerton K; Vachhani K; Nam D; Whyne C
    Bone; 2023 Feb; 167():116616. PubMed ID: 36402366
    [TBL] [Abstract][Full Text] [Related]  

  • 6. IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.
    Choi Y; Nam Y; Lee YS; Kim J; Ahn KJ; Jang J; Shin NY; Kim BS; Jeon SS
    Eur J Radiol; 2020 Jul; 128():109031. PubMed ID: 32417712
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Machine learning-based detection of aberrant deep learning segmentations of target and organs at risk for prostate radiotherapy using a secondary segmentation algorithm.
    Claessens M; Vanreusel V; De Kerf G; Mollaert I; Löfman F; Gooding MJ; Brouwer C; Dirix P; Verellen D
    Phys Med Biol; 2022 May; 67(11):. PubMed ID: 35561701
    [No Abstract]   [Full Text] [Related]  

  • 8. Reliability of Semi-Automated Segmentations in Glioblastoma.
    Huber T; Alber G; Bette S; Boeckh-Behrens T; Gempt J; Ringel F; Alberts E; Zimmer C; Bauer JS
    Clin Neuroradiol; 2017 Jun; 27(2):153-161. PubMed ID: 26490369
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of IDH1 Mutation Status in Glioblastoma Using Machine Learning Technique Based on Quantitative Radiomic Data.
    Lee MH; Kim J; Kim ST; Shin HM; You HJ; Choi JW; Seol HJ; Nam DH; Lee JI; Kong DS
    World Neurosurg; 2019 May; 125():e688-e696. PubMed ID: 30735871
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. Patch-based 3D U-Net and transfer learning for longitudinal piglet brain segmentation on MRI.
    Coupeau P; Fasquel JB; Mazerand E; Menei P; Montero-Menei CN; Dinomais M
    Comput Methods Programs Biomed; 2022 Feb; 214():106563. PubMed ID: 34890993
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Improving CCTA-based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation.
    Freiman M; Nickisch H; Prevrhal S; Schmitt H; Vembar M; Maurovich-Horvat P; Donnelly P; Goshen L
    Med Phys; 2017 Mar; 44(3):1040-1049. PubMed ID: 28112409
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A learning-based material decomposition pipeline for multi-energy x-ray imaging.
    Lu Y; Kowarschik M; Huang X; Xia Y; Choi JH; Chen S; Hu S; Ren Q; Fahrig R; Hornegger J; Maier A
    Med Phys; 2019 Feb; 46(2):689-703. PubMed ID: 30508253
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Semantic Segmentation of Spontaneous Intracerebral Hemorrhage, Intraventricular Hemorrhage, and Associated Edema on CT Images Using Deep Learning.
    Kok YE; Pszczolkowski S; Law ZK; Ali A; Krishnan K; Bath PM; Sprigg N; Dineen RA; French AP
    Radiol Artif Intell; 2022 Nov; 4(6):e220096. PubMed ID: 36523645
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware.
    Şişik F; Sert E
    Med Hypotheses; 2020 Mar; 136():109507. PubMed ID: 31812927
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.
    Macyszyn L; Akbari H; Pisapia JM; Da X; Attiah M; Pigrish V; Bi Y; Pal S; Davuluri RV; Roccograndi L; Dahmane N; Martinez-Lage M; Biros G; Wolf RL; Bilello M; O'Rourke DM; Davatzikos C
    Neuro Oncol; 2016 Mar; 18(3):417-25. PubMed ID: 26188015
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Workflow for automatic renal perfusion quantification using ASL-MRI and machine learning.
    Bones IK; Bos C; Moonen C; Hendrikse J; van Stralen M
    Magn Reson Med; 2022 Feb; 87(2):800-809. PubMed ID: 34672029
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Influence of segmentation margin on machine learning-based high-dimensional quantitative CT texture analysis: a reproducibility study on renal clear cell carcinomas.
    Kocak B; Ates E; Durmaz ES; Ulusan MB; Kilickesmez O
    Eur Radiol; 2019 Sep; 29(9):4765-4775. PubMed ID: 30747300
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Inhibitory activities of trichostatin a in U87 glioblastoma cells and tumorsphere-derived cells.
    Sassi Fde A; Caesar L; Jaeger M; Nör C; Abujamra AL; Schwartsmann G; de Farias CB; Brunetto AL; Lopez PL; Roesler R
    J Mol Neurosci; 2014 Sep; 54(1):27-40. PubMed ID: 24464841
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans.
    Barnard R; Tan J; Roller B; Chiles C; Weaver AA; Boutin RD; Kritchevsky SB; Lenchik L
    Acad Radiol; 2019 Dec; 26(12):1686-1694. PubMed ID: 31326311
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.