BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

155 related articles for article (PubMed ID: 36738710)

  • 1. Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients.
    Nalepa J; Kotowski K; Machura B; Adamski S; Bozek O; Eksner B; Kokoszka B; Pekala T; Radom M; Strzelczak M; Zarudzki L; Krason A; Arcadu F; Tessier J
    Comput Biol Med; 2023 Mar; 154():106603. PubMed ID: 36738710
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.
    Chang K; Beers AL; Bai HX; Brown JM; Ly KI; Li X; Senders JT; Kavouridis VK; Boaro A; Su C; Bi WL; Rapalino O; Liao W; Shen Q; Zhou H; Xiao B; Wang Y; Zhang PJ; Pinho MC; Wen PY; Batchelor TT; Boxerman JL; Arnaout O; Rosen BR; Gerstner ER; Yang L; Huang RY; Kalpathy-Cramer J
    Neuro Oncol; 2019 Nov; 21(11):1412-1422. PubMed ID: 31190077
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Fully automated brain resection cavity delineation for radiation target volume definition in glioblastoma patients using deep learning.
    Ermiş E; Jungo A; Poel R; Blatti-Moreno M; Meier R; Knecht U; Aebersold DM; Fix MK; Manser P; Reyes M; Herrmann E
    Radiat Oncol; 2020 May; 15(1):100. PubMed ID: 32375839
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.
    Kickingereder P; Isensee F; Tursunova I; Petersen J; Neuberger U; Bonekamp D; Brugnara G; Schell M; Kessler T; Foltyn M; Harting I; Sahm F; Prager M; Nowosielski M; Wick A; Nolden M; Radbruch A; Debus J; Schlemmer HP; Heiland S; Platten M; von Deimling A; van den Bent MJ; Gorlia T; Wick W; Bendszus M; Maier-Hein KH
    Lancet Oncol; 2019 May; 20(5):728-740. PubMed ID: 30952559
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A novel, reproducible, and objective method for volumetric magnetic resonance imaging assessment of enhancing glioblastoma.
    Kanaly CW; Mehta AI; Ding D; Hoang JK; Kranz PG; Herndon JE; Coan A; Crocker I; Waller AF; Friedman AH; Reardon DA; Sampson JH
    J Neurosurg; 2014 Sep; 121(3):536-42. PubMed ID: 25036205
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.
    Jayachandran Preetha C; Meredig H; Brugnara G; Mahmutoglu MA; Foltyn M; Isensee F; Kessler T; Pflüger I; Schell M; Neuberger U; Petersen J; Wick A; Heiland S; Debus J; Platten M; Idbaih A; Brandes AA; Winkler F; van den Bent MJ; Nabors B; Stupp R; Maier-Hein KH; Gorlia T; Tonn JC; Weller M; Wick W; Bendszus M; Vollmuth P
    Lancet Digit Health; 2021 Dec; 3(12):e784-e794. PubMed ID: 34688602
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors.
    Peng J; Kim DD; Patel JB; Zeng X; Huang J; Chang K; Xun X; Zhang C; Sollee J; Wu J; Dalal DJ; Feng X; Zhou H; Zhu C; Zou B; Jin K; Wen PY; Boxerman JL; Warren KE; Poussaint TY; States LJ; Kalpathy-Cramer J; Yang L; Huang RY; Bai HX
    Neuro Oncol; 2022 Feb; 24(2):289-299. PubMed ID: 34174070
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Auto-Segmentation and Classification of Glioma Tumors with the Goals of Treatment Response Assessment Using Deep Learning Based on Magnetic Resonance Imaging.
    Papi Z; Fathi S; Dalvand F; Vali M; Yousefi A; Tabatabaei MH; Amouheidari A; Abedi I
    Neuroinformatics; 2023 Oct; 21(4):641-650. PubMed ID: 37458971
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Volumetric segmentation of glioblastoma progression compared to bidimensional products and clinical radiological reports.
    Berntsen EM; Stensjøen AL; Langlo MS; Simonsen SQ; Christensen P; Moholdt VA; Solheim O
    Acta Neurochir (Wien); 2020 Feb; 162(2):379-387. PubMed ID: 31760532
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Deep learning for glioblastoma segmentation using preoperative magnetic resonance imaging identifies volumetric features associated with survival.
    Wan Y; Rahmat R; Price SJ
    Acta Neurochir (Wien); 2020 Dec; 162(12):3067-3080. PubMed ID: 32662042
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Inter-rater agreement in glioma segmentations on longitudinal MRI.
    Visser M; Müller DMJ; van Duijn RJM; Smits M; Verburg N; Hendriks EJ; Nabuurs RJA; Bot JCJ; Eijgelaar RS; Witte M; van Herk MB; Barkhof F; de Witt Hamer PC; de Munck JC
    Neuroimage Clin; 2019; 22():101727. PubMed ID: 30825711
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Segmenting pediatric optic pathway gliomas from MRI using deep learning.
    Nalepa J; Adamski S; Kotowski K; Chelstowska S; Machnikowska-Sokolowska M; Bozek O; Wisz A; Jurkiewicz E
    Comput Biol Med; 2022 Mar; 142():105237. PubMed ID: 35074737
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Multi-modal glioblastoma segmentation: man versus machine.
    Porz N; Bauer S; Pica A; Schucht P; Beck J; Verma RK; Slotboom J; Reyes M; Wiest R
    PLoS One; 2014; 9(5):e96873. PubMed ID: 24804720
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine.
    Perkuhn M; Stavrinou P; Thiele F; Shakirin G; Mohan M; Garmpis D; Kabbasch C; Borggrefe J
    Invest Radiol; 2018 Nov; 53(11):647-654. PubMed ID: 29863600
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Comparison of 2D (RANO) and volumetric methods for assessment of recurrent glioblastoma treated with bevacizumab-a report from the BELOB trial.
    Gahrmann R; van den Bent M; van der Holt B; Vernhout RM; Taal W; Vos M; de Groot JC; Beerepoot LV; Buter J; Flach ZH; Hanse M; Jasperse B; Smits M
    Neuro Oncol; 2017 Jun; 19(6):853-861. PubMed ID: 28204639
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Response Assessment in Neuro-Oncology criteria, contrast enhancement and perfusion MRI for assessing progression in glioblastoma.
    Tensaouti F; Khalifa J; Lusque A; Plas B; Lotterie JA; Berry I; Laprie A; Cohen-Jonathan Moyal E; Lubrano V
    Neuroradiology; 2017 Oct; 59(10):1013-1020. PubMed ID: 28842741
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Evaluation of Standard Response Assessment in Neuro-Oncology, Modified Response Assessment in Neuro-Oncology, and Immunotherapy Response Assessment in Neuro-Oncology in Newly Diagnosed and Recurrent Glioblastoma.
    Youssef G; Rahman R; Bay C; Wang W; Lim-Fat MJ; Arnaout O; Bi WL; Cagney DN; Chang YS; Cloughesy TF; DeSalvo M; Ellingson BM; Flood TF; Gerstner ER; Gonzalez Castro LN; Guenette JP; Kim AE; Lee EQ; McFaline-Figueroa JR; Potter CA; Reardon DA; Huang RY; Wen PY
    J Clin Oncol; 2023 Jun; 41(17):3160-3171. PubMed ID: 37027809
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Small increases in enhancement on MRI may predict survival post radiotherapy in patients with glioblastoma.
    Gzell CE; Wheeler HR; McCloud P; Kastelan M; Back M
    J Neurooncol; 2016 May; 128(1):67-74. PubMed ID: 26879084
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images.
    Naceur MB; Saouli R; Akil M; Kachouri R
    Comput Methods Programs Biomed; 2018 Nov; 166():39-49. PubMed ID: 30415717
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.