269 related articles for article (PubMed ID: 32129893)
1. Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma.
Akbari H; Rathore S; Bakas S; Nasrallah MP; Shukla G; Mamourian E; Rozycki M; Bagley SJ; Rudie JD; Flanders AE; Dicker AP; Desai AS; O'Rourke DM; Brem S; Lustig R; Mohan S; Wolf RL; Bilello M; Martinez-Lage M; Davatzikos C
Cancer; 2020 Jun; 126(11):2625-2636. PubMed ID: 32129893
[TBL] [Abstract][Full Text] [Related]
2. Validation of multiparametric MRI based prediction model in identification of pseudoprogression in glioblastomas.
de Godoy LL; Mohan S; Wang S; Nasrallah MP; Sakai Y; O'Rourke DM; Bagley S; Desai A; Loevner LA; Poptani H; Chawla S
J Transl Med; 2023 Apr; 21(1):287. PubMed ID: 37118754
[TBL] [Abstract][Full Text] [Related]
3. Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation.
Qian X; Tan H; Zhang J; Zhao W; Chan MD; Zhou X
Med Phys; 2016 Nov; 43(11):5889. PubMed ID: 27806598
[TBL] [Abstract][Full Text] [Related]
4. A deep learning model for discriminating true progression from pseudoprogression in glioblastoma patients.
Moassefi M; Faghani S; Conte GM; Kowalchuk RO; Vahdati S; Crompton DJ; Perez-Vega C; Cabreja RAD; Vora SA; Quiñones-Hinojosa A; Parney IF; Trifiletti DM; Erickson BJ
J Neurooncol; 2022 Sep; 159(2):447-455. PubMed ID: 35852738
[TBL] [Abstract][Full Text] [Related]
5. Three-dimensional echo planar spectroscopic imaging for differentiation of true progression from pseudoprogression in patients with glioblastoma.
Verma G; Chawla S; Mohan S; Wang S; Nasrallah M; Sheriff S; Desai A; Brem S; O'Rourke DM; Wolf RL; Maudsley AA; Poptani H
NMR Biomed; 2019 Feb; 32(2):e4042. PubMed ID: 30556932
[TBL] [Abstract][Full Text] [Related]
6. Metabolic and physiologic magnetic resonance imaging in distinguishing true progression from pseudoprogression in patients with glioblastoma.
Chawla S; Bukhari S; Afridi OM; Wang S; Yadav SK; Akbari H; Verma G; Nath K; Haris M; Bagley S; Davatzikos C; Loevner LA; Mohan S
NMR Biomed; 2022 Jul; 35(7):e4719. PubMed ID: 35233862
[TBL] [Abstract][Full Text] [Related]
7. Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma.
Fathi Kazerooni A; Akbari H; Shukla G; Badve C; Rudie JD; Sako C; Rathore S; Bakas S; Pati S; Singh A; Bergman M; Ha SM; Kontos D; Nasrallah M; Bagley SJ; Lustig RA; O'Rourke DM; Sloan AE; Barnholtz-Sloan JS; Mohan S; Bilello M; Davatzikos C
JCO Clin Cancer Inform; 2020 Mar; 4():234-244. PubMed ID: 32191542
[TBL] [Abstract][Full Text] [Related]
8. In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature.
Akbari H; Bakas S; Pisapia JM; Nasrallah MP; Rozycki M; Martinez-Lage M; Morrissette JJD; Dahmane N; O'Rourke DM; Davatzikos C
Neuro Oncol; 2018 Jul; 20(8):1068-1079. PubMed ID: 29617843
[TBL] [Abstract][Full Text] [Related]
9. Pseudoprogression in GBM versus true progression in patients with glioblastoma: A multiapproach analysis.
Sidibe I; Tensaouti F; Gilhodes J; Cabarrou B; Filleron T; Desmoulin F; Ken S; Noël G; Truc G; Sunyach MP; Charissoux M; Magné N; Lotterie JA; Roques M; Péran P; Cohen-Jonathan Moyal E; Laprie A
Radiother Oncol; 2023 Apr; 181():109486. PubMed ID: 36706959
[TBL] [Abstract][Full Text] [Related]
10. DC-AL GAN: Pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and AlexNet.
Li M; Tang H; Chan MD; Zhou X; Qian X
Med Phys; 2020 Mar; 47(3):1139-1150. PubMed ID: 31885094
[TBL] [Abstract][Full Text] [Related]
11. Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma.
Elshafeey N; Kotrotsou A; Hassan A; Elshafei N; Hassan I; Ahmed S; Abrol S; Agarwal A; El Salek K; Bergamaschi S; Acharya J; Moron FE; Law M; Fuller GN; Huse JT; Zinn PO; Colen RR
Nat Commun; 2019 Jul; 10(1):3170. PubMed ID: 31320621
[TBL] [Abstract][Full Text] [Related]
12. Differentiation of Pseudoprogression from True Progressionin Glioblastoma Patients after Standard Treatment: A Machine Learning Strategy Combinedwith Radiomics Features from T
Sun YZ; Yan LF; Han Y; Nan HY; Xiao G; Tian Q; Pu WH; Li ZY; Wei XC; Wang W; Cui GB
BMC Med Imaging; 2021 Feb; 21(1):17. PubMed ID: 33535988
[TBL] [Abstract][Full Text] [Related]
13. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.
Nakagawa M; Nakaura T; Namimoto T; Kitajima M; Uetani H; Tateishi M; Oda S; Utsunomiya D; Makino K; Nakamura H; Mukasa A; Hirai T; Yamashita Y
Eur J Radiol; 2018 Nov; 108():147-154. PubMed ID: 30396648
[TBL] [Abstract][Full Text] [Related]
14. Radiomics features to distinguish glioblastoma from primary central nervous system lymphoma on multi-parametric MRI.
Kim Y; Cho HH; Kim ST; Park H; Nam D; Kong DS
Neuroradiology; 2018 Dec; 60(12):1297-1305. PubMed ID: 30232517
[TBL] [Abstract][Full Text] [Related]
15. Combined analysis of MGMT methylation and dynamic-susceptibility-contrast MRI for the distinction between early and pseudo-progression in glioblastoma patients.
Bani-Sadr A; Berner LP; Barritault M; Chamard L; Bidet CM; Eker OF; Hermier M; Guyotat J; Jouanneau E; Meyronet D; Gouttard S; D'Hombres A; Iziquierdo C; Honnorat J; Berthezène Y; Ducray F
Rev Neurol (Paris); 2019 Oct; 175(9):534-543. PubMed ID: 31208813
[TBL] [Abstract][Full Text] [Related]
16. Incidence, molecular characteristics, and imaging features of "clinically-defined pseudoprogression" in newly diagnosed glioblastoma treated with chemoradiation.
Hagiwara A; Schlossman J; Shabani S; Raymond C; Tatekawa H; Abrey LE; Garcia J; Chinot O; Saran F; Nishikawa R; Henriksson R; Mason WP; Wick W; Cloughesy TF; Ellingson BM
J Neurooncol; 2022 Sep; 159(3):509-518. PubMed ID: 35842871
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time.
Liao X; Cai B; Tian B; Luo Y; Song W; Li Y
J Cell Mol Med; 2019 Jun; 23(6):4375-4385. PubMed ID: 31001929
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
