258 related articles for article (PubMed ID: 35361577)
21. Prognostic models for predicting relapse or recurrence of major depressive disorder in adults.
Moriarty AS; Meader N; Snell KI; Riley RD; Paton LW; Chew-Graham CA; Gilbody S; Churchill R; Phillips RS; Ali S; McMillan D
Cochrane Database Syst Rev; 2021 May; 5(5):CD013491. PubMed ID: 33956992
[TBL] [Abstract][Full Text] [Related]
22. MRI features predict p53 status in lower-grade gliomas via a machine-learning approach.
Li Y; Qian Z; Xu K; Wang K; Fan X; Li S; Jiang T; Liu X; Wang Y
Neuroimage Clin; 2018; 17():306-311. PubMed ID: 29527478
[TBL] [Abstract][Full Text] [Related]
23. Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis.
van Kempen EJ; Post M; Mannil M; Witkam RL; Ter Laan M; Patel A; Meijer FJA; Henssen D
Eur Radiol; 2021 Dec; 31(12):9638-9653. PubMed ID: 34019128
[TBL] [Abstract][Full Text] [Related]
24. Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.
Zhao J; Huang Y; Song Y; Xie D; Hu M; Qiu H; Chu J
Eur Radiol; 2020 Aug; 30(8):4664-4674. PubMed ID: 32193643
[TBL] [Abstract][Full Text] [Related]
25. Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis.
Hickman SE; Woitek R; Le EPV; Im YR; Mouritsen Luxhøj C; Aviles-Rivero AI; Baxter GC; MacKay JW; Gilbert FJ
Radiology; 2022 Jan; 302(1):88-104. PubMed ID: 34665034
[TBL] [Abstract][Full Text] [Related]
26. Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis.
Wu JH; Liu TYA; Hsu WT; Ho JH; Lee CC
J Med Internet Res; 2021 Jul; 23(7):e23863. PubMed ID: 34407500
[TBL] [Abstract][Full Text] [Related]
27. Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.
Inano R; Oishi N; Kunieda T; Arakawa Y; Yamao Y; Shibata S; Kikuchi T; Fukuyama H; Miyamoto S
Neuroimage Clin; 2014; 5():396-407. PubMed ID: 25180159
[TBL] [Abstract][Full Text] [Related]
28. Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.
Choi YS; Ahn SS; Chang JH; Kang SG; Kim EH; Kim SH; Jain R; Lee SK
Eur Radiol; 2020 Jul; 30(7):3834-3842. PubMed ID: 32162004
[TBL] [Abstract][Full Text] [Related]
29. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies.
Nagendran M; Chen Y; Lovejoy CA; Gordon AC; Komorowski M; Harvey H; Topol EJ; Ioannidis JPA; Collins GS; Maruthappu M
BMJ; 2020 Mar; 368():m689. PubMed ID: 32213531
[TBL] [Abstract][Full Text] [Related]
30. Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis.
Sufriyana H; Husnayain A; Chen YL; Kuo CY; Singh O; Yeh TY; Wu YW; Su EC
JMIR Med Inform; 2020 Nov; 8(11):e16503. PubMed ID: 33200995
[TBL] [Abstract][Full Text] [Related]
31. Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature.
Li Y; Liu X; Qian Z; Sun Z; Xu K; Wang K; Fan X; Zhang Z; Li S; Wang Y; Jiang T
Eur Radiol; 2018 Jul; 28(7):2960-2968. PubMed ID: 29404769
[TBL] [Abstract][Full Text] [Related]
32. Prognostic models for newly-diagnosed chronic lymphocytic leukaemia in adults: a systematic review and meta-analysis.
Kreuzberger N; Damen JA; Trivella M; Estcourt LJ; Aldin A; Umlauff L; Vazquez-Montes MD; Wolff R; Moons KG; Monsef I; Foroutan F; Kreuzer KA; Skoetz N
Cochrane Database Syst Rev; 2020 Jul; 7(7):CD012022. PubMed ID: 32735048
[TBL] [Abstract][Full Text] [Related]
33. Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis.
Nakamoto T; Takahashi W; Haga A; Takahashi S; Kiryu S; Nawa K; Ohta T; Ozaki S; Nozawa Y; Tanaka S; Mukasa A; Nakagawa K
Sci Rep; 2019 Dec; 9(1):19411. PubMed ID: 31857632
[TBL] [Abstract][Full Text] [Related]
34. Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.
Jian A; Jang K; Manuguerra M; Liu S; Magnussen J; Di Ieva A
Neurosurgery; 2021 Jun; 89(1):31-44. PubMed ID: 33826716
[TBL] [Abstract][Full Text] [Related]
35. Beyond the black stump: rapid reviews of health research issues affecting regional, rural and remote Australia.
Osborne SR; Alston LV; Bolton KA; Whelan J; Reeve E; Wong Shee A; Browne J; Walker T; Versace VL; Allender S; Nichols M; Backholer K; Goodwin N; Lewis S; Dalton H; Prael G; Curtin M; Brooks R; Verdon S; Crockett J; Hodgins G; Walsh S; Lyle DM; Thompson SC; Browne LJ; Knight S; Pit SW; Jones M; Gillam MH; Leach MJ; Gonzalez-Chica DA; Muyambi K; Eshetie T; Tran K; May E; Lieschke G; Parker V; Smith A; Hayes C; Dunlop AJ; Rajappa H; White R; Oakley P; Holliday S
Med J Aust; 2020 Dec; 213 Suppl 11():S3-S32.e1. PubMed ID: 33314144
[TBL] [Abstract][Full Text] [Related]
36. Current state of radiomics in pediatric neuro-oncology practice: a systematic review.
Albalkhi I; Bhatia A; Lösch N; Goetti R; Mankad K
Pediatr Radiol; 2023 Sep; 53(10):2079-2091. PubMed ID: 37195305
[TBL] [Abstract][Full Text] [Related]
37. Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features.
Bernatz S; Ackermann J; Mandel P; Kaltenbach B; Zhdanovich Y; Harter PN; Döring C; Hammerstingl R; Bodelle B; Smith K; Bucher A; Albrecht M; Rosbach N; Basten L; Yel I; Wenzel M; Bankov K; Koch I; Chun FK; Köllermann J; Wild PJ; Vogl TJ
Eur Radiol; 2020 Dec; 30(12):6757-6769. PubMed ID: 32676784
[TBL] [Abstract][Full Text] [Related]
38. Machine Learning-Based Prediction Models for Delirium: A Systematic Review and Meta-Analysis.
Xie Q; Wang X; Pei J; Wu Y; Guo Q; Su Y; Yan H; Nan R; Chen H; Dou X
J Am Med Dir Assoc; 2022 Oct; 23(10):1655-1668.e6. PubMed ID: 35922015
[TBL] [Abstract][Full Text] [Related]
39. Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.
Andaur Navarro CL; Damen JAAG; Takada T; Nijman SWJ; Dhiman P; Ma J; Collins GS; Bajpai R; Riley RD; Moons KG; Hooft L
BMJ Open; 2020 Nov; 10(11):e038832. PubMed ID: 33177137
[TBL] [Abstract][Full Text] [Related]
40. Systematic review and modelling of the cost-effectiveness of cardiac magnetic resonance imaging compared with current existing testing pathways in ischaemic cardiomyopathy.
Campbell F; Thokala P; Uttley LC; Sutton A; Sutton AJ; Al-Mohammad A; Thomas SM
Health Technol Assess; 2014 Sep; 18(59):1-120. PubMed ID: 25265259
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
