186 related articles for article (PubMed ID: 35599360)
1. Machine learning imaging applications in the differentiation of true tumour progression from treatment-related effects in brain tumours: A systematic review and meta-analysis.
Bhandari A; Marwah R; Smith J; Nguyen D; Bhatti A; Lim CP; Lasocki A
J Med Imaging Radiat Oncol; 2022 Sep; 66(6):781-797. PubMed ID: 35599360
[TBL] [Abstract][Full Text] [Related]
2. Discriminators of pseudoprogression and true progression in high-grade gliomas: A systematic review and meta-analysis.
Taylor C; Ekert JO; Sefcikova V; Fersht N; Samandouras G
Sci Rep; 2022 Aug; 12(1):13258. PubMed ID: 35918373
[TBL] [Abstract][Full Text] [Related]
3. Incidence of Tumour Progression and Pseudoprogression in High-Grade Gliomas: a Systematic Review and Meta-Analysis.
Abbasi AW; Westerlaan HE; Holtman GA; Aden KM; van Laar PJ; van der Hoorn A
Clin Neuroradiol; 2018 Sep; 28(3):401-411. PubMed ID: 28466127
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM).
Bhandari A; Scott L; Weilbach M; Marwah R; Lasocki A
Neuroradiology; 2023 May; 65(5):907-913. PubMed ID: 36746792
[TBL] [Abstract][Full Text] [Related]
6. Diagnostic Performance of Artificial Intelligence-Based Models for the Detection of Early Esophageal Cancers in Barret's Esophagus: A Meta-Analysis of Patient-Based Studies.
Bhatti KM; Khanzada ZS; Kuzman M; Ali SM; Iftikhar SY; Small P
Cureus; 2021 Jun; 13(6):e15447. PubMed ID: 34258114
[TBL] [Abstract][Full Text] [Related]
7. The diagnostic role of diffusional kurtosis imaging in glioma grading and differentiation of gliomas from other intra-axial brain tumours: a systematic review with critical appraisal and meta-analysis.
Abdalla G; Dixon L; Sanverdi E; Machado PM; Kwong JSW; Panovska-Griffiths J; Rojas-Garcia A; Yoneoka D; Veraart J; Van Cauter S; Abdel-Khalek AM; Settein M; Yousry T; Bisdas S
Neuroradiology; 2020 Jul; 62(7):791-802. PubMed ID: 32367349
[TBL] [Abstract][Full Text] [Related]
8. Role of diffusional kurtosis imaging in grading of brain gliomas: a protocol for systematic review and meta-analysis.
Abdalla G; Sanverdi E; Machado PM; Kwong JSW; Panovska-Griffiths J; Rojas-Garcia A; Yoneoka D; Yousry T; Bisdas S
BMJ Open; 2018 Dec; 8(12):e025123. PubMed ID: 30552282
[TBL] [Abstract][Full Text] [Related]
9. Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study.
Vollmuth P; Foltyn M; Huang RY; Galldiks N; Petersen J; Isensee F; van den Bent MJ; Barkhof F; Park JE; Park YW; Ahn SS; Brugnara G; Meredig H; Jain R; Smits M; Pope WB; Maier-Hein K; Weller M; Wen PY; Wick W; Bendszus M
Neuro Oncol; 2023 Mar; 25(3):533-543. PubMed ID: 35917833
[TBL] [Abstract][Full Text] [Related]
10. Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction.
Lost J; Verma T; Jekel L; von Reppert M; Tillmanns N; Merkaj S; Petersen GC; Bahar R; Gordem A; Haider MA; Subramanian H; Brim W; Ikuta I; Omuro A; Conte GM; Marquez-Nostra BV; Avesta A; Bousabarah K; Nabavizadeh A; Kazerooni AF; Aneja S; Bakas S; Lin M; Sabel M; Aboian M
AJNR Am J Neuroradiol; 2023 Oct; 44(10):1126-1134. PubMed ID: 37770204
[TBL] [Abstract][Full Text] [Related]
11. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.
Buchlak QD; Esmaili N; Leveque JC; Bennett C; Farrokhi F; Piccardi M
J Clin Neurosci; 2021 Jul; 89():177-198. PubMed ID: 34119265
[TBL] [Abstract][Full Text] [Related]
12. Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review.
Subramanian H; Dey R; Brim WR; Tillmanns N; Cassinelli Petersen G; Brackett A; Mahajan A; Johnson M; Malhotra A; Aboian M
Front Oncol; 2021; 11():788819. PubMed ID: 35004312
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis.
Nguyen AV; Blears EE; Ross E; Lall RR; Ortega-Barnett J
Neurosurg Focus; 2018 Nov; 45(5):E5. PubMed ID: 30453459
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Towards effective machine learning in medical imaging analysis: A novel approach and expert evaluation of high-grade glioma 'ground truth' simulation on MRI.
Sepehri K; Song X; Proulx R; Hajra SG; Dobberthien B; Liu CC; D'Arcy RCN; Murray D; Krauze AV
Int J Med Inform; 2021 Feb; 146():104348. PubMed ID: 33285357
[TBL] [Abstract][Full Text] [Related]
17. Increase of pseudoprogression and other treatment related effects in low-grade glioma patients treated with proton radiation and temozolomide.
Dworkin M; Mehan W; Niemierko A; Kamran SC; Lamba N; Dietrich J; Martinez-Lage M; Oh KS; Batchelor TT; Wen PY; Loeffler JS; Shih HA
J Neurooncol; 2019 Mar; 142(1):69-77. PubMed ID: 30488294
[TBL] [Abstract][Full Text] [Related]
18. Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.
De Looze C; Beausang A; Cryan J; Loftus T; Buckley PG; Farrell M; Looby S; Reilly R; Brett F; Kearney H
J Neurooncol; 2018 Sep; 139(2):491-499. PubMed ID: 29770897
[TBL] [Abstract][Full Text] [Related]
19. Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review.
Alabi RO; Youssef O; Pirinen M; Elmusrati M; Mäkitie AA; Leivo I; Almangush A
Artif Intell Med; 2021 May; 115():102060. PubMed ID: 34001326
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]