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For: Bhandari AP, Liong R, Koppen J, Murthy SV, Lasocki A. Noninvasive Determination of IDH and 1p19q Status of Lower-grade Gliomas Using MRI Radiomics: A Systematic Review. AJNR Am J Neuroradiol 2021;42:94-101. [PMID: 33243896 DOI: 10.3174/ajnr.A6875] [Cited by in Crossref: 24] [Cited by in F6Publishing: 26] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 Kalaroopan D, Lasocki A. MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas. J Med Imaging Radiat Oncol 2023. [PMID: 36919468 DOI: 10.1111/1754-9485.13522] [Reference Citation Analysis]
2 Shen N, Lv W, Li S, Liu D, Xie Y, Zhang J, Zhang J, Jiang J, Jiang R, Zhu W. Noninvasive Evaluation of the Notch Signaling Pathway via Radiomic Signatures Based on Multiparametric MRI in Association With Biological Functions of Patients With Glioma: A Multi-institutional Study. J Magn Reson Imaging 2023;57:884-96. [PMID: 35929909 DOI: 10.1002/jmri.28378] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Bhandari A, Scott L, Weilbach M, Marwah R, Lasocki A. Assessment of artificial intelligence (AI) reporting methodology in glioma MRI studies using the Checklist for AI in Medical Imaging (CLAIM). Neuroradiology 2023. [PMID: 36746792 DOI: 10.1007/s00234-023-03126-9] [Reference Citation Analysis]
4 Hosseini SA, Hosseini E, Hajianfar G, Shiri I, Servaes S, Rosa-Neto P, Godoy L, Nasrallah MP, O'Rourke DM, Mohan S, Chawla S. MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas. Cancers (Basel) 2023;15. [PMID: 36765908 DOI: 10.3390/cancers15030951] [Reference Citation Analysis]
5 Ahn SH, Ahn SS, Park YW, Park CJ, Lee SK. Association of dynamic susceptibility contrast- and dynamic contrast-enhanced magnetic resonance imaging parameters with molecular marker status in lower-grade gliomas: A retrospective study. Neuroradiol J 2023;36:49-58. [PMID: 35532193 DOI: 10.1177/19714009221098369] [Reference Citation Analysis]
6 Spadarella G, Stanzione A, Akinci D'Antonoli T, Andreychenko A, Fanni SC, Ugga L, Kotter E, Cuocolo R. Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol 2023;33:1884-94. [PMID: 36282312 DOI: 10.1007/s00330-022-09187-3] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
7 Lasocki A, Abdalla G, Chow G, Thust SC. Imaging features associated with H3 K27-altered and H3 G34-mutant gliomas: a narrative systematic review. Cancer Imaging 2022;22:63. [DOI: 10.1186/s40644-022-00500-3] [Reference Citation Analysis]
8 Uribe-Cardenas R, Giantini-Larsen AM, Garton A, Juthani RG, Schwartz TH. Innovations in the Diagnosis and Surgical Management of Low-Grade Gliomas. World Neurosurg 2022;166:321-7. [PMID: 36192864 DOI: 10.1016/j.wneu.2022.06.070] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Karabacak M, Ozkara BB, Mordag S, Bisdas S. Deep learning for prediction of isocitrate dehydrogenase mutation in gliomas: a critical approach, systematic review and meta-analysis of the diagnostic test performance using a Bayesian approach. Quant Imaging Med Surg 2022;12:4033-46. [PMID: 35919062 DOI: 10.21037/qims-22-34] [Reference Citation Analysis]
10 Kikuchi K, Togao O, Yamashita K, Momosaka D, Kikuchi Y, Kuga D, Hata N, Mizoguchi M, Yamamoto H, Iwaki T, Hiwatashi A, Ishigami K. Quantitative relaxometry using synthetic MRI could be better than T2-FLAIR mismatch sign for differentiation of IDH-mutant gliomas: a pilot study. Sci Rep 2022;12:9197. [PMID: 35654812 DOI: 10.1038/s41598-022-13036-0] [Reference Citation Analysis]
11 Tozzi AE, Fabozzi F, Eckley M, Croci I, Dell’anna VA, Colantonio E, Mastronuzzi A. Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis. Front Oncol 2022;12:905770. [DOI: 10.3389/fonc.2022.905770] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Lasocki A, Buckland ME, Drummond KJ, Wei H, Xie J, Christie M, Neal A, Gaillard F. Conventional MRI features can predict the molecular subtype of adult grade 2-3 intracranial diffuse gliomas. Neuroradiology 2022. [PMID: 35606654 DOI: 10.1007/s00234-022-02975-0] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Yao J, Hagiwara A, Oughourlian TC, Wang C, Raymond C, Pope WB, Salamon N, Lai A, Ji M, Nghiemphu PL, Liau LM, Cloughesy TF, Ellingson BM. Diagnostic and Prognostic Value of pH- and Oxygen-Sensitive Magnetic Resonance Imaging in Glioma: A Retrospective Study. Cancers (Basel) 2022;14. [PMID: 35626127 DOI: 10.3390/cancers14102520] [Reference Citation Analysis]
14 Simon OB, Jain R, Choi Y, Görg C, Suresh K, Severn C, Ghosh D. A Unified Approach to Analysis of MRI Radiomics of Glioma Using Minimum Spanning Trees. Front Phys 2022;10:783765. [DOI: 10.3389/fphy.2022.783765] [Reference Citation Analysis]
15 Zhang F, Cai H, Liu H, Gao S, Wang B, Hu Y, Cheng H, Liu J, Gao Y, Hong W, Huang M. High Expression of CISD2 in Relation to Adverse Outcome and Abnormal Immune Cell Infiltration in Glioma. Disease Markers 2022;2022:1-25. [DOI: 10.1155/2022/8133505] [Reference Citation Analysis]
16 Afridi M, Jain A, Aboian M, Payabvash S. Brain Tumor Imaging: Applications of Artificial Intelligence. Semin Ultrasound CT MR 2022;43:153-69. [PMID: 35339256 DOI: 10.1053/j.sult.2022.02.005] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Cassinelli Petersen GI, Shatalov J, Verma T, Brim WR, Subramanian H, Brackett A, Bahar RC, Merkaj S, Zeevi T, Staib LH, Cui J, Omuro A, Bronen RA, Malhotra A, Aboian MS. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment. AJNR Am J Neuroradiol 2022. [PMID: 35361577 DOI: 10.3174/ajnr.A7473] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
18 Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022;12:773840. [DOI: 10.3389/fonc.2022.773840] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
19 Huang RY, Pope WB. Imaging Advances for Central Nervous System Tumors. Hematol Oncol Clin North Am 2022;36:43-61. [PMID: 34563433 DOI: 10.1016/j.hoc.2021.08.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
20 Gore S, Jagtap J. Radiogenomic analysis: 1p/19q codeletion based subtyping of low-grade glioma by analysing advanced biomedical texture descriptors. Journal of King Saud University - Computer and Information Sciences 2021. [DOI: 10.1016/j.jksuci.2021.08.024] [Reference Citation Analysis]
21 Manikis GC, Ioannidis GS, Siakallis L, Nikiforaki K, Iv M, Vozlic D, Surlan-Popovic K, Wintermark M, Bisdas S, Marias K. Multicenter DSC-MRI-Based Radiomics Predict IDH Mutation in Gliomas. Cancers (Basel) 2021;13:3965. [PMID: 34439118 DOI: 10.3390/cancers13163965] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 4.5] [Reference Citation Analysis]
22 Smits M. MRI biomarkers in neuro-oncology. Nat Rev Neurol 2021;17:486-500. [PMID: 34149051 DOI: 10.1038/s41582-021-00510-y] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 8.0] [Reference Citation Analysis]
23 van Kempen EJ, Post M, Mannil M, Kusters B, Ter Laan M, Meijer FJA, Henssen DJHA. Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis. Cancers (Basel) 2021;13:2606. [PMID: 34073309 DOI: 10.3390/cancers13112606] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
24 Habib A, Jovanovich N, Hoppe M, Ak M, Mamindla P, R Colen R, Zinn PO. MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift. J Clin Med 2021;10:1411. [PMID: 33915813 DOI: 10.3390/jcm10071411] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]