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For: Su X, Chen N, Sun H, Liu Y, Yang X, Wang W, Zhang S, Tan Q, Su J, Gong Q, Yue Q. Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain. Neuro Oncol 2020;22:393-401. [PMID: 31563963 DOI: 10.1093/neuonc/noz184] [Cited by in Crossref: 12] [Cited by in F6Publishing: 30] [Article Influence: 12.0] [Reference Citation Analysis]
Number Citing Articles
1 Lin W, Wang Q, Chen Y, Wang N, Ni Q, Qi C, Wang Q, Zhu Y. Identification of a 6-RBP gene signature for a comprehensive analysis of glioma and ischemic stroke: Cognitive impairment and aging-related hypoxic stress. Front Aging Neurosci 2022;14:951197. [DOI: 10.3389/fnagi.2022.951197] [Reference Citation Analysis]
2 Qin D, Yang G, Jing H, Tan Y, Zhao B, Zhang H. Tumor Progression and Treatment-Related Changes: Radiological Diagnosis Challenges for the Evaluation of Post Treated Glioma. Cancers 2022;14:3771. [DOI: 10.3390/cancers14153771] [Reference Citation Analysis]
3 Zhu M, Li S, Kuang Y, Hill VB, Heimberger AB, Zhai L, Zhai S. Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective. Front Oncol 2022;12:924245. [DOI: 10.3389/fonc.2022.924245] [Reference Citation Analysis]
4 Chen S, Xu Y, Ye M, Li Y, Sun Y, Liang J, Lu J, Wang Z, Zhu Z, Zhang X, Zhang B. Predicting MGMT Promoter Methylation in Diffuse Gliomas Using Deep Learning with Radiomics. J Clin Med 2022;11:3445. [PMID: 35743511 DOI: 10.3390/jcm11123445] [Reference Citation Analysis]
5 Liu X, Li J, Xu Q, Zhang Q, Zhou X, Pan H, Wu N, Lu G, Zhang Z. RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas. Cancers (Basel) 2022;14:2818. [PMID: 35740484 DOI: 10.3390/cancers14122818] [Reference Citation Analysis]
6 Wu W, Wang Y, Xiang J, Li X, Wahafu A, Yu X, Bai X, Yan G, Wang C, Wang N, Du C, Xie W, Wang M, Wang J. A Novel Multi-Omics Analysis Model for Diagnosis and Survival Prediction of Lower-Grade Glioma Patients. Front Oncol 2022;12:729002. [PMID: 35646656 DOI: 10.3389/fonc.2022.729002] [Reference Citation Analysis]
7 Liu Y, Li T, Fan Z, Li Y, Sun Z, Li S, Liang Y, Zhou C, Zhu Q, Zhang H, Liu X, Wang L, Wang Y. Image-Based Differentiation of Intracranial Metastasis From Glioblastoma Using Automated Machine Learning. Front Neurosci 2022;16:855990. [DOI: 10.3389/fnins.2022.855990] [Reference Citation Analysis]
8 Deng D, Liao Y, Zhou J, Cheng L, He P, Wu S, Wang W, Zhou Q. Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features. Front Neurol 2022;13:866274. [DOI: 10.3389/fneur.2022.866274] [Reference Citation Analysis]
9 Guo W, She D, Xing Z, Lin X, Wang F, Song Y, Cao D. Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques. Front Oncol 2022;12:796583. [PMID: 35311083 DOI: 10.3389/fonc.2022.796583] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Huang X, Wang D, Li S, Zhou Q, Zhou J. Advances in computed tomography-based prognostic methods for intracerebral hemorrhage. Neurosurg Rev. [DOI: 10.1007/s10143-022-01760-0] [Reference Citation Analysis]
11 Zhang H, Zhang B, Pan W, Dong X, Li X, Chen J, Wang D, Ji W. Preoperative Contrast-Enhanced MRI in Differentiating Glioblastoma From Low-Grade Gliomas in The Cancer Imaging Archive Database: A Proof-of-Concept Study. Front Oncol 2022;11:761359. [DOI: 10.3389/fonc.2021.761359] [Reference Citation Analysis]
12 Hohm A, Karremann M, Gielen GH, Pietsch T, Warmuth-metz M, Vandergrift LA, Bison B, Stock A, Hoffmann M, Pham M, Kramm CM, Nowak J. Magnetic Resonance Imaging Characteristics of Molecular Subgroups in Pediatric H3 K27M Mutant Diffuse Midline Glioma. Clin Neuroradiol. [DOI: 10.1007/s00062-021-01120-3] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
13 Jaberipour M, Soliman H, Sahgal A, Sadeghi-Naini A. A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning. Sci Rep 2021;11:21620. [PMID: 34732781 DOI: 10.1038/s41598-021-01024-9] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
14 Yi Z, Long L, Zeng Y, Liu Z. Current Advances and Challenges in Radiomics of Brain Tumors. Front Oncol 2021;11:732196. [PMID: 34722274 DOI: 10.3389/fonc.2021.732196] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
15 Li D, Hu R, Li H, Cai Y, Zhang PJ, Wu J, Zhu C, Bai HX. Performance of automatic machine learning versus radiologists in the evaluation of endometrium on computed tomography. Abdom Radiol (NY) 2021;46:5316-24. [PMID: 34286371 DOI: 10.1007/s00261-021-03210-9] [Reference Citation Analysis]
16 Xu Z, Wang X, Zeng S, Ren X, Yan Y, Gong Z. Applying artificial intelligence for cancer immunotherapy. Acta Pharm Sin B 2021;11:3393-405. [PMID: 34900525 DOI: 10.1016/j.apsb.2021.02.007] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 7.0] [Reference Citation Analysis]
17 Abdel Razek AAK, Alksas A, Shehata M, AbdelKhalek A, Abdel Baky K, El-Baz A, Helmy E. Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging. Insights Imaging 2021;12:152. [PMID: 34676470 DOI: 10.1186/s13244-021-01102-6] [Cited by in F6Publishing: 5] [Reference Citation Analysis]
18 Peng WL, Zhang TJ, Shi K, Li HX, Li Y, He S, Li C, Xia D, Xia CC, Li ZL. Automatic machine learning based on native T1 mapping can identify myocardial fibrosis in patients with hypertrophic cardiomyopathy. Eur Radiol 2021. [PMID: 34477909 DOI: 10.1007/s00330-021-08228-7] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
19 Yang P, Zhang H, Lai X, Wang K, Yang Q, Yu D. Accelerating the Selection of Covalent Organic Frameworks with Automated Machine Learning. ACS Omega 2021;6:17149-61. [PMID: 34278102 DOI: 10.1021/acsomega.0c05990] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
20 Liu D, Chen J, Hu X, Yang K, Liu Y, Hu G, Ge H, Zhang W, Liu H. Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures. Front Oncol 2021;11:699265. [PMID: 34295824 DOI: 10.3389/fonc.2021.699265] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
21 Zhuo Z, Qu L, Zhang P, Duan Y, Cheng D, Xu X, Sun T, Ding J, Xie C, Liu X, Haller S, Barkhof F, Zhang L, Liu Y. Prediction of H3K27M-mutant brainstem glioma by amide proton transfer-weighted imaging and its derived radiomics. Eur J Nucl Med Mol Imaging 2021. [PMID: 34131804 DOI: 10.1007/s00259-021-05455-4] [Cited by in F6Publishing: 7] [Reference Citation Analysis]
22 Li Q, Dong F, Jiang B, Zhang M. Exploring MRI Characteristics of Brain Diffuse Midline Gliomas With the H3 K27M Mutation Using Radiomics. Front Oncol 2021;11:646267. [PMID: 34109112 DOI: 10.3389/fonc.2021.646267] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
23 Xie CY, Pang CL, Chan B, Wong EY, Dou Q, Vardhanabhuti V. Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods-A Critical Review of Literature. Cancers (Basel) 2021;13:2469. [PMID: 34069367 DOI: 10.3390/cancers13102469] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
24 Calmon R, Dangouloff-Ros V, Varlet P, Deroulers C, Philippe C, Debily MA, Castel D, Beccaria K, Blauwblomme T, Grevent D, Levy R, Roux CJ, Purcell Y, Saitovitch A, Zilbovicius M, Dufour C, Puget S, Grill J, Boddaert N. Radiogenomics of diffuse intrinsic pontine gliomas (DIPGs): correlation of histological and biological characteristics with multimodal MRI features. Eur Radiol 2021. [PMID: 34003354 DOI: 10.1007/s00330-021-07991-x] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
25 Mustafa A, Rahimi azghadi M. Automated Machine Learning for Healthcare and Clinical Notes Analysis. Computers 2021;10:24. [DOI: 10.3390/computers10020024] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 6.0] [Reference Citation Analysis]
26 Thust S, Micallef C, Okuchi S, Brandner S, Kumar A, Mankad K, Wastling S, Mancini L, Jäger HR, Shankar A. Imaging characteristics of H3 K27M histone-mutant diffuse midline glioma in teenagers and adults. Quant Imaging Med Surg 2021;11:43-56. [PMID: 33392010 DOI: 10.21037/qims-19-954] [Cited by in Crossref: 2] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
27 Tan HB, Xiong F, Jiang YL, Huang WC, Wang Y, Li HH, You T, Fu TT, Lu R, Peng BW. The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia. Sci Rep 2020;10:18926. [PMID: 33144676 DOI: 10.1038/s41598-020-76141-y] [Cited by in Crossref: 7] [Cited by in F6Publishing: 11] [Article Influence: 3.5] [Reference Citation Analysis]
28 Zhou H, Hu R, Tang O, Hu C, Tang L, Chang K, Shen Q, Wu J, Zou B, Xiao B, Boxerman J, Chen W, Huang RY, Yang L, Bai HX, Zhu C. Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging. AJNR Am J Neuroradiol 2020;41:1279-85. [PMID: 32661052 DOI: 10.3174/ajnr.A6621] [Cited by in Crossref: 21] [Cited by in F6Publishing: 19] [Article Influence: 10.5] [Reference Citation Analysis]
29 Chiang J, Diaz AK, Makepeace L, Li X, Han Y, Li Y, Klimo P Jr, Boop FA, Baker SJ, Gajjar A, Merchant TE, Ellison DW, Broniscer A, Patay Z, Tinkle CL. Clinical, imaging, and molecular analysis of pediatric pontine tumors lacking characteristic imaging features of DIPG. Acta Neuropathol Commun 2020;8:57. [PMID: 32326973 DOI: 10.1186/s40478-020-00930-9] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 7.5] [Reference Citation Analysis]
30 Huang RY, Guenette JP. Non-invasive diagnosis of H3 K27M mutant midline glioma. Neuro Oncol 2020;22:309-10. [PMID: 31858137 DOI: 10.1093/neuonc/noz240] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]