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For: Rathore S, Niazi T, Iftikhar MA, Chaddad A. Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning. Cancers (Basel) 2020;12:E578. [PMID: 32131409 DOI: 10.3390/cancers12030578] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 4.5] [Reference Citation Analysis]
Number Citing Articles
1 Zhou H, Xu R, Mei H, Zhang L, Yu Q, Liu R, Fan B, Costa ALF. Application of Enhanced T1WI of MRI Radiomics in Glioma Grading. International Journal of Clinical Practice 2022;2022:1-7. [DOI: 10.1155/2022/3252574] [Reference Citation Analysis]
2 Bhatele KR, Bhadauria SS. Machine learning application in Glioma classification: review and comparison analysis. Arch Computat Methods Eng 2022;29:247-74. [DOI: 10.1007/s11831-021-09572-z] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Truong AH, Sharmanska V, Limbӓck-Stanic C, Grech-Sollars M. Optimization of deep learning methods for visualization of tumor heterogeneity and brain tumor grading through digital pathology. Neurooncol Adv 2020;2:vdaa110. [PMID: 33196039 DOI: 10.1093/noajnl/vdaa110] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
4 Lu X, Li C, Xu W, Wu Y, Wang J, Chen S, Zhang H, Huang H, Huang H, Liu W. Malignant Tumor Purity Reveals the Driven and Prognostic Role of CD3E in Low-Grade Glioma Microenvironment. Front Oncol 2021;11:676124. [PMID: 34557404 DOI: 10.3389/fonc.2021.676124] [Reference Citation Analysis]
5 Sepehri K, Song X, Proulx R, Hajra SG, Dobberthien B, Liu CC, D'Arcy RCN, Murray D, Krauze AV. Towards effective machine learning in medical imaging analysis: A novel approach and expert evaluation of high-grade glioma 'ground truth' simulation on MRI. Int J Med Inform 2021;146:104348. [PMID: 33285357 DOI: 10.1016/j.ijmedinf.2020.104348] [Reference Citation Analysis]
6 Liu WR, Li CY, Xu WH, Liu XJ, Tang HD, Huang HN. Genome-wide analyses of the prognosis-related mRNA alternative splicing landscape and novel splicing factors based on large-scale low grade glioma cohort. Aging (Albany NY) 2020;12:13684-700. [PMID: 32658870 DOI: 10.18632/aging.103491] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
7 Capobianco E, Deng J. Radiomics at a Glance: A Few Lessons Learned from Learning Approaches. Cancers (Basel) 2020;12:E2453. [PMID: 32872466 DOI: 10.3390/cancers12092453] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
8 Khadilkar SP. Colon Cancer Detection Using Hybrid Features and Genetically Optimized Neural Network Classifier. Int J Image Grap 2022;22:2250024. [DOI: 10.1142/s0219467822500243] [Reference Citation Analysis]
9 Wang D, Liu C, Wang X, Liu X, Lan C, Zhao P, Cho WC, Graeber MB, Liu Y. Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma. Front Bioinform 2021;1:718697. [DOI: 10.3389/fbinf.2021.718697] [Reference Citation Analysis]
10 Tao B, Liu Y, Liu H, Zhang Z, Guan Y, Wang H, Shi Y, Zhang J. Prognostic Biomarker KIF18A and Its Correlations With Immune Infiltrates and Mitosis in Glioma. Front Genet 2022;13:852049. [DOI: 10.3389/fgene.2022.852049] [Reference Citation Analysis]
11 Rathore S, Chaddad A, Iftikhar MA, Bilello M, Abdulkadir A. Combining MRI and Histologic Imaging Features for Predicting Overall Survival in Patients with Glioma. Radiol Imaging Cancer 2021;3:e200108. [PMID: 34296969 DOI: 10.1148/rycan.2021200108] [Reference Citation Analysis]