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Cited by in F6Publishing
For: 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 Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
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1 Zhang L, Liu X, Xu X, Liu W, Jia Y, Chen W, Fu X, Li Q, Sun X, Zhang Y, Shu S, Zhang X, Xiang R, Chen H, Sun P, Geng D, Yu Z, Liu J, Wang J. An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach. European Journal of Radiology 2022. [DOI: 10.1016/j.ejrad.2022.110639] [Reference Citation Analysis]
2 di Noia C, Grist JT, Riemer F, Lyasheva M, Fabozzi M, Castelli M, Lodi R, Tonon C, Rundo L, Zaccagna F. Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI. Diagnostics 2022;12:2125. [DOI: 10.3390/diagnostics12092125] [Reference Citation Analysis]
3 Guo X, Wu Y, Fang J. Incidence and prognostic factors of patients with benign pituitary tumor based on SEER database. World Neurosurg 2022:S1878-8750(22)00566-6. [PMID: 35504480 DOI: 10.1016/j.wneu.2022.04.113] [Reference Citation Analysis]
4 Krauze AV, Zhuge Y, Zhao R, Tasci E, Camphausen K. AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models. J Biotechnol Biomed 2022;5:1-19. [PMID: 35106480 DOI: 10.26502/jbb.2642-91280046] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]