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Cited by in F6Publishing
For: Chen X, Tong Y, Shi Z, Chen H, Yang Z, Wang Y, Chen L, Yu J. Noninvasive molecular diagnosis of craniopharyngioma with MRI-based radiomics approach. BMC Neurol 2019;19:6. [PMID: 30616515 DOI: 10.1186/s12883-018-1216-z] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
1 Zhang R, Gu C, Pu L, Meng Y, Shu J, Cai C. High-throughput screening reveals novel mutations in spinal muscular atrophy patients. Ital J Pediatr 2020;46:166. [PMID: 33148303 DOI: 10.1186/s13052-020-00925-1] [Reference Citation Analysis]
2 Ma G, Kang J, Qiao N, Zhang B, Chen X, Li G, Gao Z, Gui S. Non-Invasive Radiomics Approach Predict Invasiveness of Adamantinomatous Craniopharyngioma Before Surgery. Front Oncol 2020;10:599888. [PMID: 33680925 DOI: 10.3389/fonc.2020.599888] [Reference Citation Analysis]
3 Huang ZS, Xiao X, Li XD, Mo HZ, He WL, Deng YH, Lu LJ, Wu YK, Liu H. Machine Learning-Based Multiparametric Magnetic Resonance Imaging Radiomic Model for Discrimination of Pathological Subtypes of Craniopharyngioma. J Magn Reson Imaging 2021. [PMID: 34085336 DOI: 10.1002/jmri.27761] [Reference Citation Analysis]
4 Forghani R. Precision Digital Oncology: Emerging Role of Radiomics-based Biomarkers and Artificial Intelligence for Advanced Imaging and Characterization of Brain Tumors. Radiol Imaging Cancer 2020;2:e190047. [PMID: 33778721 DOI: 10.1148/rycan.2020190047] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
5 Zhang XY, Yuan K, Fang YL, Wang CL. Growth hormone ameliorates hepatopulmonary syndrome and nonalcoholic steatohepatitis secondary to hypopituitarism in a child: A case report. World J Clin Cases 2022; 10(18): 6211-6217 [DOI: 10.12998/wjcc.v10.i18.6211] [Reference Citation Analysis]
6 Kalasauskas D, Kosterhon M, Keric N, Korczynski O, Kronfeld A, Ringel F, Othman A, Brockmann MA. Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors. Cancers (Basel) 2022;14:836. [PMID: 35159103 DOI: 10.3390/cancers14030836] [Reference Citation Analysis]
7 Qin C, Hu W, Wang X, Ma X. Application of Artificial Intelligence in Diagnosis of Craniopharyngioma. Front Neurol 2021;12:752119. [PMID: 35069406 DOI: 10.3389/fneur.2021.752119] [Reference Citation Analysis]
8 Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020;4:041503. [PMID: 33094213 DOI: 10.1063/5.0011697] [Cited by in Crossref: 9] [Cited by in F6Publishing: 2] [Article Influence: 4.5] [Reference Citation Analysis]
9 Zhu W, Tang T, Yuan S, Chang B, Li S, Chen M. Prediction of CTNNB1 Mutation Status in Pediatric Cystic Adamantinomatous Craniopharyngioma by Using Preoperative Magnetic Resonance Imaging Manifestation. Clin Neurol Neurosurg 2021;200:106347. [PMID: 33160718 DOI: 10.1016/j.clineuro.2020.106347] [Reference Citation Analysis]
10 Koong K, Preda V, Jian A, Liquet-Weiland B, Di Ieva A. Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis. Neuroradiology 2021. [PMID: 34839380 DOI: 10.1007/s00234-021-02845-1] [Reference Citation Analysis]
11 Danilov GV, Shifrin MA, Kotik KV, Ishankulov TA, Orlov YN, Kulikov AS, Potapov AA. Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives. Sovrem Tekhnologii Med 2021;12:111-8. [PMID: 34796024 DOI: 10.17691/stm2020.12.6.12] [Reference Citation Analysis]
12 Peng A, Dai H, Duan H, Chen Y, Huang J, Zhou L, Chen L. A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging. Eur J Radiol 2020;125:108892. [PMID: 32087466 DOI: 10.1016/j.ejrad.2020.108892] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
13 Kwon MR, Shin JH, Park H, Cho H, Hahn SY, Park KW. Radiomics Study of Thyroid Ultrasound for Predicting BRAF Mutation in Papillary Thyroid Carcinoma: Preliminary Results. AJNR Am J Neuroradiol 2020;41:700-5. [PMID: 32273326 DOI: 10.3174/ajnr.A6505] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 5.5] [Reference Citation Analysis]
14 Zhu L, Zhang L, Hu W, Chen H, Li H, Wei S, Chen X, Ma X. A multi-task two-path deep learning system for predicting the invasiveness of craniopharyngioma. Computer Methods and Programs in Biomedicine 2022;216:106651. [DOI: 10.1016/j.cmpb.2022.106651] [Reference Citation Analysis]
15 Chen X, Fan Z, Li KK, Wu G, Yang Z, Gao X, Liu Y, Wu H, Chen H, Tang Q, Chen L, Wang Y, Mao Y, Ng HK, Shi Z, Yu J, Zhou L. Molecular subgrouping of medulloblastoma based on few-shot learning of multitasking using conventional MR images: a retrospective multicenter study. Neurooncol Adv 2020;2:vdaa079. [PMID: 32760911 DOI: 10.1093/noajnl/vdaa079] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
16 Soldozy S, Farzad F, Young S, Yağmurlu K, Norat P, Sokolowski J, Park MS, Jane JA Jr, Syed HR. Pituitary Tumors in the Computational Era, Exploring Novel Approaches to Diagnosis, and Outcome Prediction with Machine Learning. World Neurosurg 2021;146:315-321.e1. [PMID: 32711142 DOI: 10.1016/j.wneu.2020.07.104] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
17 Luo SP, Zhang HW, Yu J, Jiao J, Yang JH, Lei Y, Lin F. A rare case of giant cystic adamantinomatous craniopharyngioma in an adult. Radiol Case Rep 2020;15:846-9. [PMID: 32382364 DOI: 10.1016/j.radcr.2020.04.022] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Takami H, Velásquez C, Asha MJ, Oswari S, Almeida JP, Gentili F. Creative and Innovative Methods and Techniques for the Challenges in the Management of Adult Craniopharyngioma. World Neurosurg 2020;142:601-10. [PMID: 32987616 DOI: 10.1016/j.wneu.2020.05.173] [Reference Citation Analysis]