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Cited by in F6Publishing
For: Zanfardino M, Pane K, Mirabelli P, Salvatore M, Franzese M. TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review. Int J Mol Sci 2019;20:E6033. [PMID: 31795520 DOI: 10.3390/ijms20236033] [Cited by in Crossref: 13] [Cited by in F6Publishing: 11] [Article Influence: 4.3] [Reference Citation Analysis]
Number Citing Articles
1 Stokes K, Castaldo R, Federici C, Pagliara S, Maccaro A, Cappuccio F, Fico G, Salvatore M, Franzese M, Pecchia L. The use of artificial intelligence systems in diagnosis of pneumonia via signs and symptoms: A systematic review. Biomedical Signal Processing and Control 2022;72:103325. [DOI: 10.1016/j.bspc.2021.103325] [Reference Citation Analysis]
2 Castaldo R, Pane K, Nicolai E, Salvatore M, Franzese M. The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status. Cancers (Basel) 2020;12:E518. [PMID: 32102334 DOI: 10.3390/cancers12020518] [Cited by in Crossref: 13] [Cited by in F6Publishing: 8] [Article Influence: 6.5] [Reference Citation Analysis]
3 Zhao B. Understanding Sources of Variation to Improve the Reproducibility of Radiomics. Front Oncol 2021;11:633176. [PMID: 33854969 DOI: 10.3389/fonc.2021.633176] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Zhang Y, Solinas A, Cairo S, Evert M, Chen X, Calvisi DF. Molecular Mechanisms of Hepatoblastoma. Semin Liver Dis 2021;41:28-41. [PMID: 33764483 DOI: 10.1055/s-0040-1722645] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Sheng K. Artificial intelligence in radiotherapy: a technological review. Front Med. 2020;14:431-449. [PMID: 32728877 DOI: 10.1007/s11684-020-0761-1] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
6 Zanfardino M, Castaldo R, Pane K, Affinito O, Aiello M, Salvatore M, Franzese M. MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies. Sci Rep 2021;11:1550. [PMID: 33452365 DOI: 10.1038/s41598-021-81200-z] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
7 Sadri AR, Janowczyk A, Zhou R, Verma R, Beig N, Antunes J, Madabhushi A, Tiwari P, Viswanath SE. Technical Note: MRQy - An open-source tool for quality control of MR imaging data. Med Phys 2020;47:6029-38. [PMID: 33176026 DOI: 10.1002/mp.14593] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
8 Pane K, Mirabelli P, Coppola L, Illiano E, Salvatore M, Franzese M. New Roadmaps for Non-muscle-invasive Bladder Cancer With Unfavorable Prognosis. Front Chem 2020;8:600. [PMID: 32850635 DOI: 10.3389/fchem.2020.00600] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
9 Kawaguchi RK, Takahashi M, Miyake M, Kinoshita M, Takahashi S, Ichimura K, Hamamoto R, Narita Y, Sese J. Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals. Cancers (Basel) 2021;13:3611. [PMID: 34298824 DOI: 10.3390/cancers13143611] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Zhao H, Li W, Lyu P, Zhang X, Liu H, Liang P, Gao J. TCGA-TCIA-Based CT Radiomics Study for Noninvasively Predicting Epstein-Barr Virus Status in Gastric Cancer. AJR Am J Roentgenol 2021;217:124-34. [PMID: 33955777 DOI: 10.2214/AJR.20.23534] [Reference Citation Analysis]
11 Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review. J Med Internet Res 2021;23:e22394. [PMID: 33792552 DOI: 10.2196/22394] [Reference Citation Analysis]
12 Chen T, Li X, Mao Q, Wang Y, Li H, Wang C, Shen Y, Guo E, He Q, Tian J, Zhu M, Wu J, Liang W, Liu H, Yu J, Li G. An artificial intelligence method to assess the tumor microenvironment with treatment outcomes for gastric cancer patients after gastrectomy. J Transl Med 2022;20:100. [PMID: 35189890 DOI: 10.1186/s12967-022-03298-7] [Reference Citation Analysis]
13 Zhang H, Xu L, Zhong Z, Liu Y, Long Y, Zhou S. Lower-Grade Gliomas: Predicting DNA Methylation Subtyping and its Consequences on Survival with MR Features. Acad Radiol 2021;28:e199-208. [PMID: 32241714 DOI: 10.1016/j.acra.2020.02.017] [Reference Citation Analysis]
14 Liu Q, Hu P. Extendable and explainable deep learning for pan-cancer radiogenomics research. Curr Opin Chem Biol 2022;66:102111. [PMID: 34999476 DOI: 10.1016/j.cbpa.2021.102111] [Reference Citation Analysis]