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Cited by in F6Publishing
For: Priya S, Agarwal A, Ward C, Locke T, Monga V, Bathla G. Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models. Neuroradiol J 2021;34:355-62. [PMID: 33533273 DOI: 10.1177/1971400921990766] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 García-García S, García-Galindo M, Arrese I, Sarabia R, Cepeda S. Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review. Medicina (Kaunas) 2022;58. [PMID: 36556948 DOI: 10.3390/medicina58121746] [Reference Citation Analysis]
2 Peng J, Lu F, Huang J, Zhang J, Gong W, Hu Y, Wang J. Development and validation of a pyradiomics signature to predict initial treatment response and prognosis during transarterial chemoembolization in hepatocellular carcinoma. Front Oncol 2022;12:853254. [DOI: 10.3389/fonc.2022.853254] [Reference Citation Analysis]
3 Jian A, Liu S, Di Ieva A. Artificial Intelligence for Survival Prediction in Brain Tumors on Neuroimaging. Neurosurgery 2022;91:8-26. [PMID: 35348129 DOI: 10.1227/neu.0000000000001938] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
4 Telecan T, Andras I, Crisan N, Giurgiu L, Căta ED, Caraiani C, Lebovici A, Boca B, Balint Z, Diosan L, Lupsor-platon M. More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis—A Systematic Review. JPM 2022;12:983. [DOI: 10.3390/jpm12060983] [Reference Citation Analysis]
5 Chato L, Latifi S. Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients. J Pers Med 2021;11:1336. [PMID: 34945808 DOI: 10.3390/jpm11121336] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
6 Peng J, Huang J, Huang G, Zhang J. Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning. Front Oncol 2021;11:730282. [PMID: 34745952 DOI: 10.3389/fonc.2021.730282] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
7 Priya S, Aggarwal T, Ward C, Bathla G, Jacob M, Gerke A, Hoffman EA, Nagpal P. Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models. Sci Rep 2021;11:12686. [PMID: 34135418 DOI: 10.1038/s41598-021-92155-6] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
8 Priya S, Liu Y, Ward C, Le NH, Soni N, Pillenahalli Maheshwarappa R, Monga V, Zhang H, Sonka M, Bathla G. Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics. Sci Rep 2021;11:10478. [PMID: 34006893 DOI: 10.1038/s41598-021-90032-w] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
9 Priya S, Aggarwal T, Ward C, Bathla G, Jacob M, Gerke A, Hoffman EA, Nagpal P. Radiomics Detection of Pulmonary Hypertension via Texture-Based Assessments of Cardiac MRI: A Machine-Learning Model Comparison-Cardiac MRI Radiomics in Pulmonary Hypertension. J Clin Med 2021;10:1921. [PMID: 33925262 DOI: 10.3390/jcm10091921] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]