OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!
If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.
Requested Article:
Non-Invasive Prediction of Survival Time of Midline Glioma Patients Using Machine Learning on Multiparametric MRI Radiomics Features
Dabiao Deng, Yuting Liao, Jiangfen Zhou, et al.
Frontiers in Neurology (2022) Vol. 13
Open Access | Times Cited: 7
Dabiao Deng, Yuting Liao, Jiangfen Zhou, et al.
Frontiers in Neurology (2022) Vol. 13
Open Access | Times Cited: 7
Showing 7 citing articles:
Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review
Mehnaz Tabassum, Abdulla Al Suman, Eric Suero Molina, et al.
Cancers (2023) Vol. 15, Iss. 15, pp. 3845-3845
Open Access | Times Cited: 24
Mehnaz Tabassum, Abdulla Al Suman, Eric Suero Molina, et al.
Cancers (2023) Vol. 15, Iss. 15, pp. 3845-3845
Open Access | Times Cited: 24
The Performance of Machine Learning for Prediction of H3K27 M Mutation in Midline Gliomas: A Systematic Review and Meta-Analysis
Mohammad Amin Habibi, Fateme Aghaei, Zohreh Tajabadi, et al.
World Neurosurgery (2023) Vol. 186, pp. e7-e19
Closed Access | Times Cited: 13
Mohammad Amin Habibi, Fateme Aghaei, Zohreh Tajabadi, et al.
World Neurosurgery (2023) Vol. 186, pp. e7-e19
Closed Access | Times Cited: 13
Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation
Maria-Fatima Chilaca-Rosas, Melissa Garcia-Lezama, Sergio Moreno‐Jiménez, et al.
Diagnostics (2023) Vol. 13, Iss. 5, pp. 849-849
Open Access | Times Cited: 7
Maria-Fatima Chilaca-Rosas, Melissa Garcia-Lezama, Sergio Moreno‐Jiménez, et al.
Diagnostics (2023) Vol. 13, Iss. 5, pp. 849-849
Open Access | Times Cited: 7
Identification of Radiomic Signatures in Brain MRI Sequences T1 and T2 That Differentiate Tumor Regions of Midline Gliomas with H3.3K27M Mutation
Maria-Fatima Chilaca-Rosas, Manuel-Tadeo Contreras-Aguilar, Melissa Garcia-Lezama, et al.
Diagnostics (2023) Vol. 13, Iss. 16, pp. 2669-2669
Open Access | Times Cited: 4
Maria-Fatima Chilaca-Rosas, Manuel-Tadeo Contreras-Aguilar, Melissa Garcia-Lezama, et al.
Diagnostics (2023) Vol. 13, Iss. 16, pp. 2669-2669
Open Access | Times Cited: 4
Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated
Guido Fròsina
Cancers (2024) Vol. 16, Iss. 8, pp. 1566-1566
Open Access | Times Cited: 1
Guido Fròsina
Cancers (2024) Vol. 16, Iss. 8, pp. 1566-1566
Open Access | Times Cited: 1
From Imaging to Prognosis: Crafting Clinical Nomograms Based on a Multi-Sequence MRI Radiomics Model for Non- Invasive Glioma Survival Prediction
Xiao Fan, Hongjian Zhang, Biao Huang, et al.
Research Square (Research Square) (2024)
Open Access
Xiao Fan, Hongjian Zhang, Biao Huang, et al.
Research Square (Research Square) (2024)
Open Access
Artificial intelligence in cancer imaging for diagnosis and evaluation of treatment response
Qi Yang, Yu Fu, Zhongping Chen, et al.
Radiology Science (2022) Vol. 1, Iss. 1
Open Access
Qi Yang, Yu Fu, Zhongping Chen, et al.
Radiology Science (2022) Vol. 1, Iss. 1
Open Access
