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OpenAlex Citation Counts

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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:

Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis
Xin Chen, Min Zeng, Yichen Tong, et al.
BioMed Research International (2020) Vol. 2020, pp. 1-9
Open Access | Times Cited: 38

Showing 1-25 of 38 citing articles:

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring
Shanaka Ramesh Gunasekara, H. N. T. K. Kaldera, Maheshi B. Dissanayake
Journal of Healthcare Engineering (2021) Vol. 2021, pp. 1-13
Open Access | Times Cited: 70
A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor
Akila Gurunathan, K. Batri
Brain Imaging and Behavior (2022) Vol. 16, Iss. 3, pp. 1410-1427
Open Access | Times Cited: 39
Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis
Fabio Martino Doniselli, Riccardo Pascuzzo, Federica Mazzi, et al.
European Radiology (2024) Vol. 34, Iss. 9, pp. 5802-5815
Open Access | Times Cited: 9
Predicting MGMT Promoter Methylation in Diffuse Gliomas Using Deep Learning with Radiomics
Sixuan Chen, Yue Xu, Meiping Ye, et al.
Journal of Clinical Medicine (2022) Vol. 11, Iss. 12, pp. 3445-3445
Open Access | Times Cited: 31
Preoperative prediction of MGMT promoter methylation in glioblastoma based on multiregional and multi-sequence MRI radiomics analysis
Lanqing Li, Xiao Feng, Shouchao Wang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 5
Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis
Somayeh Farahani, Marjaneh Hejazi, Sahar Moradizeyveh, et al.
Diagnostics (2025) Vol. 15, Iss. 7, pp. 797-797
Open Access
Deep learning for rare disease: A scoping review
Jung Hwan Lee, Cong Liu, Junyoung Kim, et al.
Journal of Biomedical Informatics (2022) Vol. 135, pp. 104227-104227
Open Access | Times Cited: 24
Prediction of O-6-methylguanine-DNA methyltransferase and overall survival of the patients suffering from glioblastoma using MRI-based hybrid radiomics signatures in machine and deep learning framework
Sanjay Saxena, Aaditya Agrawal, Pankaj Kumar, et al.
Neural Computing and Applications (2023) Vol. 35, Iss. 18, pp. 13647-13663
Closed Access | Times Cited: 15
Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI
Augusto Leone, Veronica Di Napoli, Nicola Pio Fochi, et al.
Diagnostics (2025) Vol. 15, Iss. 3, pp. 251-251
Open Access
An Attentive Multi-Modal CNN for Brain Tumor Radiogenomic Classification
Ruyi Qu, Zhifeng Xiao
Information (2022) Vol. 13, Iss. 3, pp. 124-124
Open Access | Times Cited: 16
Application of digital pathology‐based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response
Xiao Fu, Erik Sahai, Anna Wilkins
The Journal of Pathology (2023) Vol. 260, Iss. 5, pp. 578-591
Open Access | Times Cited: 9
AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis
Mullapudi Venkata Sai Samartha, Navneet Kumar Dubey, Biswajit Jena, et al.
Journal of Cancer Research and Clinical Oncology (2024) Vol. 150, Iss. 2
Open Access | Times Cited: 3
Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures
Dongming Liu, Jiu Chen, Xin‐Hua Hu, et al.
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 22
Brain Tumor Radiogenomic Classification of O6-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning
Houneida Sakly, Mourad Ben Saïd, Jayne Seekins, et al.
Cancer Control (2023) Vol. 30
Open Access | Times Cited: 8
Survey of deep learning techniques for disease prediction based on omics data
Xindi Yu, Shusen Zhou, Hailin Zou, et al.
Human Gene (2022) Vol. 35, pp. 201140-201140
Closed Access | Times Cited: 12
Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach
Seong‐O Shim, Lal Hussain, Wajid Aziz, et al.
International Journal of Imaging Systems and Technology (2024) Vol. 34, Iss. 2
Closed Access | Times Cited: 2
Deciphering glioblastoma: Unveiling imaging markers for predicting MGMT promoter methylation status
Eric Hexem, Taha A. Taha, Yaseen Dhemesh, et al.
Current Problems in Cancer (2024) Vol. 54, pp. 101156-101156
Closed Access | Times Cited: 2
Molecular Biomarkers and Recent Liquid Biopsy Testing Progress: A Review of the Application of Biosensors for the Diagnosis of Gliomas
Yuanbin Wu, Xuning Wang, Meng Zhang, et al.
Molecules (2023) Vol. 28, Iss. 15, pp. 5660-5660
Open Access | Times Cited: 6
Comprehensive Genomic Subtyping of Glioma Using Semi-Supervised Multi-Task Deep Learning on Multimodal MRI
Priyanka Tupe-Waghmare, Piyush Malpure, Ketan Kotecha, et al.
IEEE Access (2021) Vol. 9, pp. 167900-167910
Open Access | Times Cited: 15
Radiogenomic Predictors of Recurrence in Glioblastoma—A Systematic Review
Felix Corr, Dustin Grimm, Benjamin Saß, et al.
Journal of Personalized Medicine (2022) Vol. 12, Iss. 3, pp. 402-402
Open Access | Times Cited: 10
Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction
Jan Lost, Tej Verma, Leon Jekel, et al.
American Journal of Neuroradiology (2023) Vol. 44, Iss. 10, pp. 1126-1134
Open Access | Times Cited: 4
To Explore MR Imaging Radiomics for the Differentiation of Orbital Lymphoma and IgG4-Related Ophthalmic Disease
Yuan Ye, Guangyu Chu, Tingting Gong, et al.
BioMed Research International (2021) Vol. 2021, pp. 1-8
Open Access | Times Cited: 10
Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis
Huan Huang, Feifei Wang, Shigang Luo, et al.
Diagnostic and Interventional Radiology (2021) Vol. 27, Iss. 6, pp. 716-724
Open Access | Times Cited: 10
Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology
Lidia Gatto, Enrico Franceschi, Alicia Tosoni, et al.
Biomedicines (2022) Vol. 10, Iss. 12, pp. 3205-3205
Open Access | Times Cited: 7
Deep Learning for Rare Disease: A Scoping Review
Jung Hwan Lee, Cong Liu, Junyoung Kim, et al.
medRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 4
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