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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:

Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors
Chaoyue Chen, Xuejin Ou, Jian Wang, et al.
Frontiers in Oncology (2019) Vol. 9
Open Access | Times Cited: 80

Showing 1-25 of 80 citing articles:

Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning
Sebastian R. van der Voort, Fatih Incekara, Maarten M.J. Wijnenga, et al.
Neuro-Oncology (2022) Vol. 25, Iss. 2, pp. 279-289
Open Access | Times Cited: 86
Radioresistance in Glioblastoma and the Development of Radiosensitizers
Md. Yousuf Ali, Claudia R. Oliva, Abu Shadat Mohammod Noman, et al.
Cancers (2020) Vol. 12, Iss. 9, pp. 2511-2511
Open Access | Times Cited: 120
Potential biomarkers and challenges in glioma diagnosis, therapy and prognosis
Liyen Katrina Kan, Kate Drummond, Martin Hunn, et al.
BMJ Neurology Open (2020) Vol. 2, Iss. 2, pp. e000069-e000069
Open Access | Times Cited: 76
Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
Marc Botifoll, Ivan Pinto‐Huguet, Jordi Arbiol
Nanoscale Horizons (2022) Vol. 7, Iss. 12, pp. 1427-1477
Open Access | Times Cited: 64
Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach
Yi Zhou, Xuelei Ma, Ting Zhang, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2021) Vol. 48, Iss. 9, pp. 2904-2913
Closed Access | Times Cited: 61
Evolutionary Model for Brain Cancer-Grading and Classification
Faizan Ullah, Muhammad Nadeem, Mohammad Abrar, et al.
IEEE Access (2023) Vol. 11, pp. 126182-126194
Open Access | Times Cited: 39
Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging
Hao Zhou, Rong Hu, Oliver Y. Tang, et al.
American Journal of Neuroradiology (2020) Vol. 41, Iss. 7, pp. 1279-1285
Open Access | Times Cited: 54
Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results
Chansik An, Yae Won Park, Sung Soo Ahn, et al.
PLoS ONE (2021) Vol. 16, Iss. 8, pp. e0256152-e0256152
Open Access | Times Cited: 52
An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from Chest X-ray
Mahbubunnabi Tamal, Maha Alshammari, Meernah Alabdullah, et al.
Expert Systems with Applications (2021) Vol. 180, pp. 115152-115152
Open Access | Times Cited: 37
Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics
Sarv Priya, Yanan Liu, Caitlin Ward, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 33
Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review
Leon Jekel, Waverly Rose Brim, Marc von Reppert, et al.
Cancers (2022) Vol. 14, Iss. 6, pp. 1369-1369
Open Access | Times Cited: 27
Application of Machine Learning for Classification of Brain Tumors: A Systematic Review and Meta-Analysis
Laís Silva Santana, Jordana Borges Camargo Diniz, Luisa Mothé Glioche Gasparri, et al.
World Neurosurgery (2024) Vol. 186, pp. 204-218.e2
Closed Access | Times Cited: 5
Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings
Seyedmehdi Payabvash, Mariam Aboian, Tarık Tihan, et al.
Frontiers in Oncology (2020) Vol. 10
Open Access | Times Cited: 36
Performance of Radiomics derived morphological features for prediction of aneurysm rupture status
C Ludwig, Alexandra Lauric, Justin A Malek, et al.
Journal of NeuroInterventional Surgery (2020) Vol. 13, Iss. 8, pp. 755-761
Closed Access | Times Cited: 35
Handcrafted and Deep Learning-Based Radiomic Models Can Distinguish GBM from Brain Metastasis
Zhiyuan Liu, Zekun Jiang, Meng Li, et al.
Journal of Oncology (2021) Vol. 2021, pp. 1-10
Open Access | Times Cited: 31
Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas
Quang-Hien Kha, Viet-Huan Le, Truong Nguyen Khanh Hung, et al.
Cancers (2021) Vol. 13, Iss. 21, pp. 5398-5398
Open Access | Times Cited: 28
Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features
Linlin Bo, Zijian Zhang, Zekun Jiang, et al.
Frontiers in Medicine (2021) Vol. 8
Open Access | Times Cited: 28
A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis
Valentina Brancato, Marco Cerrone, Marialuisa Lavitrano, et al.
Cancers (2022) Vol. 14, Iss. 11, pp. 2731-2731
Open Access | Times Cited: 21
Artificial intelligence in the radiomic analysis of glioblastomas: A review, taxonomy, and perspective
Ming Zhu, Sijia Li, Yu Kuang, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 21
Distinguishing glioblastoma from brain metastasis; a systematic review and meta-analysis on the performance of machine learning
Mohammad Amin Habibi, Reza Omid, Shafaq Asgarzade, et al.
The Egyptian Journal of Neurosurgery : the official publication of the Egyptian Society of Neurological Surgeons/Egyptian journal of neurosurgery (2025) Vol. 40, Iss. 1
Open Access
Bayesian-Optimized Convolutional Neural Networks for Classifying Primary Tumor Origin of Brain Metastases from MRI
Jawed Nawabi, Semil Eminovic, Alexander Hartenstein, et al.
Brain Sciences (2025) Vol. 15, Iss. 5, pp. 450-450
Open Access
Comparison of Intraoperative Ultrasound B-Mode and Strain Elastography for the Differentiation of Glioblastomas From Solitary Brain Metastases. An Automated Deep Learning Approach for Image Analysis
Santiago Cepeda, Sergio García-García, Ignacio Arrese, et al.
Frontiers in Oncology (2021) Vol. 10
Open Access | Times Cited: 26
Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation
Chae Jung Park, Yae Won Park, Sung Soo Ahn, et al.
Korean Journal of Radiology (2022) Vol. 23, Iss. 1, pp. 77-77
Open Access | Times Cited: 18
MRI radiomics and potential applications to glioblastoma
Grayson W. Hooper, Daniel Thomas Ginat
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 10
The application value of deep learning in the background of precision medicine in glioblastoma
P. Chen, Ping Wang, Bo Gao
Science Progress (2024) Vol. 107, Iss. 1
Open Access | Times Cited: 3
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