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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:

Machine Learning in Meningioma MRI: Past to Present. A Narrative Review
Eleftherios Neromyliotis, Theodosis Κalamatianos, Athanasios Paschalis, et al.
Journal of Magnetic Resonance Imaging (2020) Vol. 55, Iss. 1, pp. 48-60
Closed Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

EANO guideline on the diagnosis and management of meningiomas
Roland Goldbrunner, Pantelis Stavrinou, Michael D. Jenkinson, et al.
Neuro-Oncology (2021) Vol. 23, Iss. 11, pp. 1821-1834
Open Access | Times Cited: 458
Clinical applications of artificial intelligence and radiomics in neuro-oncology imaging
Ahmed Abdel Khalek Abdel Razek, Ahmed Alksas, Mohamed Shehata, et al.
Insights into Imaging (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 98
Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis
Roozbeh Tavanaei, Mohammadhosein Akhlaghpasand, Alireza Alikhani, et al.
Neurosurgical Review (2025) Vol. 48, Iss. 1
Closed Access | Times Cited: 1
Fully Automated MRI Segmentation and Volumetric Measurement of Intracranial Meningioma Using Deep Learning
Ho Kang, Joseph Nathanael Witanto, Kevin Pratama, et al.
Journal of Magnetic Resonance Imaging (2022) Vol. 57, Iss. 3, pp. 871-881
Closed Access | Times Cited: 24
Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting
David Bouget, André Pedersen, Asgeir Store Jakola, et al.
Frontiers in Neurology (2022) Vol. 13
Open Access | Times Cited: 21
Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning
Yohan Jun, Yae Won Park, Hyungseob Shin, et al.
European Radiology (2023) Vol. 33, Iss. 9, pp. 6124-6133
Closed Access | Times Cited: 12
Deep learning–based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features
Liping Yang, Tianzuo Wang, Jinling Zhang, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 4
Deep learning frameworks for MRI-based diagnosis of neurological disorders: a systematic review and meta-analysis
Syed Saad Azhar Ali, Khuhed Memon, Norashikin Yahya, et al.
Artificial Intelligence Review (2025) Vol. 58, Iss. 6
Open Access
Explainable and visualizable machine learning models to predict biochemical recurrence of prostate cancer
Wenhao Lu, Lin Zhao, Shenfan Wang, et al.
Clinical & Translational Oncology (2024) Vol. 26, Iss. 9, pp. 2369-2379
Closed Access | Times Cited: 3
Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review
Paul Windisch, Carole Koechli, Susanne Rogers, et al.
Cancers (2022) Vol. 14, Iss. 11, pp. 2676-2676
Open Access | Times Cited: 15
MRI-based machine learning models predict the malignant biological behavior of meningioma
Maoyuan Li, Luzhou Liu, Jie Qi, et al.
BMC Medical Imaging (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 8
Texture Analysis in Brain Tumor MR Imaging
Akira Kunimatsu, Koichiro Yasaka, Hiroyuki Akai, et al.
Magnetic Resonance in Medical Sciences (2021) Vol. 21, Iss. 1, pp. 95-109
Open Access | Times Cited: 20
Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting
David Bouget, Demah Alsinan, Valeria Gaitan, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 7
Traditional Machine Learning Methods versus Deep Learning for Meningioma Classification, Grading, Outcome Prediction, and Segmentation: A Systematic Review and Meta-Analysis
Krish Maniar, Philipp Lassarén, Aakanksha Rana, et al.
World Neurosurgery (2023) Vol. 179, pp. e119-e134
Closed Access | Times Cited: 4
Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm
Sana Mohammadi, Sadegh Ghaderi, Kayvan Ghaderi, et al.
International Journal of Surgery Case Reports (2023) Vol. 111, pp. 108818-108818
Open Access | Times Cited: 4
Exploring machine learning applications in Meningioma Research (2004–2023)
Liwei Zhong, Kun‐Shan Chen, Hua-Biao Yang, et al.
Heliyon (2024) Vol. 10, Iss. 12, pp. e32596-e32596
Open Access | Times Cited: 1
Deep-learning model for diagnostic clue: detecting the dural tail sign for meningiomas on contrast-enhanced T1 weighted images
Hyunmin Kim, Hyug‐Gi Kim, Jang-Hoon Oh, et al.
Quantitative Imaging in Medicine and Surgery (2023) Vol. 13, Iss. 12, pp. 8132-8143
Open Access | Times Cited: 3
Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases
Jiangli Cui, Xingyu Miao, Xiaoyu Yanghao, et al.
Frontiers in Neurology (2023) Vol. 14
Open Access | Times Cited: 3
Surgical Strategies and Outcomes for Intracranial Chondromas: A Retrospective Study of 17 Cases and Systematic Review
Hongyuan Liu, Qing Cai, Junting Li, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 5
Radiosurgery for Intracranial Meningiomas: A Review of Anatomical Challenges and an Update on the Evidence
Matthew Goldman, Bin S. Teh, Simon S. Lo, et al.
Cancers (2024) Vol. 17, Iss. 1, pp. 45-45
Open Access
Development and Validation of a Machine Learning Radiomics Model based on Multiparametric MRI for Predicting Progesterone Receptor Expression in Meningioma: A Multicenter Study
Guihan Lin, Weiyue Chen, Yongjun Chen, et al.
Academic Radiology (2024)
Closed Access
The AGU-Net Architecture for Brain Tumor Segmentation: BraTS Challenges 2023
David Bouget, André Pedersen, Ole Solheim, et al.
Lecture notes in computer science (2024), pp. 11-23
Closed Access
Radiomics and machine learning for predicting the consistency of benign tumors of the central nervous system: A systematic review
Carole Koechli, Daniel R. Zwahlen, Philippe Schucht, et al.
European Journal of Radiology (2023) Vol. 164, pp. 110866-110866
Closed Access | Times Cited: 1
Molecular Biology in Treatment Decision Processes—Neuro-Oncology Edition
Andra Krauze, Kevin Camphausen
International Journal of Molecular Sciences (2021) Vol. 22, Iss. 24, pp. 13278-13278
Open Access | Times Cited: 1
MRI-based machine learning models predict the malignant biological behavior of meningioma
Maoyuan Li, Luzhou Liu, Jie Qi, et al.
Research Square (Research Square) (2023)
Open Access
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