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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:

Current Advances and Challenges in Radiomics of Brain Tumors
Zhenjie Yi, Lifu Long, Yu Zeng, et al.
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives
Matia Martucci, Rosellina Russo, Francesco Schimperna, et al.
Biomedicines (2023) Vol. 11, Iss. 2, pp. 364-364
Open Access | Times Cited: 30
Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics
Ramy Abou Ghayda, Rossella Cannarella, Aldo E. Calogero, et al.
The World Journal of Men s Health (2023) Vol. 42, Iss. 1, pp. 39-39
Open Access | Times Cited: 27
Artificial intelligence-based MRI radiomics and radiogenomics in glioma
Haiqing Fan, Yilin Luo, Fang Gu, et al.
Cancer Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 6
MRI Scoring Systems for Predicting Isocitrate Dehydrogenase Mutation and Chromosome 1p/19q Codeletion in Adult-type Diffuse Glioma Lacking Contrast Enhancement
Koung Mi Kang, Jiyoung Song, Yunhee Choi, et al.
Radiology (2024) Vol. 311, Iss. 2
Closed Access | Times Cited: 4
Research Progress of Artificial Intelligence in the Grading and Classification of Meningiomas
Yuan Gui, Jing Zhang
Academic Radiology (2024) Vol. 31, Iss. 8, pp. 3346-3354
Closed Access | Times Cited: 3
Imaging meningioma biology: Machine Learning predicts integrated risk score in WHO grade 2/3 meningioma
Olivia Kertels, Claire Delbridge, Felix Sahm, et al.
Neuro-Oncology Advances (2024) Vol. 6, Iss. 1
Open Access | Times Cited: 3
Comparison of MRI Sequences to Predict IDH Mutation Status in Gliomas Using Radiomics-Based Machine Learning
Dilek N. G. Kasap, Nabila Gala Nacul Mora, David A. Blömer, et al.
Biomedicines (2024) Vol. 12, Iss. 4, pp. 725-725
Open Access | Times Cited: 3
Predicting meningioma grades and pathologic marker expression via deep learning
Jiawei Chen, Yanping Xue, Leihao Ren, et al.
European Radiology (2023) Vol. 34, Iss. 5, pp. 2997-3008
Closed Access | Times Cited: 9
Radiomics and radiogenomics of central nervous system metastatic lesions
Teresa Perillo, Carmela Somma, Marco De Giorgi, et al.
Elsevier eBooks (2024), pp. 235-249
Closed Access | Times Cited: 2
Reirradiation for diffuse intrinsic pontine glioma: prognostic radiomic factors at progression
Dominik Wawrzuta, Marzanna Chojnacka, Monika Drogosiewicz, et al.
Strahlentherapie und Onkologie (2024) Vol. 200, Iss. 9, pp. 797-804
Open Access | Times Cited: 2
Multimodal Deep Learning Improves Recurrence Risk Prediction in Pediatric Low-Grade Gliomas
Maryamalsadat Mahootiha, Divyanshu Tak, Zezhong Ye, et al.
Neuro-Oncology (2024) Vol. 27, Iss. 1, pp. 277-290
Open Access | Times Cited: 2
The Role of Advanced Imaging in Neurosurgical Diagnosis
Kyril L. Cole, Matthew C Findlay, Mrinmoy Kundu, et al.
Journal of Modern Medical Imaging (2023)
Open Access | Times Cited: 7
Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI
Christian di Noia, James T. Grist, Frank Riemer, et al.
Diagnostics (2022) Vol. 12, Iss. 9, pp. 2125-2125
Open Access | Times Cited: 12
An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach
Lan Zhang, Xiao Liu, Xia Xu, et al.
European Journal of Radiology (2022) Vol. 158, pp. 110639-110639
Closed Access | Times Cited: 12
Prediction of meningioma grade by constructing a clinical radiomics model nomogram based on magnetic resonance imaging
Tao Han, Xianwang Liu, Changyou Long, et al.
Magnetic Resonance Imaging (2023) Vol. 104, pp. 16-22
Closed Access | Times Cited: 6
AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models
A V Krauze, Ying Zhuge, Rachel Zhao, et al.
Journal of Biotechnology and Biomedicine (2022) Vol. 05, Iss. 01
Open Access | Times Cited: 10
Determining acute ischemic stroke onset time using machine learning and radiomics features of infarct lesions and whole brain
Jiaxi Lu, Yingwei Guo, Mingming Wang, et al.
Mathematical Biosciences & Engineering (2023) Vol. 21, Iss. 1, pp. 34-48
Open Access | Times Cited: 5
Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor
H Ahmed, Dada MO, Bahago B. Samaila
Material Science & Engineering International Journal (2023) Vol. 7, Iss. 4, pp. 196-208
Open Access | Times Cited: 5
Application and constraints of AI in radiomics and radiogenomics (R-n-R) studies of neuro-oncology
Shovna Panda, Sarthak Padhi, VikasKumar Gupta, et al.
Elsevier eBooks (2024), pp. 267-300
Closed Access | Times Cited: 1
MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study
Ruiqin Tan, Chunxiao Sui, Chao Wang, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 1
The Current Diagnostic Performance of MRI-Based Radiomics for Glioma Grading: A Meta-Analysis
Lucio De Maria, Francesco Ponzio, Hwan-ho Cho, et al.
Journal of Integrative Neuroscience (2024) Vol. 23, Iss. 5
Open Access | Times Cited: 1
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
Plasma nanoDSF Denaturation Profile at Baseline Is Predictive of Glioblastoma EGFR Status
R. Eyraud, Stéphane Ayache, Philipp O. Tsvetkov, et al.
Cancers (2023) Vol. 15, Iss. 3, pp. 760-760
Open Access | Times Cited: 2
Deep learning methods for scientific and industrial research
G.K. Patra, Kantha Rao Bhimala, Ashapurna Marndi, et al.
Handbook of statistics (2023), pp. 107-168
Closed Access | Times Cited: 2
Research on application of radiomics in glioma: a bibliometric and visual analysis
Chunbao Chen, Xue Du, Lu Yang, et al.
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 2
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