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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-based computed tomography radiomic analysis for histologic subtype classification of thymic epithelial tumours
Jianping Hu, Yijing Zhao, Mengcheng Li, et al.
European Journal of Radiology (2020) Vol. 126, pp. 108929-108929
Closed Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Radiomics in Nasopharyngeal Carcinoma
Wenyue Duan, Bingdi Xiong, Ting Tian, et al.
Clinical Medicine Insights Oncology (2022) Vol. 16
Open Access | Times Cited: 29
MRI-based radiomics analysis for differentiating phyllodes tumors of the breast from fibroadenomas
M. Tsuchiya, Takayuki Masui, Terauchi Kazuma, et al.
European Radiology (2022) Vol. 32, Iss. 6, pp. 4090-4100
Closed Access | Times Cited: 24
Artificial intelligence in thoracic surgery: a narrative review
Valentina Bellini, Marina Valente, Paolo Del Rio, et al.
Journal of Thoracic Disease (2021) Vol. 13, Iss. 12, pp. 6963-6975
Open Access | Times Cited: 28
Optimizing the radiomics-machine-learning model based on non-contrast enhanced CT for the simplified risk categorization of thymic epithelial tumors: A large cohort retrospective study
Xiu-Long Feng, Shengzhong Wang, Hao-han Chen, et al.
Lung Cancer (2022) Vol. 166, pp. 150-160
Open Access | Times Cited: 19
Conventional and radiomic features to predict pathology in the preoperative assessment of anterior mediastinal masses
María Mayoral, Andrew Pagano, José de Arimateia Batista Araújo-Filho, et al.
Lung Cancer (2023) Vol. 178, pp. 206-212
Open Access | Times Cited: 11
Computed tomography radiomics-based combined model for predicting thymoma risk subgroups: a multicenter retrospective study
Yifei Liu, Chao Luo, Yongshun Wu, et al.
Academic Radiology (2025)
Closed Access
Radiomics in thymic epithelial tumors: a scoping review of current status and advances
Xiaolian Wang, Pei Huang, Z Wang, et al.
BMC Cancer (2025) Vol. 25, Iss. 1
Open Access
Predicting Hospitalization Length in Geriatric Patients Using Artificial Intelligence and Radiomics
Lorenzo Fantechi, Federico Barbarossa, S Cecchini, et al.
Bioengineering (2025) Vol. 12, Iss. 4, pp. 368-368
Open Access
Virtual Monoenergetic Images of Dual-Energy CT—Impact on Repeatability, Reproducibility, and Classification in Radiomics
André Euler, Fabian Christopher Laqua, D. Cester, et al.
Cancers (2021) Vol. 13, Iss. 18, pp. 4710-4710
Open Access | Times Cited: 25
Computed tomography radiomics for the prediction of thymic epithelial tumor histology, TNM stage and myasthenia gravis
Christian Blüthgen, Miriam Patella, André Euler, et al.
PLoS ONE (2021) Vol. 16, Iss. 12, pp. e0261401-e0261401
Open Access | Times Cited: 25
Radiomics Analysis of Multiparametric MRI for Prediction of Synchronous Lung Metastases in Osteosarcoma
Zhendong Luo, Jing Li, Yuting Liao, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 17
Identification and risk classification of thymic epithelial tumors using 3D computed tomography images and deep learning models
Ye Sung Moon, Byunggeon Park, Jongmin Park, et al.
Biomedical Signal Processing and Control (2024) Vol. 95, pp. 106473-106473
Closed Access | Times Cited: 2
Glioma classification via MR images radiomics analysis
Hajer Ouerghi, Olfa Mourali, Ezzeddine Zagrouba
The Visual Computer (2021) Vol. 38, Iss. 4, pp. 1427-1441
Closed Access | Times Cited: 16
Machine learning-based radiomic computed tomography phenotyping of thymic epithelial tumors: Predicting pathological and survival outcomes
Dong Tian, Hao‐Ji Yan, Haruhiko Shiiya, et al.
Journal of Thoracic and Cardiovascular Surgery (2022) Vol. 165, Iss. 2, pp. 502-516.e9
Open Access | Times Cited: 11
Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study
Hao Zhou, Harrison X. Bai, Zhicheng Jiao, et al.
European Journal of Radiology (2023) Vol. 168, pp. 111136-111136
Closed Access | Times Cited: 4
Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study
Daisuke Yamada, F. Kojima, Yujiro Otsuka, et al.
BMJ Open Respiratory Research (2024) Vol. 11, Iss. 1, pp. e002249-e002249
Open Access | Times Cited: 1
CT radiomics and human-machine hybrid system for differentiating mediastinal lymphomas from thymic epithelial tumors
Xia Han, Jiahui Yu, Kehui Nie, et al.
Cancer Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 1
An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors
Chia-Ying Lin, Yi‐Ting Yen, Li‐Ting Huang, et al.
Diagnostics (2022) Vol. 12, Iss. 4, pp. 889-889
Open Access | Times Cited: 7
Computed tomography radiomic feature analysis of thymic epithelial tumors: Differentiation of thymic epithelial tumors from thymic cysts and prediction of histological subtypes
Wenya Zhao, Yoshiyuki Ozawa, Masaki Hara, et al.
Japanese Journal of Radiology (2023) Vol. 42, Iss. 4, pp. 367-373
Closed Access | Times Cited: 3
CT-Based Radiomics Nomogram for Differentiation of Anterior Mediastinal Thymic Cyst From Thymic Epithelial Tumor
Chengzhou Zhang, Qinglin Yang, Fan Lin, et al.
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 5
Development and Validation of a CT-Based Radiomics Nomogram in Patients With Anterior Mediastinal Mass: Individualized Options for Preoperative Patients
Zhou Zhou, Yanjuan Qu, Yurong Zhou, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 3
Predicting treatment responses using magnetic resonance imaging-based radiomics in hepatocellular carcinoma patients undergoing transarterial radioembolization
Sinan Sözütok, Ferhat Can Pişkin, Hüseyin Tuğsan Ballı, et al.
Revista da Associação Médica Brasileira (2024) Vol. 70, Iss. 11
Open Access
Classification of brain tumours using radiomic features on MRI
Gökalp Çınarer, Bülent Gürsel Emiroğlu
New Trends and Issues Proceedings on Advances Pure and Applied Sciences (2020), Iss. 12, pp. 80-90
Open Access | Times Cited: 3
Radiomics Analysis of Multiphasic Computed Tomography Images for Distinguishing High-Risk Thymic Epithelial Tumors From Low-Risk Thymic Epithelial Tumors
Yuling Liufu, Yanhua Wen, Wensheng Wu, et al.
Journal of Computer Assisted Tomography (2023)
Closed Access | Times Cited: 1
Predicting the Risk of Thymic Tumors Using Texture Analysis of Contrast-Enhanced Chest Computed Tomography
Wei Guo, Jianfang Liu, Xiaohua Wang, et al.
Journal of Computer Assisted Tomography (2023) Vol. 47, Iss. 4, pp. 598-602
Open Access | Times Cited: 1
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