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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 Signatures of Computed Tomography Imaging for Predicting Risk Categorization and Clinical Stage of Thymomas
Xihai Wang, Wei Sun, Hongyuan Liang, et al.
BioMed Research International (2019) Vol. 2019, pp. 1-10
Open Access | Times Cited: 40

Showing 1-25 of 40 citing articles:

Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer
Xihai Wang, Zaiming Lu
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 33
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
Comparison of 2D and 3D radiomics features with conventional features based on contrast-enhanced CT images for preoperative prediction the risk of thymic epithelial tumors
Yu-Hang Yuan, Hui Zhang, Weiling Xu, et al.
Radiology and Oncology (2025) Vol. 59, Iss. 1, pp. 69-78
Open 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
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
Computed Tomography-Based Radiomics in Predicting T Stage and Length of Esophageal Squamous Cell Carcinoma
Mingwei Yang, Panpan Hu, Minglun Li, et al.
Frontiers in Oncology (2021) Vol. 11
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
CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors
Jin Liu, Ping Yin, Sicong Wang, et al.
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 22
CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: “Impact of surgical modality choice”
Ayten Cangır, Kaan Orhan, Yusuf Kahya, et al.
World Journal of Surgical Oncology (2021) Vol. 19, Iss. 1
Open Access | Times Cited: 21
Contrast-enhanced CT-based radiomics differentiate anterior mediastinum lymphoma from thymoma without myasthenia gravis and calcification
Xiangjin Huang, Xiaoyang Wang, Ying Liu, et al.
Clinical Radiology (2024) Vol. 79, Iss. 4, pp. e500-e510
Closed Access | Times Cited: 2
Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy
Kang Huang, Xuehan Huang, Chengbing Zeng, et al.
Cancer Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 2
Volumetric tumor delineation and assessment of its early response to radiotherapy with optical coherence tomography
Valentin Demidov, Natalia Demidova, Layla Pires, et al.
Biomedical Optics Express (2021) Vol. 12, Iss. 5, pp. 2952-2952
Open Access | Times Cited: 16
Differentiating Glioblastoma Multiforme from Brain Metastases Using Multidimensional Radiomics Features Derived from MRI and Multiple Machine Learning Models
Salar Bijari, Amin Jahanbakhshi, Parham Hajishafiezahramini, et al.
BioMed Research International (2022) Vol. 2022, pp. 1-10
Open Access | Times Cited: 12
Combined clinical and specific positron emission tomography/computed tomography-based radiomic features and machine-learning model in prediction of thymoma risk groups
Elgin Özkan, Kaan Orhan, Çiğdem Soydal, et al.
Nuclear Medicine Communications (2022) Vol. 43, Iss. 5, pp. 529-539
Closed Access | Times Cited: 11
CT-based radiomics analysis for differentiation between thymoma and thymic carcinoma
Ryosuke Ohira, Masahiro Yanagawa, Yuki Suzuki, et al.
Journal of Thoracic Disease (2022) Vol. 14, Iss. 5, pp. 1342-1352
Open Access | Times Cited: 11
Application of a combined radiomics nomogram based on CE-CT in the preoperative prediction of thymomas risk categorization
Wentao Dong, Situ Xiong, Pinggui Lei, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 11
Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors
Chunhai Yu, Ting Li, Xiaotang Yang, et al.
BMC Medical Imaging (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 10
Advancement in Diagnostic Imaging of Thymic Tumors
Francesco Gentili, Ilaria Monteleone, Francesco Giuseppe Mazzei, et al.
Cancers (2021) Vol. 13, Iss. 14, pp. 3599-3599
Open Access | Times Cited: 14
Machine-learning classifiers based on non-enhanced computed tomography radiomics to differentiate anterior mediastinal cysts from thymomas and low-risk from high-risk thymomas: A multi-center study
Lan Shang, Fang Wang, Yan Gao, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 9
Can computed tomography-based radiomics potentially discriminate between anterior mediastinal cysts and type B1 and B2 thymomas?
Lulu Liu, Fangxiao Lu, Peipei Pang, et al.
BioMedical Engineering OnLine (2020) Vol. 19, Iss. 1
Open Access | Times Cited: 13
Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma
Masaki Ogawa, Satoshi Osaga, Norio Shiraki, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
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
Risk stratification of thymic epithelial tumors based on peritumor CT radiomics and semantic features
Zhang Lin, Zhihan Xu, Yan Feng, et al.
Insights into Imaging (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 1
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