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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 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

Showing 11 citing articles:

Machine Learning–Based Prognostic Model for Patients After Lung Transplantation
Dong Tian, Hao‐Ji Yan, Heng Huang, et al.
JAMA Network Open (2023) Vol. 6, Iss. 5, pp. e2312022-e2312022
Open Access | Times Cited: 36
Computation of the distribution of model accuracy statistics in machine learning: Comparison between analytically derived distributions and simulation‐based methods
Alexander A. Huang, Samuel Y. Huang
Health Science Reports (2023) Vol. 6, Iss. 4
Open Access | Times Cited: 22
Survival Prediction in Second Primary Breast Cancer Patients with Machine Learning: An Analysis of SEER Database
Yafei Wu, Yaheng Zhang, Siyu Duan, et al.
Computer Methods and Programs in Biomedicine (2024) Vol. 254, pp. 108310-108310
Closed Access | Times Cited: 4
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
CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy
Kartik Iyer, Cameron Beeche, Naciye Sinem Gezer, et al.
Journal of Clinical Medicine (2023) Vol. 12, Iss. 6, pp. 2106-2106
Open Access | Times Cited: 8
The application of radiomics in esophageal cancer: Predicting the response after neoadjuvant therapy
Hai Guo, Hong-Tao Tang, Wenlong Hu, et al.
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 7
Comparison of risk prediction models for the progression of pelvic inflammatory disease patients to sepsis: Cox regression model and machine learning model
Qingyi Wang, Jianing Sun, Xiao-Fang Liu, et al.
Heliyon (2023) Vol. 10, Iss. 1, pp. e23148-e23148
Open Access | Times Cited: 7
Ultrasound-based radiomics machine learning models for diagnosing cervical lymph node metastasis in patients with non-small cell lung cancer: a multicentre study
Zhiqiang Deng, Xiaoling Liu, Renmei Wu, et al.
BMC Cancer (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 2
Predicting the risk category of thymoma with machine learning-based computed tomography radiomics signatures and their between-imaging phase differences
Liang Zhu, Jiamin Li, Yi-Han Tang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
A machine learning based on CT radiomics signature and change value features for predicting the risk classification of thymoma
Liang Zhu, Jiaming Li, Yi-Han Tang, et al.
Research Square (Research Square) (2024)
Open Access
Commentary: Radiomic evaluation of thymic tumors, yet to be validated
Philip A. Linden
Journal of Thoracic and Cardiovascular Surgery (2022) Vol. 165, Iss. 2, pp. 517-517
Open Access
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