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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:

Elaboration of Multiparametric MRI‐Based Radiomics Signature for the Preoperative Quantitative Identification of the Histological Grade in Patients With Non‐Small‐Cell Lung Cancer
Xing Tang, Guoyan Bai, Hong Wang, et al.
Journal of Magnetic Resonance Imaging (2022) Vol. 56, Iss. 2, pp. 579-589
Closed Access | Times Cited: 9

Showing 9 citing articles:

A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma
Ying-mei Zheng, Junyi Che, Ming-gang Yuan, et al.
Academic Radiology (2022) Vol. 30, Iss. 8, pp. 1591-1599
Closed Access | Times Cited: 22
Comparisons of integrated slice-specific dynamic shimming EPI and single-shot EPI diffusion-weighted imaging of the liver
S Liu, Yongwei Lin, Weijun Situ, et al.
European Journal of Radiology (2025) Vol. 183, pp. 111942-111942
Closed Access
DCE-MRI quantitative analysis and MRI-based radiomics for predicting the early efficacy of microwave ablation in lung cancers
Chen Yang, Fandong Zhu, Jing Yang, et al.
Cancer Imaging (2025) Vol. 25, Iss. 1
Open Access
A deep learning model for predicting multidrug-resistant organism infection in critically ill patients
Yaxi Wang, Gang Wang, Yuxiao Zhao, et al.
Journal of Intensive Care (2023) Vol. 11, Iss. 1
Open Access | Times Cited: 5
Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study
Guotian Pei, Dawei Wang, Kunkun Sun, et al.
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 4
Predicting pathological grade of stage I pulmonary adenocarcinoma: a CT radiomics approach
Xiaoni Huang, Yang Xue, Bing Deng, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 1
Value of contrast-enhanced magnetic resonance imaging-T2WI-based radiomic features in distinguishing lung adenocarcinoma from lung squamous cell carcinoma with solid components >8 mm
Maoyuan Yang, Liang Shi, Tian‐Wei Huang, et al.
Journal of Thoracic Disease (2023) Vol. 15, Iss. 2, pp. 635-648
Open Access | Times Cited: 3
18F-FDG PET/CT-based radiomics model for predicting the degree of pathological differentiation in non-small cell lung cancer: a multicentre study
Fan Liu, Zuo‐Lin Xiang, Qiao Li, et al.
Clinical Radiology (2023) Vol. 79, Iss. 1, pp. e147-e155
Closed Access | Times Cited: 2
T1 mapping-based radiomics in the identification of histological types of lung cancer: a reproducibility and feasibility study
Jianqin Jiang, Yong Xiao, Jia Liu, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access
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