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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:

MRI-based radiomics nomogram for predicting temporal lobe injury after radiotherapy in nasopharyngeal carcinoma
Jing Hou, Handong Li, Biao Zeng, et al.
European Radiology (2021) Vol. 32, Iss. 2, pp. 1106-1114
Closed Access | Times Cited: 38

Showing 1-25 of 38 citing articles:

Deep learning and machine learning predictive models for neurological function after interventional embolization of intracranial aneurysms
Yan Peng, Yiren Wang, Zhongjian Wen, et al.
Frontiers in Neurology (2024) Vol. 15
Open Access | Times Cited: 9
Radiomics in Nasopharyngeal Carcinoma
Wenyue Duan, Bingdi Xiong, Ting Tian, et al.
Clinical Medicine Insights Oncology (2022) Vol. 16
Open Access | Times Cited: 29
Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review
Zipei Wang, Mengjie Fang, Jie Zhang, et al.
IEEE Reviews in Biomedical Engineering (2023) Vol. 17, pp. 118-135
Closed Access | Times Cited: 21
Radiomic feature repeatability and its impact on prognostic model generalizability: A multi-institutional study on nasopharyngeal carcinoma patients
Jiang Zhang, Saikit Lam, Xinzhi Teng, et al.
Radiotherapy and Oncology (2023) Vol. 183, pp. 109578-109578
Closed Access | Times Cited: 16
MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study
Jun Xie, Yi Yang, Zekun Jiang, et al.
Frontiers in Physiology (2024) Vol. 14
Open Access | Times Cited: 6
A MRI-based radiomics model predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma
Dan Bao, Yanfeng Zhao, Lin Li, et al.
European Radiology (2022) Vol. 32, Iss. 10, pp. 6910-6921
Closed Access | Times Cited: 22
Radiomics-guided radiation therapy: opportunities and challenges
Hamid Abdollahi, Erika Chin, Haley Clark, et al.
Physics in Medicine and Biology (2022) Vol. 67, Iss. 12, pp. 12TR02-12TR02
Open Access | Times Cited: 19
Deep learning-based precise prediction and early detection of radiation-induced temporal lobe injury for nasopharyngeal carcinoma
Pu‐Yun OuYang, Bao-Yu Zhang, Jian‐Gui Guo, et al.
EClinicalMedicine (2023) Vol. 58, pp. 101930-101930
Open Access | Times Cited: 12
Deep learning for predicting the risk of immune checkpoint inhibitor-related pneumonitis in lung cancer
M. Cheng, Renying Lin, Na Bai, et al.
Clinical Radiology (2023) Vol. 78, Iss. 5, pp. e377-e385
Closed Access | Times Cited: 11
Comparative Analysis of Ntcp Models for Predicting Temporal Lobe Injury in Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy
Lei Zeng, Kai-Bin Zhu, Yuze Liu, et al.
(2025)
Closed Access
Automated deep learning-assisted early detection of radiation-induced temporal lobe injury on MRI: a multicenter retrospective analysis
Fangxue Yang, Rong Hu, Jing Hu, et al.
European Radiology (2025)
Closed Access
Dosimetric parameters predict radiation-induced temporal lobe necrosis in nasopharyngeal carcinoma patients: A systematic review and meta-analysis
Jun Dong, Wai Tong Ng, Charlene H. L. Wong, et al.
Radiotherapy and Oncology (2024) Vol. 195, pp. 110258-110258
Open Access | Times Cited: 3
A pretreatment multiparametric MRI‐based radiomics‐clinical machine learning model for predicting radiation‐induced temporal lobe injury in patients with nasopharyngeal carcinoma
L Wang, Ting Qiu, Jiawei Zhou, et al.
Head & Neck (2024) Vol. 46, Iss. 9, pp. 2132-2144
Open Access | Times Cited: 3
Advances in MRI‐guided precision radiotherapy
Chenyang Liu, Mao Li, Haonan Xiao, et al.
Precision Radiation Oncology (2022) Vol. 6, Iss. 1, pp. 75-84
Open Access | Times Cited: 13
Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases
Huai-wen Zhang, Yiren Wang, Bo Hu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Preoperatively Evaluate the Pathological Outcomes of Pancreatic Cancer
Ni Xie, Xuhui Fan, Desheng Chen, et al.
Journal of Magnetic Resonance Imaging (2022) Vol. 58, Iss. 2, pp. 379-391
Closed Access | Times Cited: 12
Nomogram Based on Clinical and Radiomics Data for Predicting Radiation-induced Temporal Lobe Injury in Patients with Non-metastatic Stage T4 Nasopharyngeal Carcinoma
Bin Xiang, Chaosheng Zhu, Yu-Xing Tang, et al.
Clinical Oncology (2022) Vol. 34, Iss. 12, pp. e482-e492
Closed Access | Times Cited: 11
Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma
Yihao Guo, Ganmian Dai, Xiaoli Xiong, et al.
Translational Oncology (2023) Vol. 31, pp. 101648-101648
Open Access | Times Cited: 6
Evolution of radiation-induced temporal lobe injury after intensity-modulated radiation therapy in nasopharyngeal carcinoma: a large cohort retrospective study
Jing Hou, Yun He, Handong Li, et al.
Radiation Oncology (2024) Vol. 19, Iss. 1
Open Access | Times Cited: 1
Divergent white matter changes in patients with nasopharyngeal carcinoma post-radiotherapy with different outcomes: a potential biomarker for prediction of radiation necrosis
Xiaoshan Lin, Zhipeng Li, Shengli Chen, et al.
European Radiology (2022) Vol. 32, Iss. 10, pp. 7036-7047
Closed Access | Times Cited: 7
Magnetic resonance imaging‐based radiomics model for predicting radiation‐induced temporal lobe injury in nasopharyngeal carcinoma after intensity‐modulated radiotherapy
Dan Bao, Yanfeng Zhao, Zhou Liu, et al.
Head & Neck (2022) Vol. 44, Iss. 12, pp. 2842-2853
Closed Access | Times Cited: 6
MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma
Lixuan Huang, Zongxiang Yang, Zisan Zeng, et al.
Frontiers in Neurology (2023) Vol. 14
Open Access | Times Cited: 3
MRI-based radiomics models predict cystic brain radionecrosis of nasopharyngeal carcinoma after intensity modulated radiotherapy
Jing Hou, Yun He, Handong Li, et al.
Frontiers in Neurology (2024) Vol. 15
Open Access
Non-complete recovery of temporal lobe white matter diffusion metrics at one year Post-Radiotherapy: Implications for Radiation-Induced necrosis risk
Jie Pan, Ziru Qiu, Gui Fu, et al.
Radiotherapy and Oncology (2024) Vol. 199, pp. 110420-110420
Closed Access
A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma
Wenting Ren, Bin Liang, Chao Sun, et al.
Physica Medica (2024) Vol. 121, pp. 103362-103362
Closed Access
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