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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 a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study
Huanjun Wang, Xiaopan Xu, Xi Zhang, et al.
European Radiology (2020) Vol. 30, Iss. 9, pp. 4816-4827
Closed Access | Times Cited: 59

Showing 1-25 of 59 citing articles:

Role of Radiomics in the Prediction of Muscle-invasive Bladder Cancer: A Systematic Review and Meta-analysis
Mieszko Kozikowski, Rodrigo Suarez-Ibarrola, Raúl Osiecki, et al.
European Urology Focus (2021) Vol. 8, Iss. 3, pp. 728-738
Closed Access | Times Cited: 57
Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning
Jianpeng Li, Zhengxuan Qiu, Kangyang Cao, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 233, pp. 107466-107466
Closed Access | Times Cited: 27
Automatic segmentation of bladder cancer on MRI using a convolutional neural network and reproducibility of radiomics features: a two-center study
Yusaku Moribata, Yasuhisa Kurata, Mizuho Nishio, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 17
Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review
Kangwen He, Xiaoyan Meng, Yanchun Wang, et al.
Diagnostics (2024) Vol. 14, Iss. 4, pp. 442-442
Open Access | Times Cited: 6
Deep Learning on Enhanced CT Images Can Predict the Muscular Invasiveness of Bladder Cancer
Gumuyang Zhang, Zhe Wu, Lili Xu, et al.
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 35
CT-based radiomics to predict muscle invasion in bladder cancer
Gumuyang Zhang, Zhe Wu, Xiaoxiao Zhang, et al.
European Radiology (2022) Vol. 32, Iss. 5, pp. 3260-3268
Closed Access | Times Cited: 27
Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer
Zongtai Zheng, Feijia Xu, Zhuoran Gu, et al.
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 27
Predicting muscle invasion in bladder cancer by deep learning analysis of MRI: comparison with vesical imaging–reporting and data system
Jianpeng Li, Kangyang Cao, Hongxin Lin, et al.
European Radiology (2022) Vol. 33, Iss. 4, pp. 2699-2709
Closed Access | Times Cited: 20
Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses
Jovana Panić, Arianna Defeudis, Gabriella Balestra, et al.
IEEE Open Journal of Engineering in Medicine and Biology (2023) Vol. 4, pp. 67-76
Open Access | Times Cited: 11
A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study
Zongjie Wei, Yingjie Xv, Huayun Liu, et al.
International Journal of Surgery (2024)
Open Access | Times Cited: 4
A stacking ensemble system for identifying the presence of histological variants in bladder carcinoma: a multicenter study
Canjie Peng, Quanhao He, Fajin Lv, et al.
Frontiers in Oncology (2025) Vol. 14
Open Access
Multi-path neural network based on mp-MRI for predicting muscle-invasive bladder cancer
Jie Yu, Lingkai Cai, Chunxiao Chen, et al.
Intelligent Data Analysis (2025)
Closed Access
Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study
Lingkai Cai, Xiao Yang, Jie Yu, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Usefulness of texture features of apparent diffusion coefficient maps in predicting chemoradiotherapy response in muscle-invasive bladder cancer
Koichiro Kimura, Soichiro Yoshida, Junichi Tsuchiya, et al.
European Radiology (2021) Vol. 32, Iss. 1, pp. 671-679
Closed Access | Times Cited: 24
Baseline MRI-based radiomics model assisted predicting disease progression in nasopharyngeal carcinoma patients with complete response after treatment
Dan Bao, Zhou Liu, Yayuan Geng, et al.
Cancer Imaging (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 18
Bladder Urothelial Carcinoma: Machine Learning-based Computed Tomography Radiomics for Prediction of Histological Variant
Şehnaz Evrimler, Mehmet Ali Gedik, T. Ahmet Serel, et al.
Academic Radiology (2022) Vol. 29, Iss. 11, pp. 1682-1689
Closed Access | Times Cited: 18
CT-based radiomics for the preoperative prediction of the muscle-invasive status of bladder cancer and comparison to radiologists' assessment
Yingpu Cui, Zepang Sun, X. Liu, et al.
Clinical Radiology (2022) Vol. 77, Iss. 6, pp. e473-e482
Closed Access | Times Cited: 17
Multi-task deep learning based on T2-Weighted Images for predicting Muscular-Invasive Bladder Cancer
Yuan Zou, Lingkai Cai, Chunxiao Chen, et al.
Computers in Biology and Medicine (2022) Vol. 151, pp. 106219-106219
Closed Access | Times Cited: 17
Contrast enhanced magnetic resonance imaging–based radiomics nomogram for preoperatively predicting expression status of Ki-67 in meningioma: a two-center study
Zhi-Qiang Ouyang, Shao-Nan He, Yi-Zhen Zeng, et al.
Quantitative Imaging in Medicine and Surgery (2023) Vol. 13, Iss. 2, pp. 1100-1114
Open Access | Times Cited: 9
MRI‐Based Machine Learning Radiomics for Preoperative Assessment of Human Epidermal Growth Factor Receptor 2 Status in Urothelial Bladder Carcinoma
Ruixi Yu, Lingkai Cai, Yuxi Gong, et al.
Journal of Magnetic Resonance Imaging (2024) Vol. 60, Iss. 6, pp. 2694-2704
Closed Access | Times Cited: 3
The accuracy and quality of image-based artificial intelligence for muscle-invasive bladder cancer prediction
Chunlei He, Hui Xu, Enyu Yuan, et al.
Insights into Imaging (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 3
The Role of 18F-FDG PET/CT in Guiding Precision Medicine for Invasive Bladder Carcinoma
Antoine Girard, Helena Vila-Reyes, Hiram Shaish, et al.
Frontiers in Oncology (2020) Vol. 10
Open Access | Times Cited: 24
Magnetic resonance imaging-based radiomics signature for preoperative prediction of Ki67 expression in bladder cancer
Zongtai Zheng, Zhuoran Gu, Feijia Xu, et al.
Cancer Imaging (2021) Vol. 21, Iss. 1
Open Access | Times Cited: 23
The Additional Value of Tri-parametric MRI in Identifying Muscle-invasive Status in Bladder Cancer
Yan Liu, Xiaopan Xu, Huanjun Wang, et al.
Academic Radiology (2022) Vol. 30, Iss. 1, pp. 64-76
Closed Access | Times Cited: 15
The role of radiomics with machine learning in the prediction of muscle-invasive bladder cancer: A mini review
Xiaodan Huang, Xiangyu Wang, Xinxin Lan, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 14
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