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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:

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach
Junjiong Zheng, Jianqiu Kong, Shaoxu Wu, et al.
Cancer (2019) Vol. 125, Iss. 24, pp. 4388-4398
Open Access | Times Cited: 69

Showing 1-25 of 69 citing articles:

Development and Validation of a Computed Tomography–Based Radiomics Signature to Predict Response to Neoadjuvant Chemotherapy for Locally Advanced Gastric Cancer
Wei Wang, Ying Peng, Xingyu Feng, et al.
JAMA Network Open (2021) Vol. 4, Iss. 8, pp. e2121143-e2121143
Open Access | Times Cited: 80
Diagnosis and Staging of Bladder Cancer
Hamed Ahmadi, Vinay Duddalwar, Siamak Daneshmand
Hematology/Oncology Clinics of North America (2021) Vol. 35, Iss. 3, pp. 531-541
Closed Access | Times Cited: 68
Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach
Yi Zhou, Xuelei Ma, Ting Zhang, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2021) Vol. 48, Iss. 9, pp. 2904-2913
Closed Access | Times Cited: 60
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
Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer
Min Hou, Long Zhou, Jihong Sun
European Radiology (2022) Vol. 33, Iss. 1, pp. 1-10
Open Access | Times Cited: 46
Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement
Matteo Ferro, Ugo Giovanni Falagario, Biagio Barone, et al.
Diagnostics (2023) Vol. 13, Iss. 13, pp. 2308-2308
Open Access | Times Cited: 30
Impact of tumour stroma-immune interactions on survival prognosis and response to neoadjuvant chemotherapy in bladder cancer
Libo Liu, Longhao Xu, Daqin Wu, et al.
EBioMedicine (2024) Vol. 104, pp. 105152-105152
Open Access | Times Cited: 9
A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning
Junjiong Zheng, Hao Yu, Jesur Batur, et al.
Kidney International (2021) Vol. 100, Iss. 4, pp. 870-880
Closed Access | Times Cited: 52
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
An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer
Jiexin Pan, Guibin Hong, Hong Zeng, et al.
Journal of Translational Medicine (2023) Vol. 21, Iss. 1
Open Access | Times Cited: 17
Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer
Zongjie Wei, Huayun Liu, Yingjie Xv, et al.
Heliyon (2024) Vol. 10, Iss. 2, pp. e24878-e24878
Open Access | Times Cited: 6
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
Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer
Xing Tang, Xiaopan Xu, Zhiping Han, et al.
BioMedical Engineering OnLine (2020) Vol. 19, Iss. 1
Open Access | Times Cited: 43
Development and validation of a CT-based nomogram for preoperative prediction of clear cell renal cell carcinoma grades
Zaosong Zheng, Zhiliang Chen, Yingwei Xie, et al.
European Radiology (2021) Vol. 31, Iss. 8, pp. 6078-6086
Closed Access | Times Cited: 35
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
Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications
Damiano Caruso, Michela Polici, Marta Zerunian, et al.
Cancers (2021) Vol. 13, Iss. 11, pp. 2681-2681
Open Access | Times Cited: 34
Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer
Fangdie Ye, Yun Hu, Jiahao Gao, et al.
Frontiers in Immunology (2021) Vol. 12
Open Access | Times Cited: 34
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
Study Progress of Radiomics With Machine Learning for Precision Medicine in Bladder Cancer Management
Lingling Ge, Yuntian Chen, Chunyi Yan, et al.
Frontiers in Oncology (2019) Vol. 9
Open Access | Times Cited: 40
Application of deep learning as a noninvasive tool to differentiate muscle-invasive bladder cancer and non–muscle-invasive bladder cancer with CT
Yuhan Yang, Xiuhe Zou, Yixi Wang, et al.
European Journal of Radiology (2021) Vol. 139, pp. 109666-109666
Closed Access | Times Cited: 32
Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram
Haiming Li, Rui Zhang, Ruimin Li, et al.
European Radiology (2021) Vol. 31, Iss. 10, pp. 7855-7864
Closed Access | Times Cited: 29
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
CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study
Hongzheng Song, Shifeng Yang, Boyang Yu, et al.
Cancer Imaging (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 12
Predicting preoperative muscle invasion status for bladder cancer using computed tomography-based radiomics nomogram
Rui Zhang, Shijun Jia, Linhan Zhai, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 4
Diagnostic Value of CT-Based Radiomics for Multi-Class T-Staging of Rectal Cancer
晨阳 邱
Advances in Clinical Medicine (2025) Vol. 15, Iss. 04, pp. 2513-2522
Closed Access
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