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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:

Quantitative Identification of Nonmuscle‐Invasive and Muscle‐Invasive Bladder Carcinomas: A Multiparametric MRI Radiomics Analysis
Xiaopan Xu, Xi Zhang, Qiang Tian, et al.
Journal of Magnetic Resonance Imaging (2018) Vol. 49, Iss. 5, pp. 1489-1498
Closed Access | Times Cited: 87

Showing 26-50 of 87 citing articles:

Applications of artificial intelligence in urologic oncology
Sahyun Pak, Sung Gon Park, Jeonghyun Park, et al.
Investigative and Clinical Urology (2024) Vol. 65, Iss. 3, pp. 202-202
Open Access | Times Cited: 4
Noninvasive identification of HER2 status by integrating multiparametric MRI-based radiomics model with the vesical imaging-reporting and data system (VI-RADS) score in bladder urothelial carcinoma
Cheng Luo, Shurong Li, Yichao Han, et al.
Abdominal Radiology (2025)
Closed 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
Optimizing bladder magnetic resonance imaging: accelerating scan time and improving image quality through deep learning
Er‐Jia Guo, Lixia Chen, Lili Xu, et al.
Abdominal Radiology (2025)
Closed Access
Recent Advances in Artificial Intelligence for Precision Diagnosis and Treatment of Bladder Cancer: A Review
Xiangxiang Yang, Rui Yang, Xiuheng Liu, et al.
Annals of Surgical Oncology (2025)
Closed Access
Machine Learning–Based MRI Texture Analysis to Predict the Histologic Grade of Oral Squamous Cell Carcinoma
Jiliang Ren, Meng Qi, Ying Yuan, et al.
American Journal of Roentgenology (2020) Vol. 215, Iss. 5, pp. 1184-1190
Closed Access | Times Cited: 30
Diagnostic Accuracy of Multiparametric MRI for Local Staging of Bladder Cancer: A Systematic Review and Meta-Analysis
Suzan W.E. Cornelissen, Paul W. Veenboer, Frank J. Wessels, et al.
Urology (2020) Vol. 145, pp. 22-29
Closed Access | Times Cited: 28
A multiparametric MRI-based CAD system for accurate diagnosis of bladder cancer staging
Kamal Hammouda, Fahmi Khalifa, Ahmed Soliman, et al.
Computerized Medical Imaging and Graphics (2021) Vol. 90, pp. 101911-101911
Closed Access | Times Cited: 26
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
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
MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses
Kexin Xie, Can Cui, Xiaoqing Li, et al.
Academic Radiology (2024) Vol. 31, Iss. 10, pp. 4231-4241
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
CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions
He Sui, Lin Liu, Xuejia Li, et al.
Journal of Thoracic Disease (2019) Vol. 11, Iss. 5, pp. 1809-1818
Open Access | Times Cited: 29
CT-Based Radiomics Signatures for Predicting the Risk Categorization of Thymic Epithelial Tumors
Jin Liu, Ping Yin, Sicong Wang, et al.
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 22
CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: “Impact of surgical modality choice”
Ayten Cangır, Kaan Orhan, Yusuf Kahya, et al.
World Journal of Surgical Oncology (2021) Vol. 19, Iss. 1
Open Access | Times Cited: 21
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
MRI-Based Radiomics in Bladder Cancer: A Systematic Review and Radiomics Quality Score Assessment
Bianca Boca, Cosmin Caraiani, Teodora Telecan, et al.
Diagnostics (2023) Vol. 13, Iss. 13, pp. 2300-2300
Open Access | Times Cited: 8
Application of bi-planar reduced field-of-view DWI (rFOV DWI) in the assessment of muscle-invasiveness of bladder cancer
Xiaoyan Meng, Henglong Hu, Yanchun Wang, et al.
European Journal of Radiology (2020) Vol. 136, pp. 109486-109486
Closed Access | Times Cited: 23
Radiomics based on multiparametric MRI for extrathyroidal extension feature prediction in papillary thyroid cancer
Ran Wei, Hao Wang, Lanyun Wang, et al.
BMC Medical Imaging (2021) Vol. 21, Iss. 1
Open Access | Times Cited: 20
Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer
Getao Du, Yun Zeng, Dan Chen, et al.
Japanese Journal of Radiology (2022)
Closed Access | Times Cited: 13
Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities
Xiaoyu Ma, Qiuchen Zhang, Lingling He, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 2
Study Progress of Noninvasive Imaging and Radiomics for Decoding the Phenotypes and Recurrence Risk of Bladder Cancer
Xiaopan Xu, Huanjun Wang, Yan Guo, et al.
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 17
Multi‐Sequence and Multi‐Regional MRI‐Based Radiomics Nomogram for the Preoperative Assessment of Muscle Invasion in Bladder Cancer
Lu Zhang, Xiaoyang Li, Li Yang, et al.
Journal of Magnetic Resonance Imaging (2022) Vol. 58, Iss. 1, pp. 258-269
Closed Access | Times Cited: 12

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