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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:

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

Showing 18 citing articles:

Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review
Georgios Feretzakis, Patrick Juliebø‐Jones, Arman Tsaturyan, et al.
Cancers (2024) Vol. 16, Iss. 4, pp. 810-810
Open Access | Times Cited: 12
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
Computed Tomography Urography: State of the Art and Beyond
Michaela Cellina, Maurizio Cè, Nicolo’ Rossini, et al.
Tomography (2023) Vol. 9, Iss. 3, pp. 909-930
Open Access | Times Cited: 16
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
Radiation hematologic toxicity prediction in rectal cancer: a comparative radiomics-based study on CT image and dose map
Yingpeng Liu, Liping Guo, Yi Wang, et al.
Frontiers in Oncology (2025) Vol. 15
Open Access
Predicting in-hospital mortality after transcatheter aortic valve replacement using administrative data and machine learning
Theyab Alhwiti, Summer Aldrugh, Fadel M. Megahed
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 9
Pelvic floor muscle contraction automatic evaluation algorithm for pelvic floor muscle training biofeedback using self-performed ultrasound
Miyako Muta, Toshiaki Takahashi, Nao Tamai, et al.
BMC Women s Health (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 3
Machine learning for predicting colon cancer recurrence
Erkan Kayıkçıoğlu, Arif Hakan Önder, Burcu Bacak, et al.
Surgical Oncology (2024) Vol. 54, pp. 102079-102079
Closed Access | Times Cited: 3
Utility of Machine Learning in the Prediction of Post-Hepatectomy Liver Failure in Liver Cancer
Hirotaka Tashiro, Takashi Onoe, Naoki Tanimine, et al.
Journal of Hepatocellular Carcinoma (2024) Vol. Volume 11, pp. 1323-1330
Open Access | Times Cited: 2
AI-powered radiomics: revolutionizing detection of urologic malignancies
David G. Gelikman, Soroush Rais‐Bahrami, Peter A. Pinto, et al.
Current Opinion in Urology (2023) Vol. 34, Iss. 1, pp. 1-7
Closed Access | Times Cited: 5
Vesical Imaging-Reporting and Data System (VI-RADS) as a grouping imaging biomarker combined with a decision-tree mode to preoperatively predict the pathological grade of bladder cancer
B. Cao, Qing Li, Peng Xu, et al.
Clinical Radiology (2024) Vol. 79, Iss. 5, pp. e725-e735
Closed Access | Times Cited: 1
Prediction of Histological Grade of Oral Squamous Cell Carcinoma Using Machine Learning Models Applied to 18F-FDG-PET Radiomics
Yutaka Nikkuni, Hideyoshi Nishiyama, Takafumi Hayashi
Biomedicines (2024) Vol. 12, Iss. 7, pp. 1411-1411
Open Access | Times Cited: 1
Use of machine learning to predict bladder cancer survival outcomes: a systematic literature review
Yi‐Shao Liu, Ryan Thaliffdeen, Sola Han, et al.
Expert Review of Pharmacoeconomics & Outcomes Research (2023) Vol. 23, Iss. 7, pp. 761-771
Closed Access | Times Cited: 3
Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning
Daisuke Oura, Soichiro Takamiya, Riku Ihara, et al.
Diagnostics (2023) Vol. 13, Iss. 13, pp. 2138-2138
Open Access | Times Cited: 2
MRI-based radiomics analysis of bladder cancer: prediction of pathological grade and histological variant
Wei Wang, Kan Wang, Jianxing Qiu, et al.
Clinical Radiology (2023) Vol. 78, Iss. 11, pp. e889-e897
Closed Access | Times Cited: 2
Construction of a CT Radiomics Model for Predicting Her2 Expression in Bladder Cancer Based on Random Forest Algorithm
Guoneng Zhang, Zeyu Chen, Wei Xia, et al.
Research Square (Research Square) (2024)
Closed Access
Multi-DECT image-based intratumoral and peritumoral radiomics for preoperative prediction of muscle invasion in bladder cancer
Mengting Hu, Jingyi Zhang, Qiye Cheng, et al.
Academic Radiology (2024)
Open Access
A New Approach to Predict the Histological Variants of Bladder Urothelial Carcinoma: Machine Learning-Based Radiomics Analysis
Muhammed Said Beşler, Ural Koç
Academic Radiology (2022) Vol. 29, Iss. 11, pp. 1690-1691
Closed Access | Times Cited: 2
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