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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:

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

Showing 1-25 of 32 citing articles:

Artificial intelligence: A promising frontier in bladder cancer diagnosis and outcome prediction
Soheila Borhani, Reza Borhani, André Kajdacsy-Balla
Critical Reviews in Oncology/Hematology (2022) Vol. 171, pp. 103601-103601
Open Access | Times Cited: 52
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
Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach
Suryadipto Sarkar, Kong Min, Waleed Ikram, et al.
Cancers (2023) Vol. 15, Iss. 6, pp. 1673-1673
Open Access | Times Cited: 23
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
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
The effect of different adipose tissue measurements on clinical prognosis in bladder cancer patients undergoing radical cystectomy: preliminary results
Aykut Demirci, Hasan Aydın
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
A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study
Xiaomeng Han, Jing Guan, Guo Li, et al.
Cancer Imaging (2025) Vol. 25, Iss. 1
Open 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
Deep Learning Integration of CT and Histopathology Images for Comprehensive Prediction of Postoperative Survival and Recurrence Risk in Non-Muscle-Invasive Bladder Cancer: A Multicenter Study
Xing Liu, Xinlei Wang, Guangyue Wang, et al.
(2025)
Closed Access
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
A Study on Bladder Cancer Detection using AI-based Learning Techniques
Apeksha Koul, Yogesh Kumar, Anish Gupta
2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) (2022)
Closed Access | Times Cited: 15
CT-based deep learning radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer
Weitian Chen, Mancheng Gong, Dongsheng Zhou, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 14
Deep learning in bladder cancer imaging: A review
Mingyang Li, Zekun Jiang, Wei Shen, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 13
The Present and Future of Artificial Intelligence in Urological Cancer
Xun Liu, Jianxi Shi, Zhaopeng Li, et al.
Journal of Clinical Medicine (2023) Vol. 12, Iss. 15, pp. 4995-4995
Open Access | Times Cited: 7
SMMF: a self-attention-based multi-parametric MRI feature fusion framework for the diagnosis of bladder cancer grading
Tingting Tao, Huiling Chen, Yunyun Shang, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 2
The classification of the bladder cancer based on Vision Transformers (ViT)
Ola S. Khedr, M. El-Sayed Wahed, Al-Sayed R. Al-Attar, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 6
Predicting Recurrence of Non-Muscle-Invasive Bladder Cancer: Current Techniques and Future Trends
Aya T. Shalata, Mohamed Shehata, Eric Van Bogaert, et al.
Cancers (2022) Vol. 14, Iss. 20, pp. 5019-5019
Open Access | Times Cited: 9
Multimodal investigation of bladder cancer data based on computed tomography, whole slide imaging, and transcriptomics
Peng Wu, Kai Wu, Zhe Li, et al.
Quantitative Imaging in Medicine and Surgery (2023) Vol. 13, Iss. 2, pp. 1023-1035
Open Access | Times Cited: 5
Artificial Intelligence in Bladder Cancer Diagnosis: Current Applications and Future Perspectives
Giulio Rossin, Federico Zorzi, Luca Ongaro, et al.
BioMedInformatics (2023) Vol. 3, Iss. 1, pp. 104-114
Open Access | Times Cited: 5
A novel self-learning framework for bladder cancer grading using histopathological images
Gabriel García, Anna Esteve, Adrián Colomer, et al.
Computers in Biology and Medicine (2021) Vol. 138, pp. 104932-104932
Open Access | Times Cited: 12
Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor
Yuan‐Hung Pong, Vincent F.S. Tsai, Yu-Hsuan Hsu, et al.
Applied Sciences (2022) Vol. 12, Iss. 14, pp. 7216-7216
Open Access | Times Cited: 8
LCANet: A Lightweight Context-Aware Network for Bladder Tumor Segmentation in MRI Images
Yixing Wang, Xiang Li, Xiufen Ye
Mathematics (2023) Vol. 11, Iss. 10, pp. 2357-2357
Open Access | Times Cited: 4
Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction
Jesutofunmi Ayo Fajemisin, Glebys Gonzalez, Stephen A. Rosenberg, et al.
Tomography (2024) Vol. 10, Iss. 9, pp. 1439-1454
Open Access | Times Cited: 1
Application of Artificial Intelligence in Abdominal Imaging
Ma Xiaohong, Feng Bing, Qi Zhang, et al.
(2024), pp. 181-191
Closed Access
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