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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:

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

Showing 1-25 of 35 citing articles:

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
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
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
Diagnosis of skull-base invasion by nasopharyngeal tumors on CT with a deep-learning approach
Junichi Nakagawa, Noriyuki Fujima, Kenji Hirata, et al.
Japanese Journal of Radiology (2024) Vol. 42, Iss. 5, pp. 450-459
Open Access | Times Cited: 4
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
Breaking barriers: noninvasive AI model for BRAFV600E mutation identification
Fan Wu, Xiangfeng Lin, Yuying Chen, et al.
International Journal of Computer Assisted Radiology and Surgery (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
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
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 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
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
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
Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor
Junichi Nakagawa, Noriyuki Fujima, Kenji Hirata, et al.
Cancer Imaging (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 12
Bladder Cancer and Artificial Intelligence
Mark Laurie, Steve Zhou, Md Tauhidul Islam, et al.
Urologic Clinics of North America (2023) Vol. 51, Iss. 1, pp. 63-75
Closed Access | Times Cited: 6
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
Artificial Intelligence-Based Classification and Segmentation of Bladder Cancer in Cystoscope Images
Wonku Hwang, Seon Beom Jo, Da Eun Han, et al.
Cancers (2024) Vol. 17, Iss. 1, pp. 57-57
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
Expression and prognostic value of carbonic anhydrase IX (CA-IX) in bladder urothelial carcinoma
Anping Xiang, Xiaonong Chen, Peng‐Fei Xu, et al.
BMC Urology (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 9
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
Virtual biopsy in abdominal pathology: where do we stand?
Arianna Defeudis, Jovana Panić, Giulia Nicoletti, et al.
BJR|Open (2023) Vol. 5, Iss. 1
Open Access | Times Cited: 5
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
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