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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 1-25 of 87 citing articles:

A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis
Lian Zou, Shaode Yu, Tiebao Meng, et al.
Computational and Mathematical Methods in Medicine (2019) Vol. 2019, pp. 1-16
Open Access | Times Cited: 109
Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study
Caixia Sun, Xin Tian, Zhenyu Liu, et al.
EBioMedicine (2019) Vol. 46, pp. 160-169
Open Access | Times Cited: 87
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
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
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 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
A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors
Xiaopan Xu, Huanjun Wang, Peng Du, et al.
Journal of Magnetic Resonance Imaging (2019) Vol. 50, Iss. 6, pp. 1893-1904
Open Access | Times Cited: 68
Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma
Hao Wang, Bin Song, Ningrong Ye, et al.
European Journal of Radiology (2019) Vol. 122, pp. 108755-108755
Closed Access | Times Cited: 68
Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach
Baoyu Yan, Xiaopan Xu, Mengwan Liu, et al.
Frontiers in Neuroscience (2020) Vol. 14
Open Access | Times Cited: 61
Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study
Huanjun Wang, Xiaopan Xu, Xi Zhang, et al.
European Radiology (2020) Vol. 30, Iss. 9, pp. 4816-4827
Closed Access | Times Cited: 59
MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study
Haimei Chen, Xiao Zhang, Xiaohong Wang, et al.
European Radiology (2021) Vol. 31, Iss. 10, pp. 7913-7924
Closed Access | Times Cited: 48
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
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
Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer
Shuaishuai Xu, Qiuying Yao, Guiqin Liu, et al.
European Radiology (2019) Vol. 30, Iss. 3, pp. 1804-1812
Closed Access | Times Cited: 50
Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study
Noriyuki Fujima, Yukie Shimizu, Daisuke Yoshida, et al.
Cancers (2019) Vol. 11, Iss. 6, pp. 800-800
Open Access | Times Cited: 44
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
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
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
Radiomics Signatures of Computed Tomography Imaging for Predicting Risk Categorization and Clinical Stage of Thymomas
Xihai Wang, Wei Sun, Hongyuan Liang, et al.
BioMed Research International (2019) Vol. 2019, pp. 1-10
Open Access | Times Cited: 40
Radiogenomics of neuroblastoma in pediatric patients: CT-based radiomics signature in predicting MYCN amplification
Haoting Wu, Chenqing Wu, Hui Zheng, et al.
European Radiology (2020) Vol. 31, Iss. 5, pp. 3080-3089
Closed Access | Times Cited: 38
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
Invasive Cancerous Area Detection in Non-Muscle Invasive Bladder Cancer Whole Slide Images
Saul Fuster, Farbod Khoraminia, Umay Kiraz, et al.
(2022), pp. 1-5
Open Access | Times Cited: 20
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
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