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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:

Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications
Jose M. Castillo T., Muhammad Arif, Wiro J. Niessen, et al.
Cancers (2020) Vol. 12, Iss. 6, pp. 1606-1606
Open Access | Times Cited: 72

Showing 1-25 of 72 citing articles:

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative
Gaia Spadarella, Arnaldo Stanzione, Tugba Akinci D’Antonoli, et al.
European Radiology (2022) Vol. 33, Iss. 3, pp. 1884-1894
Open Access | Times Cited: 81
ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans
Audrey Duran, Gaspard Dussert, Olivier Rouvière, et al.
Medical Image Analysis (2022) Vol. 77, pp. 102347-102347
Open Access | Times Cited: 76
Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential
Kruthi Suvarna, Deeptarup Biswas, Medha Gayathri J. Pai, et al.
Frontiers in Physiology (2021) Vol. 12
Open Access | Times Cited: 67
Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review
Jasper Jonathan Twilt, Kicky G. van Leeuwen, Henkjan Huisman, et al.
Diagnostics (2021) Vol. 11, Iss. 6, pp. 959-959
Open Access | Times Cited: 65
Machine and Deep Learning Prediction Of Prostate Cancer Aggressiveness Using Multiparametric MRI
Elena Bertelli, Laura Mercatelli, Chiara Marzi, et al.
Frontiers in Oncology (2022) Vol. 11
Open Access | Times Cited: 46
Artificial intelligence for prostate MRI: open datasets, available applications, and grand challenges
Mohammed R. S. Sunoqrot, Anindo Saha, Matin Hosseinzadeh, et al.
European Radiology Experimental (2022) Vol. 6, Iss. 1
Open Access | Times Cited: 41
Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review
Nikita Sushentsev, Nadia Moreira Da Silva, Michael Yeung, et al.
Insights into Imaging (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 40
Biomarkers of Aggressive Prostate Cancer at Diagnosis
Brock E. Boehm, Monica E. York, György Petrovics, et al.
International Journal of Molecular Sciences (2023) Vol. 24, Iss. 3, pp. 2185-2185
Open Access | Times Cited: 37
Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI
Hasan Khanfari, Saeed Mehranfar, Mohsen Cheki, et al.
BMC Medical Imaging (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 32
A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation
Jingyu Zhong, Yangfan Hu, Liping Si, et al.
European Radiology (2020) Vol. 31, Iss. 3, pp. 1526-1535
Closed Access | Times Cited: 62
Cross‐shaped windows transformer with self‐supervised pretraining for clinically significant prostate cancer detection in bi‐parametric MRI
Yuheng Li, Jacob Wynne, Jing Wang, et al.
Medical Physics (2024) Vol. 52, Iss. 2, pp. 993-1004
Open Access | Times Cited: 7
ProstAtlasDiff: Prostate cancer detection on MRI using Diffusion Probabilistic Models guided by population spatial cancer atlases
Cynthia Li, Indrani Bhattacharya, Sulaiman Vesal, et al.
Medical Image Analysis (2025) Vol. 101, pp. 103486-103486
Closed Access
A descriptive framework for the field of deep learning applications in medical images
Yingjie Tian, Saiji Fu
Knowledge-Based Systems (2020) Vol. 210, pp. 106445-106445
Closed Access | Times Cited: 39
A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade
Jose M. Castillo T., Martijn P. A. Starmans, Muhammad Arif, et al.
Diagnostics (2021) Vol. 11, Iss. 2, pp. 369-369
Open Access | Times Cited: 39
Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics
Jose M. Castillo T., Muhammad Arif, Martijn P. A. Starmans, et al.
Cancers (2021) Vol. 14, Iss. 1, pp. 12-12
Open Access | Times Cited: 32
Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? – A systematic review
Olivier Rouvière, Tristan Jaouen, Pierre Baseilhac, et al.
Diagnostic and Interventional Imaging (2022) Vol. 104, Iss. 5, pp. 221-234
Open Access | Times Cited: 24
MRI‐based prostate cancer classification using 3D efficient capsule network
Yuheng Li, Jacob Wynne, Jing Wang, et al.
Medical Physics (2024) Vol. 51, Iss. 7, pp. 4748-4758
Closed Access | Times Cited: 4
Investigating feature extraction by SIFT methods for prostate cancer early detection
Shadan Mohammed Jihad, Firas Husham Almukhtar, Firas Husham Almukhtar, et al.
Egyptian Informatics Journal (2025) Vol. 29, pp. 100607-100607
Open Access
Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis
Zhenlin Chen, Zhihao Li, Ruiling Dou, et al.
BMC Cancer (2025) Vol. 25, Iss. 1
Open Access
Machine Learning and Clinical-Radiological Characteristics for the Classification of Prostate Cancer in PI-RADS 3 Lesions
Michela Gravina, Lorenzo Spirito, Giuseppe Celentano, et al.
Diagnostics (2022) Vol. 12, Iss. 7, pp. 1565-1565
Open Access | Times Cited: 21
A deep learning masked segmentation alternative to manual segmentation in biparametric MRI prostate cancer radiomics
Jeroen Bleker, Thomas C. Kwee, Dennis B. Rouw, et al.
European Radiology (2022) Vol. 32, Iss. 9, pp. 6526-6535
Open Access | Times Cited: 20
Deep Learning in Prostate Cancer Diagnosis Using Multiparametric Magnetic Resonance Imaging With Whole-Mount Histopathology Referenced Delineations
Danyan Li, Xiaowei Han, Jie Gao, et al.
Frontiers in Medicine (2022) Vol. 8
Open Access | Times Cited: 19
Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review
Ahmad Chaddad, Michael Jonathan Kucharczyk, Abbas Cheddad, et al.
Cancers (2021) Vol. 13, Iss. 3, pp. 552-552
Open Access | Times Cited: 26
Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review
Meilinuer Abdurixiti, Mayila Nijiati, Rongfang Shen, et al.
British Journal of Radiology (2021) Vol. 94, Iss. 1122
Open Access | Times Cited: 23
Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers
Stefano Cipollari, Martina Pecoraro, Alì Forookhi, et al.
La radiologia medica (2022) Vol. 127, Iss. 11, pp. 1245-1253
Open Access | Times Cited: 16
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