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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:

A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset
Leandro Donisi, Giuseppe Cesarelli, Anna Castaldo, et al.
Journal of Imaging (2021) Vol. 7, Iss. 10, pp. 215-215
Open Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

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
Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects
Ahmad Chaddad, Guina Tan, Xiaojuan Liang, et al.
Cancers (2023) Vol. 15, Iss. 15, pp. 3839-3839
Open Access | Times Cited: 28
Artificial intelligence in multiparametric magnetic resonance imaging: A review
Cheng Li, Wen Li, Chenyang Liu, et al.
Medical Physics (2022) Vol. 49, Iss. 10
Closed Access | Times Cited: 32
Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography
Mario Sansone, Roberta Fusco, Francesca Grassi, et al.
Current Oncology (2023) Vol. 30, Iss. 1, pp. 839-853
Open Access | Times Cited: 16
Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer with Supervised Machine Learning Methods
Hamide Nematollahi, Masoud Moslehi, Fahimeh Aminolroayaei, et al.
Diagnostics (2023) Vol. 13, Iss. 4, pp. 806-806
Open Access | Times Cited: 16
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
Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study
Kexin Wang, Ning Luo, Zhaonan Sun, et al.
Insights into Imaging (2025) Vol. 16, Iss. 1
Open Access
Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques
Luis Esteban, Ángel Borque‐Fernando, Marta Escorihuela, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks
R. Kiruthigha, Suresh Jaganathan
Diagnostics (2023) Vol. 13, Iss. 6, pp. 1143-1143
Open Access | Times Cited: 7
MRI-based Radiomics for Predicting Prostate Cancer Grade Groups: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies
Nima Broomand Lomer, Mohammad Amin Ashoobi, Amir Mahmoud Ahmadzadeh, et al.
Academic Radiology (2024)
Closed Access | Times Cited: 2
Feasibility of Tree-based Machine Learning algorithms fed with surface electromyographic features to discriminate risk classes according to NIOSH
Leandro Donisi, E Capodaglio, Gaetano Pagano, et al.
2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (2022), pp. 1-6
Closed Access | Times Cited: 11
Advancements in traditional machine learning techniques for detection and diagnosis of fatal cancer types: Comprehensive review of biomedical imaging datasets
Hari Mohan, Joon Yoo, Syed Atif Moqurrab, et al.
Measurement (2023) Vol. 225, pp. 114059-114059
Closed Access | Times Cited: 5
Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences
Eugenia Mylona, Dimitrios I. Zaridis, Charalampos Kalantzopoulos, et al.
Insights into Imaging (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 1
Breast Density Analysis on Mammograms: Application of Machine Learning with Textural Features
Francesca Angelone, Carlo Ricciardi, Gianluca Gatta, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2022), pp. 295-300
Closed Access | Times Cited: 7
Feasibility of Tree-Based Machine Learning Models to Discriminate Safe and Unsafe Posture During Weight Lifting
G. Prisco, Maria Romano, Fabrizio Esposito, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2023), pp. 870-875
Closed Access | Times Cited: 3
Effect of X-ray scatter correction on the estimation of attenuation coefficient in mammography: a simulation study
Mario Sansone, Alfonso Maria Ponsiglione, Francesca Angelone, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2022) Vol. 4, pp. 323-328
Closed Access | Times Cited: 4
Machine Learning and Biosignals are able to discriminate biomechanical risk classes according to the Revised NIOSH Lifting Equation
Leandro Donisi, Giuseppe Cesarelli, E Capodaglio, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2022)
Closed Access | Times Cited: 4
A general framework for the assessment of scatter correction techniques in digital mammography
Francesca Angelone, Alfonso Maria Ponsiglione, Roberto Grassi, et al.
Biomedical Signal Processing and Control (2023) Vol. 89, pp. 105802-105802
Open Access | Times Cited: 2
A Cluster Analysis for Parkinson's Disease Phenotyping with Gait Parameters
Michela Russo, Carlo Ricciardi, Marianna Amboni, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2023), pp. 882-887
Closed Access | Times Cited: 2
Comparative analysis of SVM and k-nearest neighbor classification algorithm in fingerprint detection
R. Sravanthi, A. Gnana Soundari
AIP conference proceedings (2024) Vol. 3097, pp. 020129-020129
Closed Access
Evaluating the Feasibility of AI-Predicted mpMRI Image Features for Predicting Prostate Cancer Aggressiveness: a Multicenter Study
Kexin Wang, Ning Luo, Zhaonan Sun, et al.
Research Square (Research Square) (2024)
Closed Access
Using Wearable Sensors and Motion Parameters for Recognizing Progressive Supranuclear Palsy Phenotypes
Noemi Pisani, Carlo Ricciardi, Marina Picillo, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2023), pp. 864-869
Closed Access | Times Cited: 1
Predicting lifestyle using BioVRSea multi-biometric paradigms
Marco Recenti, Deborah Jacob, Romain Aubonnet, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2022) Vol. 46, pp. 329-334
Closed Access | Times Cited: 2
A combined simulation and machine learning approach to classify severity of infarction patients
Anna Procopio, Giuseppe Cesarelli, Salvatore De Rosa, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2022) Vol. 24, pp. 283-288
Closed Access | Times Cited: 2
A Machine Learning approach to classify ventilatory efficiency
G. Prisco, Klara Komici, Francesco Mercaldo, et al.
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (2023), pp. 646-651
Closed Access
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