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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:

Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study
Adrian Kobe, Juliana Zgraggen, Florian Messmer, et al.
European Journal of Radiology Open (2021) Vol. 8, pp. 100375-100375
Open Access | Times Cited: 18

Showing 18 citing articles:

Using Machine Learning to Predict Response to Image-guided Therapies for Hepatocellular Carcinoma
Celina Hsieh, Amanda Laguna, Ian Ikeda, et al.
Radiology (2023) Vol. 309, Iss. 2
Closed Access | Times Cited: 17
Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment
Vincenza Granata, Roberta Fusco, Federica De Muzio, et al.
La radiologia medica (2023) Vol. 128, Iss. 11, pp. 1310-1332
Closed Access | Times Cited: 13
Radiomics Features Extracted From Pre- and Postprocedural Imaging in Early Prediction of Treatment Response in Patients Undergoing Transarterial Radioembolization of Hepatic Lesions: A Systematic Review, Meta-Analysis, and Quality Appraisal Study
Mohammad Mirza‐Aghazadeh‐Attari, Tara Srinivas, Arun Kamireddy, et al.
Journal of the American College of Radiology (2024) Vol. 21, Iss. 5, pp. 740-751
Closed Access | Times Cited: 5
TARE Tedavilerinde CBCT ve SPECT/CT Doz Uygulama Korelasyonu
Serdar Davran, Ferhat Can Pişkin, Ebru Şimşek, et al.
Arşiv Kaynak Tarama Dergisi (2025) Vol. 34, Iss. 1, pp. 61-67
Open Access
Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review
Keyur Radiya, Henrik Lykke Joakimsen, Karl Øyvind Mikalsen, et al.
European Radiology (2023) Vol. 33, Iss. 10, pp. 6689-6717
Open Access | Times Cited: 10
Prediction of Response of Hepatocellular Carcinoma to Radioembolization: Machine Learning Using Preprocedural Clinical Factors and MR Imaging Radiomics
Okan İnce, Hakan Önder, Mehmet Gençtürk, et al.
Journal of Vascular and Interventional Radiology (2022) Vol. 34, Iss. 2, pp. 235-243.e3
Closed Access | Times Cited: 11
Technological Advancements in Interventional Oncology
Alessandro Posa, Pierluigi Barbieri, Giulia Mazza, et al.
Diagnostics (2023) Vol. 13, Iss. 2, pp. 228-228
Open Access | Times Cited: 5
Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics
Vincenza Granata, Roberta Fusco, Sergio Venanzio Setola, et al.
Infectious Agents and Cancer (2023) Vol. 18, Iss. 1
Open Access | Times Cited: 5
Predictability of the radiological response to Yttrium-90 transarterial radioembolization by dynamic magnetic resonance imaging-based radiomics analysis in patients with intrahepatic cholangiocarcinoma
Hüseyin Tuğsan Ballı, Ferhat Can Pişkin, Sevinç Püren Yücel, et al.
Diagnostic and Interventional Radiology (2023) Vol. 30, Iss. 3, pp. 193-199
Open Access | Times Cited: 5
Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors
Liuji Sheng, Chongtu Yang, Yidi Chen, et al.
Biomedicines (2023) Vol. 12, Iss. 1, pp. 58-58
Open Access | Times Cited: 5
Perspectives of Cone-beam Computed Tomography in Interventional Radiology: Techniques for Planning, Guidance, and Monitoring
Matthias Barral, Olivier Chevallier, F. Cornélis
Techniques in vascular and interventional radiology (2023) Vol. 26, Iss. 3, pp. 100912-100912
Closed Access | Times Cited: 4
Radiomics-Based Prediction Model for Outcome of Radioembolization in Metastatic Colorectal Cancer
Wolfgang Roll, Max Masthoff, Michael Köhler, et al.
CardioVascular and Interventional Radiology (2024) Vol. 47, Iss. 4, pp. 462-471
Closed Access | Times Cited: 1
New frontiers in radioembolization
Arian Mansur, Peiman Habibollahi, Adam Fang, et al.
Therapeutic Advances in Medical Oncology (2024) Vol. 16
Open Access | Times Cited: 1
Creation of a Prediction Model of Local Tumor Recurrence After a Successful Conventional Transcatheter Arterial Chemoembolization Using Cone-Beam Computed Tomography Based–Radiomics
Kazuki Hashimoto, Takafumi Haraguchi, Shintaro Nawata, et al.
CardioVascular and Interventional Radiology (2024) Vol. 47, Iss. 11, pp. 1495-1505
Closed Access | Times Cited: 1
Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis
Linyong Wu, Qingfeng Lai, Songhua Li, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 1
Research progress on machine algorithm prediction of liver cancer prognosis after intervention therapy
Feng Guo, Hao Hu, Hao Peng, et al.
American Journal of Cancer Research (2024) Vol. 14, Iss. 9, pp. 4580-4596
Closed Access
The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review
Sonia Triggiani, Maria Contaldo, Giulia Mastellone, et al.
Critical Reviews™ in Oncogenesis (2023) Vol. 29, Iss. 2, pp. 37-52
Closed Access
Imaging in interventional oncology, the better you see, the better you treat
Fernando M. Gómez, Denise J. van der Reijd, Ilia A Panfilov, et al.
Journal of Medical Imaging and Radiation Oncology (2023) Vol. 67, Iss. 8, pp. 895-902
Closed Access
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