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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:

Recent Radiomics Advancements in Breast Cancer: Lessons and Pitfalls for the Next Future
Filippo Pesapane, Anna Rotili, Giorgio Maria Agazzi, et al.
Current Oncology (2021) Vol. 28, Iss. 4, pp. 2351-2372
Open Access | Times Cited: 54

Showing 1-25 of 54 citing articles:

Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging
Reabal Najjar
Diagnostics (2023) Vol. 13, Iss. 17, pp. 2760-2760
Open Access | Times Cited: 271
Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging
Reabal Najjar
(2023)
Open Access | Times Cited: 42
Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence
Hiroko Satake, Satoko Ishigaki, Rintaro Ito, et al.
La radiologia medica (2021) Vol. 127, Iss. 1, pp. 39-56
Closed Access | Times Cited: 67
How Radiomics Can Improve Breast Cancer Diagnosis and Treatment
Filippo Pesapane, P. De Marco, Anna Rapino, et al.
Journal of Clinical Medicine (2023) Vol. 12, Iss. 4, pp. 1372-1372
Open Access | Times Cited: 37
Intra‐ and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer
Wenyan Jiang, Ruiqing Meng, Yuan Cheng, et al.
Journal of Magnetic Resonance Imaging (2023) Vol. 59, Iss. 2, pp. 613-625
Closed Access | Times Cited: 25
Advancements in AI based healthcare techniques with FOCUS ON diagnostic techniques
Nishita Kalra, Prachi Verma, Surajpal Verma
Computers in Biology and Medicine (2024) Vol. 179, pp. 108917-108917
Closed Access | Times Cited: 10
An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies
Roberta Fusco, Vincenza Granata, Igino Simonetti, et al.
Current Oncology (2024) Vol. 31, Iss. 1, pp. 403-424
Open Access | Times Cited: 9
Deep learning performance for detection and classification of microcalcifications on mammography
Filippo Pesapane, Chiara Trentin, Federica Ferrari, et al.
European Radiology Experimental (2023) Vol. 7, Iss. 1
Open Access | Times Cited: 19
Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment
Josip Vrdoljak, Zvonimir Boban, Domjan Barić, et al.
Cancers (2023) Vol. 15, Iss. 3, pp. 634-634
Open Access | Times Cited: 16
Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis
Humayra Afrin, Nicholas B. Larson, Mostafa Fatemi, et al.
Cancers (2023) Vol. 15, Iss. 12, pp. 3139-3139
Open Access | Times Cited: 16
The application of radiomics in cancer imaging with a focus on lung cancer, renal cell carcinoma, gastrointestinal cancer, and head and neck cancer: A systematic review
Roberta Fusco, Vincenza Granata, Sergio Venanzio Setola, et al.
Physica Medica (2025) Vol. 130, pp. 104891-104891
Closed Access
Machine learning for predicting neoadjuvant chemotherapy effectiveness using ultrasound radiomics features and routine clinical data of patients with breast cancer
Pu Zhou, Hongyan Qian, Pengfei Zhu, et al.
Frontiers in Oncology (2025) Vol. 14
Open Access
Combined morphology and radiomics of intravoxel incoherent movement as a predictive model for the pathologic complete response before neoadjuvant chemotherapy in patients with breast cancer
Yunyan Zheng, Hui Zhang, Huijian Chen, et al.
Frontiers in Oncology (2025) Vol. 15
Open Access
Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients With MRI-Radiomics: A Systematic Review and Meta-analysis
Filippo Pesapane, Giorgio Maria Agazzi, Anna Rotili, et al.
Current Problems in Cancer (2022) Vol. 46, Iss. 5, pp. 100883-100883
Closed Access | Times Cited: 26
Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review
Omofolarin Debellotte, Richard L Dookie, FNU Rinkoo, et al.
Cureus (2025)
Open Access
Radiomics of MRI for the Prediction of the Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer Patients: A Single Referral Centre Analysis
Filippo Pesapane, Anna Rotili, Francesca Botta, et al.
Cancers (2021) Vol. 13, Iss. 17, pp. 4271-4271
Open Access | Times Cited: 31
Legal and Regulatory Framework for AI Solutions in Healthcare in EU, US, China, and Russia: New Scenarios after a Pandemic
Filippo Pesapane, Daniele Alberto Bracchi, Janice F. Mulligan, et al.
Radiation (2021) Vol. 1, Iss. 4, pp. 261-276
Open Access | Times Cited: 31
Advances in Breast Cancer Risk Modeling: Integrating Clinics, Imaging, Pathology and Artificial Intelligence for Personalized Risk Assessment
Filippo Pesapane, Ottavia Battaglia, Giuseppe Pellegrino, et al.
Future Oncology (2023) Vol. 19, Iss. 38, pp. 2547-2564
Closed Access | Times Cited: 9
Ultrasound Radiomics‐Based Logistic Regression Model to Differentiate Between Benign and Malignant Breast Nodules
Shanshan Shi, Xin An, Yuhong Li
Journal of Ultrasound in Medicine (2022) Vol. 42, Iss. 4, pp. 869-879
Closed Access | Times Cited: 12
Can multi-modal radiomics using pretreatment ultrasound and tomosynthesis predict response to neoadjuvant systemic treatment in breast cancer?
Lie Cai, Chris Sidey‐Gibbons, Juliane Nees, et al.
European Radiology (2023) Vol. 34, Iss. 4, pp. 2560-2573
Open Access | Times Cited: 7
Prediction of neoadjuvant chemotherapy pathological complete response for breast cancer based on radiomics nomogram of intratumoral and derived tissue
Guangying Zheng, Jie Hou, Zhenyu Shu, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 2
Empowering brain cancer diagnosis: harnessing artificial intelligence for advanced imaging insights
Omar S. Al-Kadi, Roa’a Al-Emaryeen, Sara Al-Nahhas, et al.
Reviews in the Neurosciences (2024) Vol. 35, Iss. 4, pp. 399-419
Closed Access | Times Cited: 2
Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer
Zi Huai Huang, Lianghong Chen, Yan Sun, et al.
Journal of Translational Medicine (2024) Vol. 22, Iss. 1
Open Access | Times Cited: 2
Technical Review of Breast Cancer Screening and Detection using Artificial Intelligence and Radiomics
Arshpreet Singh, Simranpreet Kaur, Deepjyot Singh, et al.
2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) (2024), pp. 1171-1176
Closed Access | Times Cited: 2
Ultrasound-based radiomics nomogram for predicting HER2-low expression breast cancer
Xueling Zhang, Shaoyou Wu, Xiao Zu, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 2
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