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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 clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy
Paul Doolan, Stefanie Charalambous, Yiannis Roussakis, et al.
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 39

Showing 1-25 of 39 citing articles:

Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives
Ayhan Can Erdur, Daniel Rusche, Daniel Scholz, et al.
Strahlentherapie und Onkologie (2024)
Open Access | Times Cited: 11
NRG Oncology Assessment of Artificial Intelligence Deep Learning–Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions
Yi Rong, Quan Chen, Yabo Fu, et al.
International Journal of Radiation Oncology*Biology*Physics (2023) Vol. 119, Iss. 1, pp. 261-280
Closed Access | Times Cited: 17
Generation and evaluation of anatomy‐preserving virtual CT for online adaptive proton therapy
Suryakant Kaushik, Jakob Ödén, Dayananda Sharma, et al.
Medical Physics (2024) Vol. 51, Iss. 3, pp. 1536-1546
Open Access | Times Cited: 5
Guidance on selecting and evaluating AI auto-segmentation systems in clinical radiotherapy: insights from a six-vendor analysis
Branimir Rusanov, Martin A. Ebert, Mahsheed Sabet, et al.
Physical and Engineering Sciences in Medicine (2025)
Open Access
Under-representation for Female Pelvis Cancers in Commercial Auto-segmentation Solutions and Open-source Imaging Datasets
Maria Thor, Vonetta M. Williams, Christian El Hajj, et al.
Clinical Oncology (2025), pp. 103651-103651
Closed Access
Evaluation and failure analysis of four commercial deep learning‐based autosegmentation software for abdominal organs at risk
Mingdong Fan, Tonghe Wang, Yang Lei, et al.
Journal of Applied Clinical Medical Physics (2025)
Open Access
Geometric and Dosimetric Evaluation of a RayStation Deep Learning Model for Auto-Segmentation of Organs at Risk in a Real-World Head and Neck Cancer Dataset
Dayananda Sharma, Gaganpreet Singh, Nagarjuna Burela, et al.
Clinical Oncology (2025) Vol. 41, pp. 103796-103796
Closed Access
Improving organ dose sparing in left‐sided breast cancer with yaw‐limited volumetric modulated arc therapy: A dosimetric comparison to conventional and intensity modulated radiation therapy approaches
Gerhard Pollul, Sascha Großmann, Heiko Karle, et al.
Journal of Applied Clinical Medical Physics (2025)
Open Access
Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review
Curtise K. C. Ng
Information (2025) Vol. 16, Iss. 3, pp. 215-215
Open Access
Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy
Jun Li, Wookjin Choi, Rani Anne
Technology in Cancer Research & Treatment (2025) Vol. 24
Open Access
Development and clinical implementation of a comprehensive multifractionation scheme HyperArc-based RapidPlan model for single and multiple brain metastases
Shirley McCarthy, William St Clair, Damodar Pokhrel
Medical dosimetry (2025)
Closed Access
Fully automated radiotherapy treatment planning: A scan to plan challenge
Mark J. Gooding, Shafak Aluwini, Teresa Guerrero Urbano, et al.
Radiotherapy and Oncology (2024) Vol. 200, pp. 110513-110513
Closed Access | Times Cited: 4
Deep learning for automated segmentation in radiotherapy: a narrative review
Jean‐Emmanuel Bibault, P Giraud
British Journal of Radiology (2023) Vol. 97, Iss. 1153, pp. 13-20
Open Access | Times Cited: 10
Evaluation of multiple-vendor AI autocontouring solutions
Lee Goddard, Christian Velten, Justin Tang, et al.
Radiation Oncology (2024) Vol. 19, Iss. 1
Open Access | Times Cited: 3
The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks on 18F-PSMA-1007 PET
Julius C. Holzschuh, Michael Mix, Martin T. Freitag, et al.
Radiation Oncology (2024) Vol. 19, Iss. 1
Open Access | Times Cited: 3
Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications
Hasan Cavus, P. Bulens, Koen Tournel, et al.
Physics and Imaging in Radiation Oncology (2024) Vol. 31, pp. 100627-100627
Open Access | Times Cited: 3
Real-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning
Gabriele Palazzo, P. Mangili, C.L. Deantoni, et al.
Physics and Imaging in Radiation Oncology (2023) Vol. 28, pp. 100501-100501
Open Access | Times Cited: 8
Clinical evaluation of the efficacy of limbus artificial intelligence software to augment contouring for prostate and nodes radiotherapy
Alison Starke, J. Poxon, Kishen Patel, et al.
British Journal of Radiology (2024) Vol. 97, Iss. 1158, pp. 1125-1131
Open Access | Times Cited: 2
The Evolving Role of Artificial Intelligence in Radiotherapy Treatment Planning—A Literature Review
Savraj Kalsi, Heleen French, Sheetal Chhaya, et al.
Clinical Oncology (2024) Vol. 36, Iss. 10, pp. 596-605
Closed Access | Times Cited: 2
Auto-delineation of treatment target volume for radiation therapy using large language model-aided multimodal learning
Praveenbalaji Rajendran, Yizheng Chen, Liang Qiu, et al.
International Journal of Radiation Oncology*Biology*Physics (2024)
Closed Access | Times Cited: 2
Clinical validation of commercial deep-learning based auto-segmentation models for organs at risk in the head and neck region: a single institution study
Casey L Johnson, Robert H. Press, Charles B. Simone, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 1
Incorporating patient-specific information for the development of rectal tumor auto-segmentation models for online adaptive magnetic resonance Image-guided radiotherapy
Chavelli M Kensen, Rita Simões, Anja Betgen, et al.
Physics and Imaging in Radiation Oncology (2024) Vol. 32, pp. 100648-100648
Open Access | Times Cited: 1
Artificial intelligence for treatment delivery: image-guided radiotherapy
Moritz Rabe, Christopher Kurz, Adrian Thummerer, et al.
Strahlentherapie und Onkologie (2024)
Closed Access | Times Cited: 1
Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Combining Multiple Quantitative Metrics Through Machine Learning
Ying Zhang, Asma Amjad, Jie Ding, et al.
Practical Radiation Oncology (2024) Vol. 15, Iss. 1, pp. 93-102
Closed Access | Times Cited: 1
Investigation on performance of multiple AI-based auto-contouring systems in organs at risks (OARs) delineation
Y. Kim, Simon Biggs, Elizabeth Claridge Mackonis
Physical and Engineering Sciences in Medicine (2024) Vol. 47, Iss. 3, pp. 1123-1140
Open Access | Times Cited: 1
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