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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.

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Showing 12 citing articles:

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis
Zhi Yang, Jie Gong, Jie Li, et al.
International Journal of Surgery (2023) Vol. 109, Iss. 8, pp. 2451-2466
Open Access | Times Cited: 15
Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of-texture features
Aryan Safakish, Lakshmanan Sannachi, Daniel DiCenzo, et al.
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 10
CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy
Kartik Iyer, Cameron Beeche, Naciye Sinem Gezer, et al.
Journal of Clinical Medicine (2023) Vol. 12, Iss. 6, pp. 2106-2106
Open Access | Times Cited: 8
Deep Texture Analysis—Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach
Aryan Safakish, Lakshmanan Sannachi, Amir Moslemi, et al.
Radiation (2024) Vol. 4, Iss. 1, pp. 50-68
Open Access | Times Cited: 2
Radiation-Induced Esophageal Cancer: Investigating the Pathogenesis, Management, and Prognosis
Athanasios Syllaios, Michail Vailas, Maria Tolia, et al.
Medicina (2022) Vol. 58, Iss. 7, pp. 949-949
Open Access | Times Cited: 6
Radiomic applications in upper gastrointestinal cancer surgery
Joseph Doyle, Pranav Patel, Nikoletta A. Petrou, et al.
Langenbeck s Archives of Surgery (2023) Vol. 408, Iss. 1
Closed Access | Times Cited: 3
Predicting Head & Neck Cancer Treatment Outcomes with Quantitative Ultrasound Texture Features & Optimizing Machine Learning Classifiers with Novel Texture-of-Texture Features
Aryan Safakish, Lakshmanan Sannachi, Daniel DiCenzo, et al.
(2024)
Open Access
Predicting Head & Neck Cancer Treatment Outcomes with Quantitative Ultrasound Texture Features & Optimizing Machine Learning Classifiers with Novel Texture-of-Texture Features
Aryan Safakish, Lakshmanan Sannachi, Daniel DiCenzo, et al.
(2024)
Open Access
Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach
Aryan Safakish, Lakshmanan Sannachi, Amir Moslemi, et al.
(2024)
Open Access
Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach
Aryan Safakish, Lakshmanan Sannachi, Amir Moslemi, et al.
(2024)
Open Access
Deep Texture Analysis Enhanced MRI Radiomics for Predicting Head and Neck Cancer Treatment Outcomes with Machine Learning Classifiers
Aryan Safakish, Amir Moslemi, Daniel Moore-Palhares, et al.
Radiation (2024) Vol. 4, Iss. 2, pp. 192-212
Open Access
The application of machine learning and deep learning radiomics in the treatment of esophageal cancer
Jinling Yi, Yibo Wu, Boda Ning, et al.
Radiation Medicine and Protection (2023) Vol. 4, Iss. 4, pp. 182-189
Open Access | Times Cited: 1
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