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 extranodal extension in head and neck squamous cell carcinoma by CT images using an evolutionary learning model
Tzu‐Ting Huang, Yi-Chen Lin, Chia-Heng Yen, et al.
Cancer Imaging (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 7
Tzu‐Ting Huang, Yi-Chen Lin, Chia-Heng Yen, et al.
Cancer Imaging (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 7
Showing 7 citing articles:
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
Aryan Safakish, Lakshmanan Sannachi, Amir Moslemi, et al.
Radiation (2024) Vol. 4, Iss. 1, pp. 50-68
Open Access | Times Cited: 2
A Radiomics Approach to Identify Immunologically Active Tumor in Patients with Head and Neck Squamous Cell Carcinomas
Tan Mai Nguyen, Chloé Bertolus, P Giraud, et al.
Cancers (2023) Vol. 15, Iss. 22, pp. 5369-5369
Open Access | Times Cited: 5
Tan Mai Nguyen, Chloé Bertolus, P Giraud, et al.
Cancers (2023) Vol. 15, Iss. 22, pp. 5369-5369
Open Access | Times Cited: 5
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
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
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 Freitas Palhares, et al.
Radiation (2024) Vol. 4, Iss. 2, pp. 192-212
Open Access
Aryan Safakish, Amir Moslemi, Daniel Moore Freitas Palhares, et al.
Radiation (2024) Vol. 4, Iss. 2, pp. 192-212
Open Access
Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology
Rahul Alapati, Bryan Renslo, Sarah F. Wagoner, et al.
The Laryngoscope (2024) Vol. 135, Iss. 2, pp. 687-694
Closed Access
Rahul Alapati, Bryan Renslo, Sarah F. Wagoner, et al.
The Laryngoscope (2024) Vol. 135, Iss. 2, pp. 687-694
Closed Access
Predictors of extra nodal extension in oral squamous cell carcinoma: A retrospective analysis
Kinjal Shankar Majumdar, Vikramjit Singh Kailey, Abhinav Thaduri, et al.
Cancer Research Statistics and Treatment (2024) Vol. 7, Iss. 4, pp. 421-427
Open Access
Kinjal Shankar Majumdar, Vikramjit Singh Kailey, Abhinav Thaduri, et al.
Cancer Research Statistics and Treatment (2024) Vol. 7, Iss. 4, pp. 421-427
Open Access
