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:
CT-based deep learning radiomics and hematological biomarkers in the assessment of pathological complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma: A two-center study
Meng Zhang, Yukun Lu, Hongfu Sun, et al.
Translational Oncology (2023) Vol. 39, pp. 101804-101804
Open Access | Times Cited: 7
Meng Zhang, Yukun Lu, Hongfu Sun, et al.
Translational Oncology (2023) Vol. 39, pp. 101804-101804
Open Access | Times Cited: 7
Showing 7 citing articles:
A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors
Wei‐Xiang Qi, Shuyan Li, Ji-Feng Xiao, et al.
Frontiers in Immunology (2024) Vol. 15
Open Access | Times Cited: 4
Wei‐Xiang Qi, Shuyan Li, Ji-Feng Xiao, et al.
Frontiers in Immunology (2024) Vol. 15
Open Access | Times Cited: 4
A preoperative predictive model based on multi-modal features to predict pathological complete response after neoadjuvant chemoimmunotherapy in esophageal cancer patients
Yana Qi, Yanran Hu, Lin Cai, et al.
Frontiers in Immunology (2025) Vol. 16
Open Access
Yana Qi, Yanran Hu, Lin Cai, et al.
Frontiers in Immunology (2025) Vol. 16
Open Access
Deep Learning in Oncology: Transforming Cancer Diagnosis, Prognosis, and Treatment
Thaís Santos Anjo Reis
Emerging Trends in Drugs Addictions and Health (2025), pp. 100171-100171
Open Access
Thaís Santos Anjo Reis
Emerging Trends in Drugs Addictions and Health (2025), pp. 100171-100171
Open Access
Self-supervised network predicting neoadjuvant chemoradiotherapy response to locally advanced rectal cancer patients
Qian Chen, Jun Dang, Yuanyuan Wang, et al.
Computerized Medical Imaging and Graphics (2025), pp. 102552-102552
Closed Access
Qian Chen, Jun Dang, Yuanyuan Wang, et al.
Computerized Medical Imaging and Graphics (2025), pp. 102552-102552
Closed Access
Application of predictive model based on CT radiomics and machine learning in diagnosis for occult locally advanced esophageal squamous cell carcinoma before treatment: A two-center study
Shuhan Xie, Wanfei Zhang, Yue Wu, et al.
Translational Oncology (2024) Vol. 47, pp. 102050-102050
Open Access | Times Cited: 3
Shuhan Xie, Wanfei Zhang, Yue Wu, et al.
Translational Oncology (2024) Vol. 47, pp. 102050-102050
Open Access | Times Cited: 3
Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma
Yuli Ruan, Yue Ma, Ming Ma, et al.
Journal of Translational Medicine (2024) Vol. 22, Iss. 1
Open Access | Times Cited: 2
Yuli Ruan, Yue Ma, Ming Ma, et al.
Journal of Translational Medicine (2024) Vol. 22, Iss. 1
Open Access | Times Cited: 2
Integrating MR radiomics and dynamic hematological factors predicts pathological response to neoadjuvant chemoradiotherapy in esophageal cancer
Yunsong Liu, Zeliang Ma, Yongxing Bao, et al.
Heliyon (2024) Vol. 10, Iss. 13, pp. e33702-e33702
Open Access | Times Cited: 2
Yunsong Liu, Zeliang Ma, Yongxing Bao, et al.
Heliyon (2024) Vol. 10, Iss. 13, pp. e33702-e33702
Open Access | Times Cited: 2
