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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:

Computed Tomography-Based Radiomics in Predicting T Stage and Length of Esophageal Squamous Cell Carcinoma
Mingwei Yang, Panpan Hu, Minglun Li, et al.
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 25

Showing 25 citing articles:

Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma
Xiaotao Geng, Yaping Zhang, Yang Li, et al.
British Journal of Radiology (2024) Vol. 97, Iss. 1155, pp. 652-659
Open Access | Times Cited: 7
A preoperative pathological staging prediction model for esophageal cancer based on CT radiomics
Haojun Li, Shuo Liang, Mengxuan Cui, et al.
BMC Cancer (2025) Vol. 25, Iss. 1
Open Access
Artificial intelligence in early screening for esophageal squamous cell carcinoma
Si-yan Yan, Xin-yu Fu, Yan Yang, et al.
Best Practice & Research Clinical Gastroenterology (2025), pp. 102004-102004
Open Access
Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy
Nuo Yu, Yidong Wan, Lijing Zuo, et al.
BMC Cancer (2025) Vol. 25, Iss. 1
Open Access
Global research trends of artificial intelligence applied in esophageal carcinoma: A bibliometric analysis (2000-2022) via CiteSpace and VOSviewer
Jiaxin Tu, Xue-Ting Lin, Hui-Qing Ye, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 17
Research Progress on the Predicting Factors and Coping Strategies for Postoperative Recurrence of Esophageal Cancer
Yujie Zhang, Yuxin Zhang, Lin Peng, et al.
Cells (2022) Vol. 12, Iss. 1, pp. 114-114
Open Access | Times Cited: 17
Preoperative prediction of clinical and pathological stages for patients with esophageal cancer using PET/CT radiomics
Xiyao Lei, Z. Alexander Cao, Yibo Wu, et al.
Insights into Imaging (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 9
Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach
Xiaoli Wu, Hao Wu, Shouliang Miao, et al.
BMC Medical Informatics and Decision Making (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 3
Role of radiomics in the diagnosis and treatment of gastrointestinal cancer
Qi Mao, Mao-Ting Zhou, Zhang-Ping Zhao, et al.
World Journal of Gastroenterology (2022) Vol. 28, Iss. 42, pp. 6002-6016
Open Access | Times Cited: 14
Preoperative Prediction of Perineural Invasion in Oesophageal Squamous Cell Carcinoma Based on CT Radiomics Nomogram: A Multicenter Study
Hui Zhou, Jian Zhou, Cai Qin, et al.
Academic Radiology (2023) Vol. 31, Iss. 4, pp. 1355-1366
Closed Access | Times Cited: 6
Can 18F-FDG PET/CT Radiomics Features Predict Clinical Outcomes in Patients with Locally Advanced Esophageal Squamous Cell Carcinoma?
Vetri Sudar Jayaprakasam, Peter Gibbs, Natalie Gangai, et al.
Cancers (2022) Vol. 14, Iss. 12, pp. 3035-3035
Open Access | Times Cited: 10
Computed tomography radiomics identification of T1–2 and T3–4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional?
Yang Li, Yang Li, Xiaolong Gu, et al.
Abdominal Radiology (2023) Vol. 49, Iss. 1, pp. 288-300
Open Access | Times Cited: 5
Radiomics in Oesogastric Cancer: Staging and Prediction of Preoperative Treatment Response: A Narrative Review and the Results of Personal Experience
Giovanni Maria Garbarino, Michela Polici, Damiano Caruso, et al.
Cancers (2024) Vol. 16, Iss. 15, pp. 2664-2664
Open Access | Times Cited: 1
Value of Computed Tomography Scan for Detecting Lymph Node Metastasis in Early Esophageal Squamous Cell Carcinoma
Yunqing Zeng, Yaping Liu, Jinhou Li, et al.
Annals of Surgical Oncology (2024)
Closed Access | Times Cited: 1
CT radiomics in the identification of preoperative understaging in patients with clinical stage T1–2N0 esophageal squamous cell carcinoma
Bo Zhao, Shuo Yan, Zhengyan Jia, et al.
Quantitative Imaging in Medicine and Surgery (2023) Vol. 13, Iss. 12, pp. 7996-8008
Open Access | Times Cited: 3
Could tumour volume and major and minor axis based on CTA statistical anatomy improve the pre‐operative T‐stage in oesophageal cancer?
Runyuan Wang, Xiaoqin Zhang, Wei Wu, et al.
Cancer Medicine (2023) Vol. 12, Iss. 13, pp. 14037-14051
Open Access | Times Cited: 2
Development and Validation of a New Staging System for Esophageal Squamous Cell Carcinoma Patients Based on Combined Pathological TNM, Radiomics, and Proteomics
Shao-Jun Zheng, Chun-Peng Zheng, Tian‐Tian Zhai, et al.
Annals of Surgical Oncology (2022) Vol. 30, Iss. 4, pp. 2227-2241
Closed Access | Times Cited: 4
Establishment and validation of novel predictive models to predict bone metastasis in newly diagnosed prostate adenocarcinoma based on single-photon emission computed tomography radiomics.
Ning Wang, shihui qu, Weiwei Kong, et al.
Research Square (Research Square) (2024)
Open Access
A CT-based model integrating deep learning and radiomics for predicting T stage of gastric cancer: A multicenter study (Preprint)
Tao Jin, Dan Liu, Fubi Hu, et al.
(2024)
Closed Access
Multi-modal Radiomics Features to Predict Overall Survival of Locally Advanced Esophageal Cancer after Definitive Chemoradiotherapy
Nuo Yu, Yidong Wan, Lijing Zuo, et al.
Research Square (Research Square) (2024)
Open Access
Establishment and validation of novel predictive models to predict bone metastasis in newly diagnosed prostate adenocarcinoma based on single-photon emission computed tomography radiomics
Ning Wang, shihui qu, Weiwei Kong, et al.
Annals of Nuclear Medicine (2024) Vol. 38, Iss. 9, pp. 734-743
Closed Access
Development and validation of a CT-based model for noninvasive prediction of T stage in gastric cancer: A multicenter study (Preprint)
Tao Jin, Dan Liu, Fubi Hu, et al.
Journal of Medical Internet Research (2024) Vol. 26, pp. e56851-e56851
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
CT radiomics features of meso-esophageal fat in predicting overall survival of patients with locally advanced esophageal squamous cell carcinoma treated by definitive chemoradiotherapy
Shuo Yan, Feiping Li, Lian Jian, et al.
BMC Cancer (2023) Vol. 23, Iss. 1
Open Access
The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Oesophageal Squamous Carcinoma
Jian Xiang, Yuntao Tang, Jian Wu
Research Square (Research Square) (2023)
Open Access
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