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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 1-25 of 27 citing articles:

CT-based radiomics nomogram may predict local recurrence-free survival in esophageal cancer patients receiving definitive chemoradiation or radiotherapy: A multicenter study
Jie Gong, Wencheng Zhang, Wei Huang, et al.
Radiotherapy and Oncology (2022) Vol. 174, pp. 8-15
Closed Access | Times Cited: 33
Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures
Yongbin Cui, Zhengjiang Li, Mingyue Xiang, et al.
Radiation Oncology (2022) Vol. 17, Iss. 1
Open Access | Times Cited: 30
Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features
Fu-Hai Wang, Hua‐Long Zheng, Jintao Li, et al.
La radiologia medica (2022) Vol. 127, Iss. 10, pp. 1085-1097
Closed Access | Times Cited: 26
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
Multi-Centered Pre-Treatment CT-Based Radiomics Features to Predict Locoregional Recurrence of Locally Advanced Esophageal Cancer After Definitive Chemoradiotherapy
Nuo Yu, Xiaolin Ge, Lijing Zuo, et al.
Cancers (2025) Vol. 17, Iss. 1, pp. 126-126
Open Access
Radiomics from dual-energy CT-derived iodine maps for predicting lymph node metastases in patients with resectable rectal cancer
Xia Liu, Yi Yuan, Xiaoli Chen, et al.
Journal of X-Ray Science and Technology (2025)
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.
BMC Cancer (2025) Vol. 25, Iss. 1
Open Access
Application of peritumoral radiomics based on simulated positioning CT images in the prognosis of intermediate-advanced esophageal cancer
Ruiling Yang, Zhihui Shi, Jinqiu Ruan, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images
Simon Keek, Frederik Wesseling, Henry C. Woodruff, et al.
Cancers (2021) Vol. 13, Iss. 13, pp. 3271-3271
Open Access | Times Cited: 24
CT-based radiomics combined with hematologic parameters for survival prediction in locally advanced esophageal cancer patients receiving definitive chemoradiotherapy
Jinfeng Cui, Dexian Zhang, Yongsheng Gao, et al.
Insights into Imaging (2024) Vol. 15, 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
Quantitative parameters derived from dual-energy computed tomography for the preoperative prediction of early recurrence in patients with esophageal squamous cell carcinoma
Yan Liu, Fanrong Cheng, Lei Wang, et al.
European Radiology (2023) Vol. 33, Iss. 11, pp. 7419-7428
Closed Access | Times Cited: 7
Preoperative CT radiomics of esophageal squamous cell carcinoma and lymph node to predict nodal disease with a high diagnostic capability
Yuping Wu, Lan Wu, Jing Ou, et al.
European Journal of Radiology (2023) Vol. 170, pp. 111197-111197
Open Access | Times Cited: 6
Contrast-enhanced CT radiomics features to preoperatively identify differences between tumor and proximal tumor-adjacent and tumor-distant tissues of resectable esophageal squamous cell carcinoma
Dan Gao, Bang-guo Tan, Xiaoqian Chen, et al.
Cancer Imaging (2024) Vol. 24, Iss. 1
Open Access | Times Cited: 1
Potential value of CT-based comprehensive nomogram in predicting occult lymph node metastasis of esophageal squamous cell paralaryngeal nerves: a two-center study
Ting Xue, Xinyi Wan, Taohu Zhou, et al.
Journal of Translational Medicine (2024) Vol. 22, Iss. 1
Open Access | Times Cited: 1
Computed Tomography-Based Radiomics Nomogram for Predicting the Postoperative Prognosis of Esophageal Squamous Cell Carcinoma: A Multicenter Study
Hui Peng, Ting Xue, Qiaoling Chen, et al.
Academic Radiology (2022) Vol. 29, Iss. 11, pp. 1631-1640
Closed Access | Times Cited: 7
5-year recurrence prediction after hepatocellular carcinoma resection: deep learning vs. Cox regression models.
Hon‐Yi Shi, King-The Lee, Chong‐Chi Chiu, et al.
PubMed (2022) Vol. 12, Iss. 6, pp. 2876-2890
Closed 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
GTF2E2 is a novel biomarker for recurrence after surgery and promotes progression of esophageal squamous cell carcinoma via miR-139-5p/GTF2E2/FUS axis
Yujie Zhang, Yuxin Zhang, Bo Ai, et al.
Oncogene (2021) Vol. 41, Iss. 6, pp. 782-796
Open Access | Times Cited: 6
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
Development of a prognostic nomogram for esophageal squamous cell carcinoma patients received radiotherapy based on clinical risk factors
Yang Li, Xian Shao, Li-Juan Dai, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access
Preoperative identification of small metastatic lymph nodes in esophageal squamous cell carcinoma using CT radiomics of lymph nodes
Yuping Wu, Lan Wu, Jing Ou, et al.
Abdominal Radiology (2024)
Closed Access
Multi-centered Pre-treatment CT Based Radiomics Features to Predict Locoregional Recurrence of Locally Advanced Esophageal Cancer After Definitive Chemoradiotherapy
Nuo Yu, Peipei Wang, Lijing Zuo, et al.
Research Square (Research Square) (2024)
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
Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures
Yongbin Cui, Zhengjiang Li, Mingyue Xiang, et al.
Research Square (Research Square) (2022)
Open Access | Times Cited: 1
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