Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

An artificial intelligence method to assess the tumor microenvironment with treatment outcomes for gastric cancer patients after gastrectomy
Tao Chen, Xunjun Li, Qingyi Mao, et al.
Journal of Translational Medicine (2022) Vol. 20, Iss. 1
Open Access | Times Cited: 12

Showing 12 citing articles:

Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology
Zhe Wang, Yang Liu, Xing Niu
Seminars in Cancer Biology (2023) Vol. 93, pp. 83-96
Closed Access | Times Cited: 42
Advancing precision medicine: the transformative role of artificial intelligence in immunogenomics, radiomics, and pathomics for biomarker discovery and immunotherapy optimization
Luchen Chang, Jiamei Liu, Jialin Zhu, et al.
Cancer Biology and Medicine (2025), pp. 1-15
Open Access | Times Cited: 1
See Lung Cancer with an AI
Joanna Bidzińska, Edyta Szurowska
Cancers (2023) Vol. 15, Iss. 4, pp. 1321-1321
Open Access | Times Cited: 10
APDF: An active preference-based deep forest expert system for overall survival prediction in gastric cancer
Qiucen Li, Yuheng Wang, Zedong Du, et al.
Expert Systems with Applications (2024) Vol. 245, pp. 123131-123131
Closed Access | Times Cited: 2
Computed tomography (CT) derived radiomics to predict post-operative disease recurrence in gastric cancer; a systematic review
Niall J. O’Sullivan, Hugo C. Temperley, M. Horan, et al.
Current Problems in Diagnostic Radiology (2024) Vol. 53, Iss. 6, pp. 717-722
Closed Access | Times Cited: 2
Contrast-Enhanced Computed Tomography–Based Radiogenomics Analysis for Predicting Prognosis in Gastric Cancer
Han Liu, Yiyun Wang, Yingqiao Liu, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 6
Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic Review
Marianne Linley L. Sy‐Janairo, Jose Isagani B. Janairo
Journal of Gastrointestinal Cancer (2023) Vol. 55, Iss. 1, pp. 47-64
Closed Access | Times Cited: 2
Small Tweaks, Major Changes: Post-Translational Modifications That Occur within M2 Macrophages in the Tumor Microenvironment
Shutao Zheng, Yan Liang, Yiyi Tan, et al.
Cancers (2022) Vol. 14, Iss. 22, pp. 5532-5532
Open Access | Times Cited: 3
Application Prospect of Artificial Intelligence in the Field of Surgery: Visual Analysis
婧 徐
Advances in Clinical Medicine (2024) Vol. 14, Iss. 02, pp. 4151-4163
Closed Access
Computed tomography radiomics reveals prognostic value of immunophenotyping in laryngeal squamous cell carcinoma: a comparison of whole tumor- versus habitats-based approaches
Meng Qi, Weiding Zhou, Ying Yuan, et al.
BMC Medical Imaging (2024) Vol. 24, Iss. 1
Open Access
Radiomic-based models are able to predict the pathologic response to different neoadjuvant chemotherapy regimens in patients with gastric and gastroesophageal cancer: a cohort study
Agnes Annamaria, Luca Boldrini, Federica Perillo, et al.
Research Square (Research Square) (2024)
Open Access
Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis
Pengfei Lyu, Yu Wang, Qingxiang Meng, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 1
Page 1

Scroll to top