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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:

Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study
Casper Reijnen, Evangelia Gogou, Nicole C.M. Visser, et al.
PLoS Medicine (2020) Vol. 17, Iss. 5, pp. e1003111-e1003111
Open Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

Multimodal machine learning in precision health: A scoping review
Adrienne Kline, Hanyin Wang, Yikuan Li, et al.
npj Digital Medicine (2022) Vol. 5, Iss. 1
Open Access | Times Cited: 223
Exploring data mining and machine learning in gynecologic oncology
Ferdaous Idlahcen, Ali Idri, Evgin Göçeri
Artificial Intelligence Review (2024) Vol. 57, Iss. 2
Open Access | Times Cited: 22
Risk Stratification of Endometrial Cancer Patients: FIGO Stage, Biomarkers and Molecular Classification
Jenneke C. Kasius, Johanna M.A. Pijnenborg, Kristina Lindemann, et al.
Cancers (2021) Vol. 13, Iss. 22, pp. 5848-5848
Open Access | Times Cited: 65
Current and Emerging Prognostic Biomarkers in Endometrial Cancer
Kelechi Njoku, Chloe E. Barr, Emma J. Crosbie
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 58
The role of L1CAM as predictor of poor prognosis in stage I endometrial cancer: a systematic review and meta-analysis
Andrea Giannini, Ottavia D’Oria, Giacomo Corrado, et al.
Archives of Gynecology and Obstetrics (2023) Vol. 309, Iss. 3, pp. 789-799
Closed Access | Times Cited: 26
Machine Learning for Endometrial Cancer Prediction and Prognostication
Vipul Bhardwaj, Arundhiti Sharma, V. P. Snijesh, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 35
Artificial intelligence-based risk stratification, accurate diagnosis and treatment prediction in gynecologic oncology
Yuting Jiang, Chengdi Wang, Shengtao Zhou
Seminars in Cancer Biology (2023) Vol. 96, pp. 82-99
Open Access | Times Cited: 19
Gynecological cancer prognosis using machine learning techniques: A systematic review of the last three decades (1990–2022)
Joshua Sheehy, Hamish Rutledge, U. Rajendra Acharya, et al.
Artificial Intelligence in Medicine (2023) Vol. 139, pp. 102536-102536
Closed Access | Times Cited: 18
XAI-based Clinical Decision Support System: A Systematic Review
SeYoung Kim, Daeho Kim, Minji Kim, et al.
(2024)
Open Access | Times Cited: 7
Nomograms of Combining Apparent Diffusion Coefficient Value and Radiomics for Preoperative Risk Evaluation in Endometrial Carcinoma
Kaiyue Zhang, Yu Zhang, Xin Fang, et al.
Frontiers in Oncology (2021) Vol. 11
Open Access | Times Cited: 28
A deep learning model for lymph node metastasis prediction based on digital histopathological images of primary endometrial cancer
Min Feng, Yu Zhao, Jie Chen, et al.
Quantitative Imaging in Medicine and Surgery (2023) Vol. 13, Iss. 3, pp. 1899-1913
Open Access | Times Cited: 11
Molecular classification improves preoperative risk assessment of endometrial cancer
Silvia Cabrera, Vicente Bebia, Carlos López-Gil, et al.
Gynecologic Oncology (2024) Vol. 189, pp. 56-63
Closed Access | Times Cited: 4
XAI-Based Clinical Decision Support Systems: A Systematic Review
Se Young Kim, Dae Ho Kim, Min‐Ji Kim, et al.
Applied Sciences (2024) Vol. 14, Iss. 15, pp. 6638-6638
Open Access | Times Cited: 4
Immunohistochemical biomarkers are prognostic relevant in addition to the ESMO-ESGO-ESTRO risk classification in endometrial cancer
Stephanie Vrede, Willem Jan van Weelden, Nicole C.M. Visser, et al.
Gynecologic Oncology (2021) Vol. 161, Iss. 3, pp. 787-794
Open Access | Times Cited: 26
Improving preoperative binary grading: relevance of p53 and PR expression in grade 2 endometrioid endometrial carcinoma
Puk Meijs-Hermanns, Henrica M.J. Werner, Loes Kooreman, et al.
International Journal of Gynecological Cancer (2025), pp. 101682-101682
Open Access
Inferring the Langevin equation with uncertainty via Bayesian neural networks
Youngkyoung Bae, Seungwoong Ha, Hawoong Jeong
Chaos Solitons & Fractals (2025) Vol. 197, pp. 116440-116440
Closed Access
Hormonal biomarkers remain prognostically relevant within the molecular subgroups in endometrial cancer
Stephanie Vrede, Willem Jan van Weelden, Johan Bulten, et al.
Gynecologic Oncology (2024) Vol. 192, pp. 15-23
Open Access | Times Cited: 3
Endometrial Cancer Molecular Characterization: The Key to Identifying High-Risk Patients and Defining Guidelines for Clinical Decision-Making?
Regina Baiden-Amissah, Daniela Annibali, Sandra Tuyaerts, et al.
Cancers (2021) Vol. 13, Iss. 16, pp. 3988-3988
Open Access | Times Cited: 19
Abnormal preoperative haematological parameters in Endometrial cancer; reflecting tumour aggressiveness or reduced response to radiotherapy?
Stephanie Vrede, Hannah Donkers, Casper Reijnen, et al.
Journal of Obstetrics and Gynaecology (2024) Vol. 44, Iss. 1
Open Access | Times Cited: 2
Preoperative CA125 Significantly Improves Risk Stratification in High-Grade Endometrial Cancer
Marike Lombaers, Karlijn Cornel, Nicole C.M. Visser, et al.
Cancers (2023) Vol. 15, Iss. 9, pp. 2605-2605
Open Access | Times Cited: 6
Machine learning applications in gynecological cancer: A critical review
Oraianthi Fiste, Michalis Liontos, Flora Zagouri, et al.
Critical Reviews in Oncology/Hematology (2022) Vol. 179, pp. 103808-103808
Closed Access | Times Cited: 9
Evaluating the use of machine learning in endometrial cancer: a systematic review
Sabrina Piedimonte, Gabriella Rosa, Brigitte Gerstl, et al.
International Journal of Gynecological Cancer (2023) Vol. 33, Iss. 9, pp. 1383-1393
Closed Access | Times Cited: 4
The Clinical Significance of DJ1 and L1CAM Serum Level Monitoring in Patients with Endometrial Cancer
Markéta Bednaříková, Petra Vinklerová, Jana Gottwaldová, et al.
Journal of Clinical Medicine (2021) Vol. 10, Iss. 12, pp. 2640-2640
Open Access | Times Cited: 10
Preoperative pelvic MRI and 2-[18F]FDG PET/CT for lymph node staging and prognostication in endometrial cancer—time to revisit current imaging guidelines?
Kristine E. Fasmer, Ankush Gulati, Julie A. Dybvik, et al.
European Radiology (2022) Vol. 33, Iss. 1, pp. 221-232
Open Access | Times Cited: 7
Improved preoperative risk stratification in endometrial carcinoma patients: external validation of the ENDORISK Bayesian network model in a large population-based case series
Marcel Grube, Casper Reijnen, Peter Lucas, et al.
Journal of Cancer Research and Clinical Oncology (2022) Vol. 149, Iss. 7, pp. 3361-3369
Open Access | Times Cited: 7
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