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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:

Artificial intelligence-based radiomics models in endometrial cancer: A systematic review
Lise Lecointre, Jérémy Dana, Massimo Lodi, et al.
European Journal of Surgical Oncology (2021) Vol. 47, Iss. 11, pp. 2734-2741
Open Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative
Gaia Spadarella, Arnaldo Stanzione, Tugba Akinci D’Antonoli, et al.
European Radiology (2022) Vol. 33, Iss. 3, pp. 1884-1894
Open Access | Times Cited: 84
Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease
Jérémy Dana, Aïna Venkatasamy, Antonio Saviano, et al.
Hepatology International (2022) Vol. 16, Iss. 3, pp. 509-522
Open Access | Times Cited: 34
Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review
F. Moro, Marianna Ciancia, Drieda Zaçe, et al.
International Journal of Cancer (2024) Vol. 155, Iss. 10, pp. 1832-1845
Open Access | Times Cited: 8
Impact of artificial intelligence on the diagnosis, treatment and prognosis of endometrial cancer
Samia Rauf Butt, Amna Soulat, Priyanka Mohan Lal, et al.
Annals of Medicine and Surgery (2024) Vol. 86, Iss. 3, pp. 1531-1539
Open Access | Times Cited: 7
Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review
Alfonso Reginelli, Valerio Nardone, Giuliana Giacobbe, et al.
Diagnostics (2021) Vol. 11, Iss. 10, pp. 1796-1796
Open Access | Times Cited: 41
Systematic reviews of machine learning in healthcare: a literature review
Katarzyna Kolasa, Bisrat Yeshewas Admassu, Malwina Hołownia-Voloskova, et al.
Expert Review of Pharmacoeconomics & Outcomes Research (2023) Vol. 24, Iss. 1, pp. 63-115
Open Access | Times Cited: 16
Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis
Meng-Lin Huang, Jing Ren, Zhengyu Jin, et al.
La radiologia medica (2024) Vol. 129, Iss. 3, pp. 439-456
Closed Access | Times Cited: 6
A Radiomic-based model to predict the depth of myometrial invasion in endometrial cancer on ultrasound images
Francesca Arezzo, Annarita Fanizzi, Rosanna Mancari, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Accuracy of radiomics in the diagnosis and preoperative high-risk assessment of endometrial cancer: a systematic review and meta-analysis
Junmei He, Yurong Liu, Jinzhu Li, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access | Times Cited: 3
Advances in Radiological Techniques for Cancer Diagnosis: A Narrative Review of Current Technologies
Zuhair Ali, Anas Hamdoun, Abdelmoneim Alattaya
Deleted Journal (2024) Vol. 2, Iss. 1, pp. 43-56
Open Access | Times Cited: 2
Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status
Xiaoyang Liu, Mohamed Elbanan, Antonio Luna, et al.
American Journal of Roentgenology (2022) Vol. 219, Iss. 6, pp. 985-995
Open Access | Times Cited: 11
Preoperative Tumor Texture Analysis on MRI for High-Risk Disease Prediction in Endometrial Cancer: A Hypothesis-Generating Study
Maura Miccò, Benedetta Gui, Luca Russo, et al.
Journal of Personalized Medicine (2022) Vol. 12, Iss. 11, pp. 1854-1854
Open Access | Times Cited: 11
A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study
Camelia Alexandra Coadă, Miriam Santoro, Vladislav Zybin, et al.
Cancers (2023) Vol. 15, Iss. 18, pp. 4534-4534
Open Access | Times Cited: 6
Both intra- and peri-tumoral radiomics signatures can be used to predict lymphatic vascular space invasion and lymphatic metastasis positive status from endometrial cancer MR imaging
Shengyong Li, Yida Wang, Yiyang Sun, et al.
Abdominal Radiology (2024) Vol. 49, Iss. 11, pp. 4140-4150
Closed Access | Times Cited: 2
Feasibility and clinical applicability of genomic profiling based on cervical smear samples in patients with endometrial cancer
Namsoo Kim, Yoo‐Na Kim, Kyunglim Lee, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 9
Prediction of Endometrial Carcinoma Using the Combination of Electronic Health Records and an Ensemble Machine Learning Method
Wenwen Wang, Yang Xu, Suzhen Yuan, et al.
Frontiers in Medicine (2022) Vol. 9
Open Access | Times Cited: 7
Radiomics and Artificial Intelligence in Uterine Sarcomas: A Systematic Review
Gloria Ravegnini, Martina Ferioli, A.G. Morganti, et al.
Journal of Personalized Medicine (2021) Vol. 11, Iss. 11, pp. 1179-1179
Open Access | Times Cited: 9
Systematic Reviews of Machine Learning in Healthcare: A Literature Review
Katarzyna Kolasa, Bisrat Yeshewas Admassu, Malwina Hołownia-Voloskova, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 2
Radiomics quality score in renal masses: a systematic assessment on current literature
Afshin Azadikhah, Bino Varghese, Xiaomeng Lei, et al.
British Journal of Radiology (2022) Vol. 95, Iss. 1137
Open Access | Times Cited: 3
Feasible does not mean useful: Do we always need radiomics?
Arnaldo Stanzione
European Journal of Radiology (2022) Vol. 156, pp. 110545-110545
Closed Access | Times Cited: 3
Multiparametric MRI radiomics for predicting disease-free survival and high-risk histopathological features for tumor recurrence in endometrial cancer
Mary Renton, Mina Fakhriyehasl, Jessica Weiss, et al.
Frontiers in Oncology (2024) Vol. 14
Open Access
Artificial intelligence-enhanced MRI-based preoperative staging in patients with endometrial cancer
Lise Lecointre, Julia Alekseenko, Matteo Pavone, et al.
International Journal of Gynecological Cancer (2024) Vol. 35, Iss. 1, pp. 100017-100017
Closed Access
Artificial Intelligence: A Primer for the Radiologists
Harsimran Bhatia, Anmol Bhatia, Chirag Ahuja, et al.
Indographics (2022) Vol. 01, Iss. 02, pp. 215-221
Open Access | Times Cited: 1
Diagnosis subtype of endometrial carcinoma using a deep learning model based on histopathological images
Lisha Qi, Lingmei Li, Yijun Guo, et al.
Research Square (Research Square) (2023)
Open Access
A Deep Learning Model Based on Histological Images for Classifying Endometrioid Carcinoma and Non-Endometrioid Carcinoma
Lingmei Li, Yijun Guo, Rui Yin, et al.
(2023)
Closed Access
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