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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:

Development and validation of MRI‐based deep learning models for prediction of microsatellite instability in rectal cancer
Wei Zhang, Hongkun Yin, Zixing Huang, et al.
Cancer Medicine (2021) Vol. 10, Iss. 12, pp. 4164-4173
Open Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions
Mohamed Khalifa, Mona Albadawy
Computer Methods and Programs in Biomedicine Update (2024) Vol. 5, pp. 100148-100148
Open Access | Times Cited: 33
Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy
Laurent Dercle, Jeremy McGale, Shawn Sun, et al.
Journal for ImmunoTherapy of Cancer (2022) Vol. 10, Iss. 9, pp. e005292-e005292
Open Access | Times Cited: 54
Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer
Narmin Ghaffari Laleh, Marta Ligero, Raquel Pérez-López, et al.
Clinical Cancer Research (2022) Vol. 29, Iss. 2, pp. 316-323
Open Access | Times Cited: 43
Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review
Jiaqing Yang, Jing Huang, Deqian Han, et al.
Clinical Medicine Insights Oncology (2024) Vol. 18
Open Access | Times Cited: 9
Performance reporting design in artificial intelligence studies using image-based TNM staging and prognostic parameters in rectal cancer: a systematic review
Minsung Kim, Taeyong Park, Bo Young Oh, et al.
Annals of Coloproctology (2024) Vol. 40, Iss. 1, pp. 13-26
Open Access | Times Cited: 6
Radiology and multi-scale data integration for precision oncology
Hania Paverd, Konstantinos Zormpas‐Petridis, Hannah Clayton, et al.
npj Precision Oncology (2024) Vol. 8, Iss. 1
Open Access | Times Cited: 5
Deep Learning in Oncology: Transforming Cancer Diagnosis, Prognosis, and Treatment
Thaís Santos Anjo Reis
Emerging Trends in Drugs Addictions and Health (2025), pp. 100171-100171
Open Access
Artificial intelligence applied to the diagnosis of colorectal cancer
Ana Jiménez-Pastor, Carmen Prieto-de-la-Lastra
Elsevier eBooks (2025), pp. 249-260
Closed Access
Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review
A. Damiano, Tia Cheunkarndee, Chinenye C. Azoba, et al.
Cancer Medicine (2025) Vol. 14, Iss. 5
Open Access
Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
Lakpa Dorje Tamang, Byung Wook Kim
Applied Sciences (2021) Vol. 11, Iss. 22, pp. 10982-10982
Open Access | Times Cited: 38
The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer
Sebastian Curcean, Andra Curcean, Daniela Martin, et al.
Cancers (2024) Vol. 16, Iss. 17, pp. 3111-3111
Open Access | Times Cited: 4
A pelvis MR transformer-based deep learning model for predicting lung metastases risk in patients with rectal cancer
Li Yin, Shuang Li, Ruhui Xiao, et al.
Frontiers in Oncology (2025) Vol. 15
Open Access
An Overview of Predictive Biomarkers and Detection Approaches for Immunotherapy Response in GI Malignancies
Xinyu Chai, Yiwen Zhang, Zhihui Shi, et al.
Journal of Gastroenterology and Hepatology (2025)
Closed Access
Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment 18F-FDG PET/CT Using Deep Learning
Hongyue Zhao, Yexin Su, Zhehao Lyu, et al.
Academic Radiology (2023) Vol. 31, Iss. 1, pp. 35-45
Closed Access | Times Cited: 11
Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review
Giuseppe Costanzo, Raffaele Ascione, Andrea Ponsiglione, et al.
Exploration of Targeted Anti-tumor Therapy (2023), pp. 406-421
Open Access | Times Cited: 10
Improving prediction of treatment response and prognosis in colorectal cancer with AI-based medical image analysis
Xiangyu Liu, Song Zhang, Lizhi Shao, et al.
The Innovation Medicine (2024) Vol. 2, Iss. 2, pp. 100069-100069
Open Access | Times Cited: 3
Development and external validation of a multiparametric MRI-based radiomics model for preoperative prediction of microsatellite instability status in rectal cancer: a retrospective multicenter study
Zhi Li, Jing Zhang, Qi Zhong, et al.
European Radiology (2022) Vol. 33, Iss. 3, pp. 1835-1843
Closed Access | Times Cited: 18
Microsatellite instability in rectal cancer: what does it mean? A study of two randomized trials and a systematic review of the literature
Marloes Swets, Cristina Graham Martínez, Shannon van Vliet, et al.
Histopathology (2022) Vol. 81, Iss. 3, pp. 352-362
Open Access | Times Cited: 17
MRI‐Based Artificial Intelligence in Rectal Cancer
Chinting Wong, Yu Fu, Mingyang Li, et al.
Journal of Magnetic Resonance Imaging (2022) Vol. 57, Iss. 1, pp. 45-56
Closed Access | Times Cited: 17
Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview
Pak Kin Wong, In Neng Chan, H. J. Yan, et al.
World Journal of Gastroenterology (2022) Vol. 28, Iss. 45, pp. 6363-6379
Open Access | Times Cited: 16
CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study
Wuteng Cao, Huabin Hu, Jirui Guo, et al.
Journal of Translational Medicine (2023) Vol. 21, Iss. 1
Open Access | Times Cited: 9
A novel radiomics based on multi-parametric magnetic resonance imaging for predicting Ki-67 expression in rectal cancer: a multicenter study
Xiuzhen Yao, Weiqun Ao, Xiandi Zhu, et al.
BMC Medical Imaging (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 9
A deep learning-based approach toward differentiating scalp psoriasis and seborrheic dermatitis from dermoscopic images
Yu Zhang, Kaizhi Shen, Han Jianwen, et al.
Frontiers in Medicine (2022) Vol. 9
Open Access | Times Cited: 15
A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma
Hang Yuan, Peng Yu, Xiren Xu, et al.
Cancer Management and Research (2022) Vol. Volume 14, pp. 2409-2418
Open Access | Times Cited: 12
Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians
Huijie Zhou, Qian Luo, Wanchun Wu, et al.
Frontiers in Immunology (2023) Vol. 14
Open Access | Times Cited: 7
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