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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:

Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers
Stefano Trebeschi, Silvia Girolama Drago, Nicolai J. Birkbak, et al.
Annals of Oncology (2019) Vol. 30, Iss. 6, pp. 998-1004
Open Access | Times Cited: 451

Showing 1-25 of 451 citing articles:

Neoadjuvant checkpoint blockade for cancer immunotherapy
Suzanne L. Topalian, Janis M. Taube, Drew M. Pardoll
Science (2020) Vol. 367, Iss. 6477
Open Access | Times Cited: 790
Predicting cancer outcomes with radiomics and artificial intelligence in radiology
Kaustav Bera, Nathaniel Braman, Amit Gupta, et al.
Nature Reviews Clinical Oncology (2021) Vol. 19, Iss. 2, pp. 132-146
Open Access | Times Cited: 464
Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non–Small Cell Lung Cancer
Mohammadhadi Khorrami, Prateek Prasanna, Amit Gupta, et al.
Cancer Immunology Research (2019) Vol. 8, Iss. 1, pp. 108-119
Open Access | Times Cited: 241
Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework
Abdalla Ibrahim, Sergey Primakov, Manon Beuque, et al.
Methods (2020) Vol. 188, pp. 20-29
Open Access | Times Cited: 191
Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer
R. Vanguri, Jia Luo, Andrew Aukerman, et al.
Nature Cancer (2022) Vol. 3, Iss. 10, pp. 1151-1164
Open Access | Times Cited: 190
Machine learning applications in prostate cancer magnetic resonance imaging
Renato Cuocolo, Maria Brunella Cipullo, Arnaldo Stanzione, et al.
European Radiology Experimental (2019) Vol. 3, Iss. 1
Open Access | Times Cited: 160
Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker
Bingxi He, Di Dong, Yunlang She, et al.
Journal for ImmunoTherapy of Cancer (2020) Vol. 8, Iss. 2, pp. e000550-e000550
Open Access | Times Cited: 155
Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review
Sujan Sarker, Lafifa Jamal, Syeda Faiza Ahmed, et al.
Robotics and Autonomous Systems (2021) Vol. 146, pp. 103902-103902
Open Access | Times Cited: 152
Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images
Wei Mu, Lei Jiang, Yu Shi, et al.
Journal for ImmunoTherapy of Cancer (2021) Vol. 9, Iss. 6, pp. e002118-e002118
Open Access | Times Cited: 148
Minimizing acquisition-related radiomics variability by image resampling and batch effect correction to allow for large-scale data analysis
Marta Ligero, Olivia Jordi-Ollero, Kinga Bernatowicz, et al.
European Radiology (2020) Vol. 31, Iss. 3, pp. 1460-1470
Open Access | Times Cited: 138
Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review
Arsela Prelaj, Vanja Mišković, Michele Zanitti, et al.
Annals of Oncology (2023) Vol. 35, Iss. 1, pp. 29-65
Open Access | Times Cited: 100
Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis
Yawei Li, Wu Xin, Ping Yang, et al.
Genomics Proteomics & Bioinformatics (2022) Vol. 20, Iss. 5, pp. 850-866
Open Access | Times Cited: 99
Targeting the tumor biophysical microenvironment to reduce resistance to immunotherapy
Tian Zhang, Yuanbo Jia, Yang Yu, et al.
Advanced Drug Delivery Reviews (2022) Vol. 186, pp. 114319-114319
Closed Access | Times Cited: 76
Radiomics and artificial intelligence for precision medicine in lung cancer treatment
Mitchell Chen, Susan J. Copley, Patrizia Viola, et al.
Seminars in Cancer Biology (2023) Vol. 93, pp. 97-113
Open Access | Times Cited: 74
An Overview of Artificial Intelligence in Oncology
Eduardo Moreno Júdice de Mattos Farina, Jacqueline Justino Nabhen, Maria Inez Dacoregio, et al.
Future Science OA (2022) Vol. 8, Iss. 4
Open Access | Times Cited: 73
Early Readout on Overall Survival of Patients With Melanoma Treated With Immunotherapy Using a Novel Imaging Analysis
Laurent Dercle, Binsheng Zhao, Mithat Gönen, et al.
JAMA Oncology (2022) Vol. 8, Iss. 3, pp. 385-385
Open Access | Times Cited: 72
Artificial intelligence in lung cancer: current applications and perspectives
Guillaume Chassagnon, Constance de Margerie‐Mellon, Maria Vakalopoulou, et al.
Japanese Journal of Radiology (2022)
Open Access | Times Cited: 70
Introduction to radiomics for a clinical audience
Cathal McCague, Syafiq Ramlee, Marika Reinius, et al.
Clinical Radiology (2023) Vol. 78, Iss. 2, pp. 83-98
Open Access | Times Cited: 63
Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours
Ashley M. Holder, Aikaterini Dedeilia, Kailan Sierra-Davidson, et al.
Nature reviews. Cancer (2024) Vol. 24, Iss. 7, pp. 498-512
Closed Access | Times Cited: 60
Advances in artificial intelligence to predict cancer immunotherapy efficacy
Jindong Xie, Xiyuan Luo, Xinpei Deng, et al.
Frontiers in Immunology (2023) Vol. 13
Open Access | Times Cited: 57
Nanoparticle-mediated cancer cell therapy: basic science to clinical applications
Jaya Verma, Caaisha Warsame, Rajkumar Kottayasamy Seenivasagam, et al.
Cancer and Metastasis Reviews (2023) Vol. 42, Iss. 3, pp. 601-627
Open Access | Times Cited: 57
Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study
Maliazurina Saad, Lingzhi Hong, Muhammad Aminu, et al.
The Lancet Digital Health (2023) Vol. 5, Iss. 7, pp. e404-e420
Open Access | Times Cited: 48
An overview and a roadmap for artificial intelligence in hematology and oncology
Wiebke Rösler, Michael Altenbuchinger, Bettina Baeßler, et al.
Journal of Cancer Research and Clinical Oncology (2023) Vol. 149, Iss. 10, pp. 7997-8006
Open Access | Times Cited: 47
Integration of artificial intelligence in lung cancer: Rise of the machine
Colton Ladbury, Arya Amini, Ameish Govindarajan, et al.
Cell Reports Medicine (2023) Vol. 4, Iss. 2, pp. 100933-100933
Open Access | Times Cited: 44
Radiomics signature for dynamic monitoring of tumor inflamed microenvironment and immunotherapy response prediction
Kinga Bernatowicz, Ramon Amat, Olivia Prior, et al.
Journal for ImmunoTherapy of Cancer (2025) Vol. 13, Iss. 1, pp. e009140-e009140
Open Access | Times Cited: 3
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