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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:

An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients
D.I. Wallis, Michaël Soussan, Maxime Lacroix, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2021) Vol. 49, Iss. 3, pp. 881-888
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine
Sudipta Roy, Tanushree Meena, Se‐Jung Lim
Diagnostics (2022) Vol. 12, Iss. 10, pp. 2549-2549
Open Access | Times Cited: 108
[18F]FDG-PET/CT Radiomics and Artificial Intelligence in Lung Cancer: Technical Aspects and Potential Clinical Applications
Reyhaneh Manafi‐Farid, Emran Askari, Isaac Shiri, et al.
Seminars in Nuclear Medicine (2022) Vol. 52, Iss. 6, pp. 759-780
Open Access | Times Cited: 54
PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer
Yifan Zhong, Chuang Cai, Tao Chen, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 24
Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects
C. Jiménez-Mesa, Juan E. Arco, Francisco J. Martínez-Murcia, et al.
Pharmacological Research (2023) Vol. 197, pp. 106984-106984
Open Access | Times Cited: 19
Clinical application of AI-based PET images in oncological patients
Jiaona Dai, Hui Wang, Yuchao Xu, et al.
Seminars in Cancer Biology (2023) Vol. 91, pp. 124-142
Closed Access | Times Cited: 18
Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space
Maryam Fallahpoor, Subrata Chakraborty, Biswajeet Pradhan, et al.
Computer Methods and Programs in Biomedicine (2023) Vol. 243, pp. 107880-107880
Closed Access | Times Cited: 18
The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges
Qiuyuan Hu, Ke Li, Conghui Yang, et al.
Frontiers in Oncology (2023) Vol. 13
Open Access | Times Cited: 16
Deep learning-based diagnosis of disease activity in patients with Graves’ orbitopathy using orbital SPECT/CT
Ni Yao, Longxi Li, Zhengyuan Gao, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2023) Vol. 50, Iss. 12, pp. 3666-3674
Open Access | Times Cited: 12
A PET/CT-based 3D deep learning model for predicting spread through air spaces in stage I lung adenocarcinoma
Cheng Zheng, Yujie Cai, Junjie Miao, et al.
Clinical & Translational Oncology (2025)
Closed Access
A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer
Serafeim‐Chrysovalantis Kotoulas, Dionysios Spyratos, Κonstantinos Porpodis, et al.
Cancers (2025) Vol. 17, Iss. 5, pp. 882-882
Open Access
Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations
Lente H. M. Dankelman, Sanne Schilstra, Frank F. A. IJpma, et al.
European Journal of Trauma and Emergency Surgery (2022) Vol. 49, Iss. 2, pp. 681-691
Open Access | Times Cited: 18
Prognostic value of combining clinical factors, 18F-FDG PET-based intensity, volumetric features, and deep learning predictor in patients with EGFR-mutated lung adenocarcinoma undergoing targeted therapies: a cross-scanner and temporal validation study
Kun‐Han Lue, Yu‐Hung Chen, Sung-Chao Chu, et al.
Annals of Nuclear Medicine (2024) Vol. 38, Iss. 8, pp. 647-658
Closed Access | Times Cited: 3
Multimodal deep learning model on interim [18F]FDG PET/CT for predicting primary treatment failure in diffuse large B-cell lymphoma
Yuan Cheng, Qing Shi, Xin‐Yun Huang, et al.
European Radiology (2022) Vol. 33, Iss. 1, pp. 77-88
Closed Access | Times Cited: 13
Non-invasive molecular imaging for precision diagnosis of metastatic lymph nodes: opportunities from preclinical to clinical applications
Zhongquan Cheng, Jiaojiao Ma, Lin Yin, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2022) Vol. 50, Iss. 4, pp. 1111-1133
Closed Access | Times Cited: 13
Value of dual-source CT dual-energy parameters combined with serum detection of VEGF and CEA in the diagnosis of early lung cancer
Liliang Ren, Yulong Yang
Biotechnology and Genetic Engineering Reviews (2023) Vol. 39, Iss. 2, pp. 1000-1011
Closed Access | Times Cited: 5
Multiparametric evaluation of mediastinal lymph node metastases in clinical T0–T1c stage non-small-cell lung cancers
Siyang Wang, Xiao Bao, Feixing Yang, et al.
European Journal of Cardio-Thoracic Surgery (2024) Vol. 65, Iss. 3
Closed Access | Times Cited: 1
Endobronchial Ultrasound-Based Support Vector Machine Model for Differentiating between Benign and Malignant Mediastinal and Hilar Lymph Nodes
Wenjia Hu, Feifei Wen, Mengyu Zhao, et al.
Respiration (2024) Vol. 103, Iss. 11, pp. 675-685
Closed Access | Times Cited: 1
Artificial Intelligence in Oncological Hybrid Imaging
Benedikt Feuerecker, Maurice M. Heimer, Thomas Geyer, et al.
RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren (2022) Vol. 195, Iss. 02, pp. 105-114
Open Access | Times Cited: 6
A comparison of 18F-FDG PET-based radiomics and deep learning in predicting regional lymph node metastasis in patients with resectable lung adenocarcinoma: a cross-scanner and temporal validation study
Kun‐Han Lue, Yu‐Hung Chen, Sung‐Chao Chu, et al.
Nuclear Medicine Communications (2023) Vol. 44, Iss. 12, pp. 1094-1105
Closed Access | Times Cited: 3
Convolutional neural network-based program to predict lymph node metastasis of non-small cell lung cancer using 18F-FDG PET
Eitaro Kidera, Sho Koyasu, Kenji Hirata, et al.
Annals of Nuclear Medicine (2023) Vol. 38, Iss. 1, pp. 71-80
Open Access | Times Cited: 2
A Deep Multi-Task Network to Learn Tumor Pathological Representations for Lymph Node Metastasis Prediction
Danqing Hu, Bing Liu, Lechao Cheng, et al.
Studies in health technology and informatics (2024)
Open Access
Breast Cancer Subtype Prediction Model Employing Artificial Neural Network and 18F-Fluorodeoxyglucose Positron Emission Tomography/ Computed Tomography
Alamgir Hossain, Shariful Islam Chowdhury
Journal of Medical Physics (2024) Vol. 49, Iss. 2, pp. 181-188
Open Access
The value of 18F-fluorodeoxyglucose positron emission tomography-based radiomics in non-small cell lung cancer
Yu‐Hung Chen, Kun‐Han Lue, Sung-Chao Chu, et al.
Tzu Chi Medical Journal (2024) Vol. 37, Iss. 1, pp. 17-27
Open Access
A 3 M Evaluation Protocol for Examining Lymph Nodes in Cancer Patients: Multi-Modal, Multi-Omics, Multi-Stage Approach
R F Wang, Zhiyan Zhang, Mengyun Zhao, et al.
Technology in Cancer Research & Treatment (2024) Vol. 23
Open Access
Künstliche Intelligenz in der onkologischen Hybridbildgebung
Benedikt Feuerecker, Maurice M. Heimer, Thomas Geyer, et al.
Angewandte Nuklearmedizin (2024) Vol. 47, Iss. 04, pp. 246-256
Closed Access
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