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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:

Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.
Paul Blanc‐Durand, Simon Jégou, Salim Kanoun, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2020) Vol. 48, Iss. 5, pp. 1362-1370
Closed Access | Times Cited: 104

Showing 1-25 of 104 citing articles:

A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions
Sergios Gatidis, Tobias Hepp, Marcel Früh, et al.
Scientific Data (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 113
TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images — a multi-center generalizability analysis
Fereshteh Yousefirizi, Ivan S. Klyuzhin, Joo Hyun O, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2024) Vol. 51, Iss. 7, pp. 1937-1954
Closed Access | Times Cited: 21
Deep learning based joint segmentation and characterization of multi-class retinal fluid lesions on OCT scans for clinical use in anti-VEGF therapy
Bilal Hassan, Shiyin Qin, Ramsha Ahmed, et al.
Computers in Biology and Medicine (2021) Vol. 136, pp. 104727-104727
Closed Access | Times Cited: 66
PET/CT in Non-Hodgkin Lymphoma: An Update
Lucia Zanoni, Davide Bezzi, Cristina Nanni, et al.
Seminars in Nuclear Medicine (2022) Vol. 53, Iss. 3, pp. 320-351
Closed Access | Times Cited: 42
Lymphoma segmentation from 3D PET-CT images using a deep evidential network
Ling Huang, Su Ruan, Pierre Decazes, et al.
International Journal of Approximate Reasoning (2022) Vol. 149, pp. 39-60
Open Access | Times Cited: 40
18F-FDG PET Maximum-Intensity Projections and Artificial Intelligence: A Win-Win Combination to Easily Measure Prognostic Biomarkers in DLBCL Patients
Kibrom Berihu Girum, Louis Rebaud, Anne‐Ségolène Cottereau, et al.
Journal of Nuclear Medicine (2022) Vol. 63, Iss. 12, pp. 1925-1932
Open Access | Times Cited: 38
Total metabolic tumor volume on18F-FDG PET/CT is a game-changer for patients with metastatic lung cancer treated with immunotherapy
Pierre Tricarico, David Chardin, Nicolas Martin, et al.
Journal for ImmunoTherapy of Cancer (2024) Vol. 12, Iss. 4, pp. e007628-e007628
Open Access | Times Cited: 11
The Evolution of Artificial Intelligence in Nuclear Medicine
Leonor Lopes, Alejandro López-Montes, Yizhou Chen, et al.
Seminars in Nuclear Medicine (2025)
Open Access | Times Cited: 1
The Role of AI in Lymphoma: An Update
James Cairns, Russell Frood, Chirag Patel, et al.
Seminars in Nuclear Medicine (2025)
Open Access | Times Cited: 1
Applications of artificial intelligence in nuclear medicine image generation
Zhibiao Cheng, Junhai Wen, Gang Huang, et al.
Quantitative Imaging in Medicine and Surgery (2021) Vol. 11, Iss. 6, pp. 2792-2822
Open Access | Times Cited: 45
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
Dapagliflozin Attenuates HFpEF Remodeling and Dysfunction by Elevating β-Hydroxybutyrate-activated Citrate Synthase
Xinxin Zhang, Ning Wang, Peng Fu, et al.
Journal of Cardiovascular Pharmacology (2023) Vol. 82, Iss. 5, pp. 375-388
Open Access | Times Cited: 18
An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients
Maria C. Ferrández, Sandeep S.V. Golla, Jakoba J. Eertink, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 17
What clinicians should know about surrogate end points in hematologic malignancies
C. Bommier, Matthew J. Maurer, Jérôme Lambert
Blood (2024) Vol. 144, Iss. 1, pp. 11-20
Closed Access | Times Cited: 6
Whole-body uptake classification and prostate cancer staging in 68Ga-PSMA-11 PET/CT using dual-tracer learning
Nicolò Capobianco, Ludovic Sibille, Maythinee Chantadisai, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2021) Vol. 49, Iss. 2, pp. 517-526
Open Access | Times Cited: 37
Quantitative PET-based biomarkers in lymphoma: getting ready for primetime
Juan Pablo Alderuccio, Russ Kuker, Fei Yang, et al.
Nature Reviews Clinical Oncology (2023) Vol. 20, Iss. 9, pp. 640-657
Closed Access | Times Cited: 16
Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma
Eren M. Veziroglu, Faraz Farhadi, Navid Hasani, et al.
Seminars in Nuclear Medicine (2023) Vol. 53, Iss. 3, pp. 426-448
Closed Access | Times Cited: 15
Application of machine learning in the management of lymphoma: Current practice and future prospects
Junyun Yuan, Ya Zhang, Xin Wang
Digital Health (2024) Vol. 10
Open Access | Times Cited: 5
Multicenter PET image harmonization using generative adversarial networks
David Haberl, Clemens P. Spielvogel, Zewen Jiang, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2024) Vol. 51, Iss. 9, pp. 2532-2546
Open Access | Times Cited: 5
FDG-PET/CT in Lymphoma: Where Do We Go Now?
Yassine Al Tabaa, Clément Bailly, Salim Kanoun
Cancers (2021) Vol. 13, Iss. 20, pp. 5222-5222
Open Access | Times Cited: 29
Artificial Intelligence in Lymphoma PET Imaging
Navid Hasani, Sriram S. Paravastu, Faraz Farhadi, et al.
PET Clinics (2021) Vol. 17, Iss. 1, pp. 145-174
Open Access | Times Cited: 28
A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [18F]FDG PET/CT
Xiaohui Zhang, Lin Chen, Han Jiang, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2021) Vol. 49, Iss. 4, pp. 1298-1310
Open Access | Times Cited: 27
Deep Neural Networks and Machine Learning Radiomics Modelling for Prediction of Relapse in Mantle Cell Lymphoma
Catharina Silvia Lisson, Christoph Gerhard Lisson, Marc Fabian Mezger, et al.
Cancers (2022) Vol. 14, Iss. 8, pp. 2008-2008
Open Access | Times Cited: 22
Deep learning–based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images
Chong Jiang, Kai Chen, Yue Teng, et al.
European Radiology (2022) Vol. 32, Iss. 7, pp. 4801-4812
Closed Access | Times Cited: 19
Evaluation of Semiautomatic and Deep Learning–Based Fully Automatic Segmentation Methods on [18F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization
Cláudia S. Constantino, Sónia Leocádio, Francisco P. M. Oliveira, et al.
Journal of Digital Imaging (2023) Vol. 36, Iss. 4, pp. 1864-1876
Open Access | Times Cited: 11
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