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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:

Tumor fragmentation estimated by volume surface ratio of tumors measured on 18F-FDG PET/CT is an independent prognostic factor of diffuse large B-cell lymphoma
Pierre Decazes, Stéphanie Becker, Mathieu Nessim Toledano, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2018) Vol. 45, Iss. 10, pp. 1672-1679
Closed Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

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: 472
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
Baseline PET/CT imaging parameters for prediction of treatment outcome in Hodgkin and diffuse large B cell lymphoma: a systematic review
Russell Frood, Christopher Burton, Charalampos Tsoumpas, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2021) Vol. 48, Iss. 10, pp. 3198-3220
Open Access | Times Cited: 65
Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma
Amy J. Weisman, Minnie Kieler, Scott B. Perlman, et al.
Radiology Artificial Intelligence (2020) Vol. 2, Iss. 5, pp. e200016-e200016
Open Access | Times Cited: 55
Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features
Jakoba J. Eertink, Gerben J. C. Zwezerijnen, Matthijs C. F. Cysouw, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2022) Vol. 49, Iss. 13, pp. 4642-4651
Open Access | Times Cited: 25
Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients
Amy J. Weisman, Ji‐Hyun Kim, Inki Lee, et al.
EJNMMI Physics (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 36
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
Prognostic value of the combination of volume, massiveness and fragmentation parameters measured on baseline FDG pet in high-burden follicular lymphoma
Simon Draye-Carbonnier, Vincent Camus, Stéphanie Becker, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 4
Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis
Saeed Shafiee Hasanabadi, Seyed Mahmud Reza Aghamiri, Ahmad Ali Abin, et al.
Cancers (2024) Vol. 16, Iss. 20, pp. 3511-3511
Open Access | Times Cited: 4
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
Understandings 18 FDG PET radiomics and its application to lymphoma
Luca Ceriani, Lisa Milan, Stéphane Chauvie, et al.
British Journal of Haematology (2025)
Closed Access
Integrating AI Into PET/CT and PET/MRI
R. T. Subhalakshmi, M. Nivaashini, Gowrishankar Ganesh, et al.
Advances in computational intelligence and robotics book series (2025), pp. 311-332
Closed Access
A prognostic model integrating PET‐derived metrics and image texture analyses with clinical risk factors from GOYA
Lale Kostakoğlu, F Dalmasso, Paola Berchialla, et al.
eJHaem (2022) Vol. 3, Iss. 2, pp. 406-414
Open Access | Times Cited: 15
Discovery of Pre-Treatment FDG PET/CT-Derived Radiomics-Based Models for Predicting Outcome in Diffuse Large B-Cell Lymphoma
Russell Frood, Matthew Clark, Cathy Burton, et al.
Cancers (2022) Vol. 14, Iss. 7, pp. 1711-1711
Open Access | Times Cited: 15
Indicators describing the tumor lesion aggregation and dissemination and their impact on the prognosis of patients with diffuse large B cell lymphoma receiving chimeric antigen receptor T cell therapy
Xiuyong Dang, Ping Li, Aijun Shen, et al.
Cancer Medicine (2024) Vol. 13, Iss. 6
Open Access | Times Cited: 3
Prognosis Prediction of Diffuse Large B-Cell Lymphoma in $^{18}$F-FDG PET Images Based on Multi-Deep-Learning Models
Chunjun Qian, Chong Jiang, Kai Xie, et al.
IEEE Journal of Biomedical and Health Informatics (2024) Vol. 28, Iss. 7, pp. 4010-4023
Closed Access | Times Cited: 3
Development of a radiomic-clinical nomogram for prediction of survival in patients with diffuse large B-cell lymphoma treated with chimeric antigen receptor T cells
Yeye Zhou, Bin Zhang, Jiangqin Han, et al.
Journal of Cancer Research and Clinical Oncology (2023) Vol. 149, Iss. 13, pp. 11549-11560
Open Access | Times Cited: 7
Lymphoma
Stéphane Chauvie, Alessia Castellino, Fabrizio Bergesio, et al.
PET Clinics (2024) Vol. 19, Iss. 4, pp. 561-568
Closed Access | Times Cited: 2
Gross Tumor Volume Definition and Comparative Assessment for Esophageal Squamous Cell Carcinoma From 3D 18F-FDG PET/CT by Deep Learning-Based Method
Yaoting Yue, Nan Li, Husnain Shahid, et al.
Frontiers in Oncology (2022) Vol. 12
Open Access | Times Cited: 11
Correlations between baseline 18F-FDG PET tumour parameters and circulating DNA in diffuse large B cell lymphoma and Hodgkin lymphoma
Pierre Decazes, Vincent Camus, Élodie Bohers, et al.
EJNMMI Research (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 14
Prognostic value of the metabolic bulk volume in patients with diffuse large B-cell lymphoma on baseline 18F-FDG PET-CT
Gauthier Delaby, Marc-Antoine Hubaut, Franck Morschhauser, et al.
Leukemia & lymphoma/Leukemia and lymphoma (2020) Vol. 61, Iss. 7, pp. 1584-1591
Closed Access | Times Cited: 13
Development and validation of preoperative magnetic resonance imaging-based survival predictive nomograms for patients with perihilar cholangiocarcinoma after radical resection: A pilot study
Jian Zhao, Wei Zhang, Chenglin Fan, et al.
European Journal of Radiology (2021) Vol. 138, pp. 109631-109631
Closed Access | Times Cited: 12
Prognostic value of whole-body dynamic 18F-FDG PET/CT Patlak in diffuse large B-cell lymphoma
Jiankang Yin, Hui Wang, Gan Zhu, et al.
Heliyon (2023) Vol. 9, Iss. 9, pp. e19749-e19749
Open Access | Times Cited: 3
Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images
Zhengshan Huang, Yu Guo, Ning Zhang, et al.
Computers in Biology and Medicine (2022) Vol. 151, pp. 106230-106230
Closed Access | Times Cited: 4
Radiomics in Malignant Lymphomas
Stéphane Chauvie, Luca Ceriani, Emanuele Zucca
Exon Publications eBooks (2021), pp. 71-82
Open Access | Times Cited: 5
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