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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:

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

Showing 1-25 of 36 citing articles:

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
Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development
Tyler Bradshaw, Ronald Boellaard, Joyita Dutta, et al.
Journal of Nuclear Medicine (2021) Vol. 63, Iss. 4, pp. 500-510
Open Access | Times Cited: 69
Deep Learning for Medical Image Cryptography: A Comprehensive Review
Kusum Lata, Linga Reddy Cenkeramaddi
Applied Sciences (2023) Vol. 13, Iss. 14, pp. 8295-8295
Open Access | Times Cited: 26
Recent advances and impending challenges for the radiopharmaceutical sciences in oncology
Suzanne E. Lapi, Peter J. H. Scott, Andrew M. Scott, et al.
The Lancet Oncology (2024) Vol. 25, Iss. 6, pp. e236-e249
Closed Access | Times Cited: 13
The Impact of Semiautomatic Segmentation Methods on Metabolic Tumor Volume, Intensity, and Dissemination Radiomics in 18F-FDG PET Scans of Patients with Classical Hodgkin Lymphoma
Julia Driessen, Gerben J. C. Zwezerijnen, Heiko Schöder, et al.
Journal of Nuclear Medicine (2022) Vol. 63, Iss. 9, pp. 1424-1430
Open Access | Times Cited: 38
18F-FDG PET/CT Maximum Tumor Dissemination (Dmax) in Lymphoma: A New Prognostic Factor?
Domenico Albano, Giorgio Treglia, Francesco Dondi, et al.
Cancers (2023) Vol. 15, Iss. 9, pp. 2494-2494
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
Artificial Intelligence in Medical Imaging and its Impact on the Rare Disease Community: Threats, Challenges and Opportunities
Navid Hasani, Faraz Farhadi, Michael Morris, et al.
PET Clinics (2021) Vol. 17, Iss. 1, pp. 13-29
Open Access | Times Cited: 34
Objective Task-Based Evaluation of Artificial Intelligence-Based Medical Imaging Methods
Abhinav K. Jha, Kyle J. Myers, Nancy A. Obuchowski, et al.
PET Clinics (2021) Vol. 16, Iss. 4, pp. 493-511
Closed Access | Times Cited: 33
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
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
Can 18F-FDG PET/CT Metabolic Tumor Volume Contribute to Better Prognostication in Pediatric Hodgkin's Lymphoma?
Sangeetha Ramdas, Saumya Sara Sunny, Hema Nalapullu Srinivasan, et al.
Indian Journal of Medical and Paediatric Oncology (2025)
Open Access
Prognostic impact of metabolic tumor volume using the SUV4.0 segmentation threshold in 1,960 lymphoma patients from prospective LYSA trials
Solène Malmon, Mad‐Hélénie Elsensohn, Catherine Thieblemont, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2025)
Closed Access
Pre‐treatment total metabolic tumour volumes in lymphoma: Does quantity matter?
Tarec Christoffer El‐Galaly, Diego Villa, Chan Y. Cheah, et al.
British Journal of Haematology (2022) Vol. 197, Iss. 2, pp. 139-155
Closed Access | Times Cited: 18
Semiquantitative 2-[18F]FDG PET/CT-based parameters role in lymphoma
Domenico Albano, Marco Ravanelli, Rexhep Durmo, et al.
Frontiers in Medicine (2024) Vol. 11
Open Access | Times Cited: 3
Children's Oncology Group's 2023 blueprint for research: Hodgkin lymphoma
Sharon M. Castellino, Lisa Giulino‐Roth, Paul Harker‐Murray, et al.
Pediatric Blood & Cancer (2023) Vol. 70, Iss. S6
Open Access | Times Cited: 8
Deep learning applications in visual data for benign and malignant hematological conditions: a systematic review and visual glossary
Andrew Srisuwananukorn, Mohamed E. Salama, Alexander T. Pearson
Haematologica (2023) Vol. 108, Iss. 8, pp. 1993-2010
Open Access | Times Cited: 5
Importance of Central Imaging Review in a Pediatric Hodgkin Lymphoma Trial Using Positron Emission Tomography Response Adapted Radiation Therapy
Bradford S. Hoppe, Kathleen M. McCarten, Qinglin Pei, et al.
International Journal of Radiation Oncology*Biology*Physics (2023) Vol. 116, Iss. 5, pp. 1025-1030
Open Access | Times Cited: 5
A nomogram for predicting the rapid progression of diffuse large B‐cell lymphoma established by combining baseline PET/CT total metabolic tumor volume, lesion diffusion, and TP53 mutations
Cong Liu, Pengyue Shi, Zhenjiang Li, et al.
Cancer Medicine (2023) Vol. 12, Iss. 16, pp. 16734-16743
Open Access | Times Cited: 5
A robust deep-learning model for fully automatic segmentation of lymphoma lesions on whole-body [18F]FDG PET/CT images
Cláudia S. Constantino, Francisco P. M. Oliveira, Sónia Leocádio, et al.
(2024), pp. 1-4
Closed Access | Times Cited: 1
Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis
Julia Moran-Sanchez, Antonio Santisteban‐Espejo, Miguel Ángel Martín‐Piedra, et al.
Biomolecules (2021) Vol. 11, Iss. 6, pp. 793-793
Open Access | Times Cited: 9
Anatomy and Physiology of Artificial Intelligence in PET Imaging
Tyler Bradshaw, Alan B. McMillan
PET Clinics (2021) Vol. 16, Iss. 4, pp. 471-482
Open Access | Times Cited: 7
Modeling contrast-to-noise ratio from list mode reconstructions of 68Ga DOTATATE PET/CT: predicting detectability of hepatic metastases in shorter acquisition PET reconstructions.
Michael Silosky, Fuyong Xing, John Wehrend, et al.
PubMed (2023) Vol. 13, Iss. 1, pp. 33-42
Closed Access | Times Cited: 2
Baseline total metabolic tumor volume (TMTV) application in Hodgkin lymphoma: a review article
Carolina Feres, Rafael Fernandes Nunes, Larissa Lane Cardoso Teixeira, et al.
Clinical and Translational Imaging (2022) Vol. 10, Iss. 3, pp. 273-284
Closed Access | Times Cited: 4
Objective task-based evaluation of artificial intelligence-based medical imaging methods: Framework, strategies and role of the physician
Abhinav K. Jha, Kyle J. Myers, Nancy A. Obuchowski, et al.
arXiv (Cornell University) (2021)
Open Access | Times Cited: 5
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