Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

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

Showing 1-25 of 27 citing articles:

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
Intraoperative fluorescence molecular imaging accelerates the coming of precision surgery in China
Zeyu Zhang, Kunshan He, Chongwei Chi, et al.
European Journal of Nuclear Medicine and Molecular Imaging (2022) Vol. 49, Iss. 8, pp. 2531-2543
Open Access | Times Cited: 41
Baseline PET radiomics outperform the IPI risk score for prediction of outcome in diffuse large B-cell lymphoma
Jakoba J. Eertink, Gerben J. C. Zwezerijnen, Martijn W. Heymans, et al.
Blood (2023)
Open Access | Times Cited: 23
Stacking Ensemble Learning–Based [18F]FDG PET Radiomics for Outcome Prediction in Diffuse Large B-Cell Lymphoma
Shuilin Zhao, Jing Wang, Chentao Jin, et al.
Journal of Nuclear Medicine (2023) Vol. 64, Iss. 10, pp. 1603-1609
Open Access | Times Cited: 16
Prognostic Value of18F-FDG PET Radiomics Features at Baseline in PET-Guided Consolidation Strategy in Diffuse Large B-Cell Lymphoma: A Machine-Learning Analysis from the GAINED Study
Thomas Carlier, Gauthier Frécon, Diana Mateus, et al.
Journal of Nuclear Medicine (2023) Vol. 65, Iss. 1, pp. 156-162
Open Access | Times Cited: 11
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
Axial Skeleton Radiomics of 18F-FDG PET/CT: Impact on Event-Free Survival Prediction in High-Risk Pediatric Neuroblastoma
Lijuan Feng, Shuxin Zhang, Chaoran Wang, et al.
Academic Radiology (2023) Vol. 30, Iss. 11, pp. 2487-2496
Closed Access | Times Cited: 10
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
Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma with extranodal involvement
Fenglian Jing, Xinchao Zhang, Yunuan Liu, et al.
Clinical & Translational Oncology (2024)
Closed Access | Times Cited: 3
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
Predicting event-free survival after induction of remission in high-risk pediatric neuroblastoma: combining 123I-MIBG SPECT-CT radiomics and clinical factors
Lijuan Feng, Xu Yang, Chao Wang, et al.
Pediatric Radiology (2024) Vol. 54, Iss. 5, pp. 805-819
Closed Access | Times Cited: 2
The 13th World Federation of Nuclear Medicine and Biology congress (WFNMB 2022): summarize the past half century and discuss the next half century of WFNMB-
Hirofumi Fujii, Hiroshi Toyama, Daiki Kayano, et al.
Annals of Nuclear Medicine (2024)
Closed Access | Times Cited: 2
Baseline 18F-FDG PET/CT radiomics for prognosis prediction in diffuse large B cell lymphoma
Fenglian Jing, Yunuan Liu, Xinming Zhao, et al.
EJNMMI Research (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 6
Joint Lymphoma Lesion Segmentation and Prognosis Prediction From Baseline FDG-PET Images via Multitask Convolutional Neural Networks
Peng Liu, Miao Zhang, Xiaoru Gao, et al.
IEEE Access (2022) Vol. 10, pp. 81612-81623
Open Access | Times Cited: 9
Radiomics in Oncological PET Imaging: A Systematic Review—Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers
David Morland, Elizabeth Katherine Anna Triumbari, Luca Boldrini, et al.
Diagnostics (2022) Vol. 12, Iss. 6, pp. 1330-1330
Open Access | Times Cited: 8
An Optimal Radiomics Nomogram Based on 18F-FDG PET/CT for Identifying Event-Free Survival in Pediatric Neuroblastoma
Lijuan Feng, Shuxin Zhang, Xia Lu, et al.
Academic Radiology (2023) Vol. 30, Iss. 10, pp. 2309-2320
Closed Access | Times Cited: 4
Should we use nomograms for risk predictions in diffuse large B cell lymphoma patients? A systematic review
Jelena Jelicic, Thomas Stauffer Larsen, Bosko Andjelic, et al.
Critical Reviews in Oncology/Hematology (2024) Vol. 196, pp. 104293-104293
Open Access | Times Cited: 1
Clinical Application of Radiomics in Oncology: Where Do We Stand?
Riccardo Pascuzzo, Silvio Ken Garattini, Fabio Martino Doniselli
Journal of Magnetic Resonance Imaging (2024) Vol. 60, Iss. 6, pp. 2745-2746
Open Access | Times Cited: 1
Baseline 18F-FDG PET Radiomics Predicting Therapeutic Efficacy of Diffuse Large B-Cell Lymphoma after R-CHOP (-Like) Therapy
Fenglian Jing, Xinchao Zhang, Yunuan Liu, et al.
Cancer Biotherapy and Radiopharmaceuticals (2024)
Closed Access | Times Cited: 1
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
Pet-radiomics in lymphoma and multiple myeloma: update of current literature
Luca Filippi, Cristina Ferrari, Susanna Nuvoli, et al.
Clinical and Translational Imaging (2023) Vol. 12, Iss. 2, pp. 119-135
Closed Access | Times Cited: 2
Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET)
Julian M.M. Rogasch, Kuangyu Shi, David Kersting, et al.
Nuklearmedizin - NuclearMedicine (2023) Vol. 62, Iss. 06, pp. 361-369
Open Access | Times Cited: 2
Radiomics in Malignant Lymphomas
Stéphane Chauvie, Luca Ceriani, Emanuele Zucca
Exon Publications eBooks (2021), pp. 71-82
Open Access | Times Cited: 5
CT radiomics can predict disease progression within 6 months after chimeric antigen receptor-modified T-cell therapy in relapsed/refractory B-cell non-Hodgkin's lymphoma patients
R. Huang, Hongzhi Geng, Li Zhu, et al.
Clinical Radiology (2023) Vol. 78, Iss. 10, pp. e707-e717
Open Access | Times Cited: 1
Dissemination feature based on PET/CT is a risk factor for diffuse large B cell lymphoma patients outcome
Fei Wang, Silu Cui, Lu Luo, et al.
BMC Cancer (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 1
Page 1 - Next Page

Scroll to top