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For: Zhou Y, Zhu Y, Chen Z, Li J, Sang S, Deng S. Radiomic Features of 18F-FDG PET in Hodgkin Lymphoma Are Predictive of Outcomes. Contrast Media Mol Imaging 2021;2021:6347404. [PMID: 34887712 DOI: 10.1155/2021/6347404] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
Number Citing Articles
1 Deng H, Zhou Y, Lu W, Chen W, Yuan Y, Li L, Shu H, Zhang P, Ye X. Development and validation of nomograms by radiomic features on ultrasound imaging for predicting overall survival in patients with primary nodal diffuse large B-cell lymphoma. Front Oncol 2022;12:991948. [PMID: 36568168 DOI: 10.3389/fonc.2022.991948] [Reference Citation Analysis]
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3 Gong H, Tang B, Li T, Li J, Tang L, Ding C. The added prognostic values of baseline PET dissemination parameter in patients with angioimmunoblastic T‐cell lymphoma. eJHaem 2022. [DOI: 10.1002/jha2.610] [Reference Citation Analysis]
4 Frood R, Clark M, Burton C, Tsoumpas C, Frangi AF, Gleeson F, Patel C, Scarsbrook A. Utility of pre-treatment FDG PET/CT-derived machine learning models for outcome prediction in classical Hodgkin lymphoma. Eur Radiol 2022. [PMID: 36006428 DOI: 10.1007/s00330-022-09039-0] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
5 Li H, Wang M, Zhang Y, Hu F, Wang K, Wang C, Gao Z. Prediction of prognosis and pathologic grade in follicular lymphoma using 18F-FDG PET/CT. Front Oncol 2022;12:943151. [DOI: 10.3389/fonc.2022.943151] [Reference Citation Analysis]
6 Kallergi M, Georgakopoulos A, Lyra V, Chatziioannou S. Tumor Size Measurements for Predicting Hodgkin’s and Non-Hodgkin’s Lymphoma Response to Treatment. Metabolites 2022;12:285. [DOI: 10.3390/metabo12040285] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]