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Prognostic Value of Functional Parameters of (18)F-FDG-PET Images in Patients with Primary Renal/Adrenal Lymphoma

OBJECTIVES: The aim of this study is to explore the textural features that may identify the morphological changes in the lymphoma region and predict the prognosis of patients with primary renal lymphoma (PRL) and primary adrenal lymphoma (PAL). METHODS: This retrospective study comprised nineteen no...

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Detalles Bibliográficos
Autores principales: Wang, Manni, Xu, Hui, Xiao, Liu, Song, Wenpeng, Zhu, Sha, Ma, Xuelei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683818/
https://www.ncbi.nlm.nih.gov/pubmed/31427906
http://dx.doi.org/10.1155/2019/2641627
Descripción
Sumario:OBJECTIVES: The aim of this study is to explore the textural features that may identify the morphological changes in the lymphoma region and predict the prognosis of patients with primary renal lymphoma (PRL) and primary adrenal lymphoma (PAL). METHODS: This retrospective study comprised nineteen non-Hodgkin's lymphoma (NHL) patients undergoing (18)F-FDG-PET/CT at West China Hospital from December 2013 to May 2017. (18)F-FDG-PET images were reviewed independently by two board certificated radiologists of nuclear medicine, and the texture features were extracted from LifeX packages. The prognostic value of PET FDG-uptake parameters, patients' baseline characteristics, and textural parameters were analyzed using Kaplan–Meier analysis. Cox regression analysis was used to identify the independent prognostic factors among the imaging and clinical features. RESULTS: The overall survival of included patients was 18.84 ± 13.40 (mean ± SD) months. Univariate Cox analyses found that the tumor stage, GLCM (gray-level co-occurrence matrix) entropy, GLZLM_GLNU (gray-level nonuniformity), and GLZLM_ZLNU (zone length nonuniformity), values were significant predictors for OS. Among them, GLRLM_RLNU ≥216.6 demonstrated association with worse OS at multivariate analysis (HR 9.016, 95% CI 1.041–78.112, p=0.046). CONCLUSIONS: The texture analysis of (18)F-FDG-PET images could potentially serve as a noninvasive strategy to predict the overall survival of patients with PRL and PAL.