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 18F-FDG-PET/CT at West China Hospital from December 2013 to May 2017. 18F-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 18F-FDG-PET images could potentially serve as a noninvasive strategy to predict the overall survival of patients with PRL and PAL.

中文翻译：

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18F-FDG-PET影像功能参数对原发性肾/肾上腺淋巴瘤患者的预后价值。

目的本研究的目的是探讨可能识别淋巴瘤区域形态变化并预测原发性肾淋巴瘤（PRL）和原发性肾上腺淋巴瘤（PAL）患者预后的质地特征。方法这项回顾性研究包括2013年12月至2017年5月在华西医院接受18F-FDG-PET / CT手术的19例非霍奇金淋巴瘤（NHL）患者。18F-FDG-PET图像由两名经核证的放射医学核证医师独立审查，并且纹理特征是从LifeX包中提取的。使用Kaplan-Meier分析法分析PET FDG摄取参数，患者的基线特征和质地参数的预后价值。使用Cox回归分析来确定影像学和临床特征之间的独立预后因素。结果纳入患者的总生存期为18.84±13.40（平均±SD）月。单变量Cox分析发现，肿瘤分期，GLCM（灰色共生矩阵）熵，GLZLM_GLNU（灰色不均匀性）和GLZLM_ZLNU（区域长度不均匀性）值是OS的重要预测指标。其中，在多变量分析中，GLLRM_RLNU≥216.6证明与较差的OS相关（HR 9.016，95％CI 1.041-78.112，p = 0.046）。结论18F-FDG-PET图像的纹理分析可作为预测PRL和PAL患者总体生存的非侵入性策略。单变量Cox分析发现，肿瘤分期，GLCM（灰色共生矩阵）熵，GLZLM_GLNU（灰色不均匀性）和GLZLM_ZLNU（区域长度不均匀性）值是OS的重要预测指标。其中，在多变量分析中，GLLRM_RLNU≥216.6表现出与较差的OS相关（HR 9.016，95％CI 1.041-78.112，p = 0.046）。结论18F-FDG-PET图像的纹理分析可作为预测PRL和PAL患者总体生存的非侵入性策略。单变量Cox分析发现，肿瘤分期，GLCM（灰色共生矩阵）熵，GLZLM_GLNU（灰色不均匀性）和GLZLM_ZLNU（区域长度不均匀性）值是OS的重要预测指标。其中，GLLRM_RLNU≥216.6在多变量分析中显示与较差的OS相关（HR 9.016，95％CI 1.041-78.112，p = 0.046）。结论18F-FDG-PET图像的纹理分析可作为预测PRL和PAL患者总体生存的非侵入性策略。p = 0.046）。结论18F-FDG-PET图像的纹理分析可作为预测PRL和PAL患者总体生存的非侵入性策略。p = 0.046）。结论18F-FDG-PET图像的纹理分析可作为预测PRL和PAL患者总体生存的非侵入性策略。