在非霍奇金淋巴瘤(NHL)的治疗中,多种治疗选择是可用的。改善结果预测对于优化治疗至关重要。代谢活跃肿瘤体积(MATV)已被证明是NHL的预后因素。通常使用基于标准化摄取值(SUV)的半自动阈值方法检索,从18F-氟脱氧葡萄糖正电子发射断层扫描(18F-FDGPET)图像计算。然而,目前尚无NHL的共识方法。这项研究的目的是回顾有关所使用的不同分割方法的文献,并使用内部创建的软件工具评估选定的方法。一个软件工具,开发了MUltipleSUV阈值(MUST)分割器,通过在PET图像上放置种子点来识别肿瘤位置,其次是随后的地区增长。在文献综述的基础上,选择了9种SUV阈值方法并提取了MATV。在68例NHL患者的队列中使用了MUST节段。用配对t检验评估MATV的差异,以及相关性和分布数字。在NHL患者中观察到基于不同分割方法的MATV之间的高变异性和显着差异(p<0.05)。MATV的中位数范围为35至211cc。根据文献没有确定MATV的共识。使用MUST分割器和9种选定的SUV阈值方法,我们证明了MATV的巨大和显着的变化。确定NHL患者的最佳分割方法对于进一步改善毒性预测至关重要,回应,和治疗结果,这可以由MUST-Segmenter促进。
In the treatment of Non-Hodgkin lymphoma (NHL), multiple therapeutic options are available. Improving outcome predictions are essential to optimize treatment. The metabolic active tumor volume (MATV) has shown to be a prognostic factor in NHL. It is usually retrieved using semi-automated thresholding methods based on standardized uptake values (SUV), calculated from 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) images. However, there is currently no consensus method for NHL. The aim of this study was to review literature on different segmentation methods used, and to evaluate selected methods by using an in house created software tool. A software tool, MUltiple SUV Threshold (MUST)-segmenter was developed where tumor locations are identified by placing seed-points on the PET images, followed by subsequent region growing. Based on a literature review, 9 SUV thresholding methods were selected and MATVs were extracted. The MUST-segmenter was utilized in a cohort of 68 patients with NHL. Differences in MATVs were assessed with paired t-tests, and correlations and distributions figures. High variability and significant differences between the MATVs based on different segmentation methods (p < 0.05) were observed in the NHL patients. Median MATVs ranged from 35 to 211 cc. No consensus for determining MATV is available based on the literature. Using the MUST-segmenter with 9 selected SUV thresholding methods, we demonstrated a large and significant variation in MATVs. Identifying the most optimal segmentation method for patients with NHL is essential to further improve predictions of toxicity, response, and treatment outcomes, which can be facilitated by the MUST-segmenter.