King's College London and Guy's and St. Thomas' PET Center, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
J Nucl Med. 2021 Mar;62(3):332-337. doi: 10.2967/jnumed.119.238923. Epub 2020 Jul 17.
Metabolic tumor volume (MTV) is a promising biomarker of pretreatment risk in diffuse large B-cell lymphoma (DLBCL). Different segmentation methods can be used that predict prognosis equally well but give different optimal cutoffs for risk stratification. Segmentation can be cumbersome; a fast, easy, and robust method is needed. Our aims were to evaluate the best automated MTV workflow in DLBCL; determine whether uptake time, compliance or noncompliance with standardized recommendations for F-FDG scanning, and subsequent disease progression influence the success of segmentation; and assess differences in MTVs and discriminatory power of segmentation methods. One hundred forty baseline F-FDG PET/CT scans were selected from U.K. and Dutch studies on DLBCL to provide a balance between scans at 60 and 90 min of uptake, parameters compliant and noncompliant with standardized recommendations for scanning, and patients with and without progression. An automated tool was applied for segmentation using an SUV of 2.5 (SUV2.5), an SUV of 4.0 (SUV4.0), adaptive thresholding (A50P), 41% of SUV (41%), a majority vote including voxels detected by at least 2 methods (MV2), and a majority vote including voxels detected by at least 3 methods (MV3). Two independent observers rated the success of the tool to delineate MTV. Scans that required minimal interaction were rated as a success; scans that missed more than 50% of the tumor or required more than 2 editing steps were rated as a failure. One hundred thirty-eight scans were evaluable, with significant differences in success and failure ratings among methods. The best performing was SUV4.0, with higher success and lower failure rates than any other method except MV2, which also performed well. SUV4.0 gave a good approximation of MTV in 105 (76%) scans, with simple editing for a satisfactory result in additionally 20% of cases. MTV was significantly different for all methods between patients with and without progression. The 41% segmentation method performed slightly worse, with longer uptake times; otherwise, scanning conditions and patient outcome did not influence the tool's performance. The discriminative power was similar among methods, but MTVs were significantly greater using SUV4.0 and MV2 than using other thresholds, except for SUV2.5. SUV4.0 and MV2 are recommended for further evaluation. Automated estimation of MTV is feasible.
代谢肿瘤体积（MTV）是弥漫性大 B 细胞淋巴瘤（DLBCL）患者治疗前风险的一个很有前途的生物标志物。可以使用不同的分割方法，这些方法都能同样准确地预测预后，但在风险分层方面给出的最佳分割阈值不同。分割可能很繁琐；需要一种快速、简单和可靠的方法。我们的目的是评估在 DLBCL 中最佳的自动 MTV 工作流程；确定摄取时间、是否遵守或不遵守 F-FDG 扫描的标准化建议，以及随后的疾病进展是否会影响分割的成功；并评估 MTV 和分割方法的区分能力的差异。从英国和荷兰的 DLBCL 研究中选择了 140 个基线 F-FDG PET/CT 扫描，以平衡摄取 60 分钟和 90 分钟的扫描、符合和不符合扫描标准化建议的参数，以及有进展和无进展的患者。使用 SUV 为 2.5（SUV2.5）、SUV 为 4.0（SUV4.0）、自适应阈值（A50P）、41%的 SUV（41%）、至少 2 种方法检测到的体素的多数票（MV2），以及至少 3 种方法检测到的体素的多数票（MV3）的自动工具进行分割。两位独立观察者评估工具勾画 MTV 的成功程度。需要最小交互的扫描被评为成功；错过超过 50%的肿瘤或需要超过 2 次编辑步骤的扫描被评为失败。138 个扫描可评估，方法之间的成功率和失败率有显著差异。表现最好的是 SUV4.0，其成功率高于任何其他方法，除了 MV2，MV2 的失败率也较低。SUV4.0 在 105 次（76%）扫描中能很好地近似 MTV，在另外 20%的情况下只需简单编辑即可获得满意的结果。在进展和无进展的患者之间，所有方法的 MTV 均有显著差异。41%的分割方法由于摄取时间较长，表现稍差；否则，扫描条件和患者预后并不影响工具的性能。方法之间的判别能力相似，但 SUV4.0 和 MV2 的 MTV 明显大于其他阈值，除了 SUV2.5。建议进一步评估 SUV4.0 和 MV2。自动估计 MTV 是可行的。
