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弥漫大 B 细胞淋巴瘤中期 F-FDG PET 中 Deauville 评分 4 和 5 病变的自动代谢肿瘤体积测量的观察者间一致性。

Interobserver Agreement on Automated Metabolic Tumor Volume Measurements of Deauville Score 4 and 5 Lesions at Interim F-FDG PET in Diffuse Large B-Cell Lymphoma.

机构信息

Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Department of Hematology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

出版信息

J Nucl Med. 2021 Nov;62(11):1531-1536. doi: 10.2967/jnumed.120.258673. Epub 2021 Mar 5.

Abstract

Metabolic tumor volume (MTV) on interim PET (I-PET) is a potential prognostic biomarker for diffuse large B-cell lymphoma (DLBCL). Implementation of MTV on I-PET requires a consensus on which semiautomated segmentation method delineates lesions most successfully with least user interaction. Methods used for baseline PET are not necessarily optimal for I-PET because of lower lesional SUVs at I-PET. Therefore, we aimed to evaluate which method provides the best delineation quality for Deauville score (DS) 4-5 DLBCL lesions on I-PET at the best interobserver agreement on delineation quality and, second, to assess the effect of lesional SUV on delineation quality and performance agreement. DS 4-5 lesions from 45 I-PET scans were delineated using 6 semiautomated methods: a fixed SUV threshold of 2.5 g/cm, a fixed SUV threshold of 4.0 g/cm, an adaptive threshold corrected for source-to-local background activity contrast at 50% of the SUV, 41% of SUV per lesion, a majority vote including voxels detected by at least 2 methods, and a majority vote including voxels detected by at least 3 methods (MV3). Delineation quality per MTV was rated by 3 independent observers as acceptable or nonacceptable. For each method, observer scores on delineation quality, specific agreement, and MTV were assessed for all lesions and per category of lesional SUV (<5, 5-10, >10). In 60 DS 4-5 lesions on I-PET, MV3 performed best, with acceptable delineation in 90% of lesions and a positive agreement of 93%. Delineation quality scores and agreement per method strongly depended on lesional SUV: the best delineation quality scores were obtained using MV3 in lesions with an SUV of less than 10 and using SUV4.0 in more F-FDG-avid lesions. Consequently, overall delineation quality and positive agreement improved by applying the most preferred method per SUV category instead of using MV3 as the single best method. The MV3- and SUV4.0-derived MTVs of lesions with an SUV of more than 10 were comparable after exclusion of visually failed MV3 contouring. For lesions with an SUV of less than 10, MTVs using different methods correlated poorly. On I-PET, MV3 performed best and provided the highest interobserver agreement regarding acceptable delineations of DS 4-5 DLBCL lesions. However, delineation-method preference strongly depended on lesional SUV. Therefore, we suggest exploration of an approach that identifies the optimal delineation method per lesion as a function of tumor F-FDG uptake characteristics, that is, SUV.

摘要

代谢肿瘤体积(MTV)在中期 PET(I-PET)中是弥漫性大 B 细胞淋巴瘤(DLBCL)的一个潜在预后生物标志物。在 I-PET 上实施 MTV 需要就哪种半自动分割方法能以最少的用户交互最成功地描绘病变达成共识。由于 I-PET 时病变的 SUV 值较低,用于基线 PET 的方法不一定最适合 I-PET。因此,我们旨在评估哪种方法在最佳观察者间一致性的情况下,为 I-PET 上 Deauville 评分(DS)为 4-5 的 DLBCL 病变提供最佳的描绘质量,其次是评估病变 SUV 对描绘质量和性能一致性的影响。从 45 个 I-PET 扫描中勾勒出 6 种半自动方法的 DS 4-5 病变:固定 SUV 阈值为 2.5 g/cm,固定 SUV 阈值为 4.0 g/cm,自适应阈值校正源至局部背景活动对比为 SUV 的 50%,SUV 每病变的 41%,包括至少 2 种方法检测到的体素的多数投票,以及包括至少 3 种方法(MV3)检测到的体素的多数投票。3 名独立观察者对每个 MTV 的描绘质量进行了可接受或不可接受的评分。对于每种方法,根据病变 SUV(<5、5-10、>10)的类别评估了观察者对描绘质量、特定一致性和 MTV 的评分。在 I-PET 上的 60 个 DS 4-5 病变中,MV3 表现最佳,90%的病变可接受描绘,阳性一致性为 93%。每个方法的描绘质量评分和一致性强烈依赖于病变 SUV:在 SUV 低于 10 的病变中使用 MV3 获得了最佳的描绘质量评分,在更 F-FDG 浓聚的病变中使用 SUV4.0。因此,通过为每个 SUV 类别应用最受欢迎的方法代替使用 MV3 作为单一最佳方法,整体描绘质量和阳性一致性得到了提高。SUV 大于 10 的病变的 MV3 和 SUV4.0 衍生的 MTV 在排除视觉上失败的 MV3 轮廓后是可比的。对于 SUV 小于 10 的病变,使用不同方法的 MTV 相关性较差。在 I-PET 上,MV3 表现最佳,在 DS 4-5 DLBCL 病变的可接受描绘方面提供了最高的观察者间一致性。然而,描绘方法的偏好强烈依赖于病变 SUV。因此,我们建议探索一种方法,该方法根据肿瘤 F-FDG 摄取特征(即 SUV)为每个病变确定最佳描绘方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf4a/8612315/6d533e1f5dc3/jnm258673absf1.jpg

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