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临床读片者之间代谢肿瘤负荷与病灶糖酵解的可重复性

英文原题:Repeatability of metabolic tumor burden and lesion glycolysis between clinical readers.

查看英文原题

Repeatability of metabolic tumor burden and lesion glycolysis between clinical readers.

PubMed 2023/02/15(内容时间) Front Immunol Q1 · IF 7(JCR 2025)

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中文摘要

代谢性肿瘤体积(MTV)和肿瘤病灶糖酵解(TLG)已被证实是弥漫大B细胞淋巴瘤(DLBCL)临床结局的独立预后指标。然而,这些指标的定义尚未标准化,存在多种变异来源,其中操作者判读仍是主要来源之一。

本研究提出一项判读者重复性研究,根据病灶勾画差异评估肿瘤代谢体积(TMV)和TLG指标的计算。第一种方法中,判读者在全身扫描自动检出病灶后手动修正区域边界(判读者M,手动流程);另一名判读者采用半自动病灶识别方法,不修改边界(判读者A,半自动或自动流程)。两种方法对活动性病灶采用相同参数,依据标准摄取值(SUV)41%阈值设定。

我们系统比较了专家判读者M和A计算的MTV和TLG差异。两名判读者计算的MTV一致性良好(一致性相关系数0.96),且均可独立预测治疗后的总生存期,P值分别为0.0001和0.0002。

此外,两种判读方法计算的TLG也高度一致(一致性相关系数0.96),且均可预测总生存期(两者p<0.0001)。总之,与专家辅助测量(判读者M)相比,半自动方法(判读者A)在PET/CT扫描中对肿瘤负荷(MTV)和TLG的定量及预后评估达到可接受水平。

展开英文摘要原文

The Metabolic Tumor Volume (MTV) and Tumor Lesion Glycolysis (TLG) has been shown to be independent prognostic predictors for clinical outcome in Diffuse Large B-cell Lymphoma (DLBCL).

However, definitions of these measurements have not been standardized, leading to many sources of variation, operator evaluation continues to be one major source. In this study, we propose a reader reproducibility study to evaluate computation of TMV (& TLG) metrics based on differences in lesion delineation.

In the first approach, reader manually corrected regional boundaries after automated detection performed across the lesions in a body scan (Reader M using a manual process, or manual). The other reader used a semi-automated method of lesion identification, without any boundary modification (Reader A using a semi- automated process, or auto). Parameters for active lesion were kept the same, derived from standard uptake values (SUVs) over a 41% threshold.

We systematically contrasted MTV & TLG differences between expert readers (Reader M & A).

We find that MTVs computed by Readers M and A were both concordant between them (concordant correlation coefficient of 0. 96) and independently prognostic with a P-value of 0. 0001 and 0. 0002 respectively for overall survival after treatment.

Additionally, we find TLG for these reader approaches showed concordance (CCC of 0. 96) and was prognostic for over -all survival (p 0. 0001 for both).

In conclusion, the semi-automated approach (Reader A) provides acceptable quantification & prognosis of tumor burden (MTV) and TLG in comparison to expert reader assisted measurement (Reader M) on PET/CT scans.

论文信息

作者
Choi JW、Dean EA、Lu H、Thompson Z、Qi J、Krivenko G、Jain MD、Locke FL
第一作者单位
Department of Diagnostic Imaging and Interventional Radiology, H Lee Moffitt Cancer Center, Tampa, FL, United States.United States
通讯作者单位
Machine Learning, H. Lee. Moffitt Cancer Center, Tampa, FL, United States.United States
文献类型
美国 NIH 资助研究 · 非美国政府资助研究
期刊
Frontiers in immunology2023
原文标识
PubMed 36875072 · DOI 10.3389/fimmu.2023.994520