论著摘要 |【Radiomics-CT】多组CT成像的影像组学特征之间的评估一致性(双语版)

2018-01-16 11:06:21 admin 0
标签:   影像组学 radiomics CT 肿瘤 定量图像特征

Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings.

发表日期: 2016.12.29   来源:PLoS One. 2016 Dec 29;11(12):e0166550.

作者:

Lu L1, Ehmke RC2, Schwartz LH1, Zhao B1.

作者介绍:

1. Department of Radiology, Columbia University Medical Center, New York, NY, United States of America.

2. Department of Medicine, Columbia University Medical Center, New York, NY, United States of America.

摘要

Abstact

目标

影像组学利用定量图像特征(QIF)来表征肿瘤表型。实际上,获得的放射学图像来自于不同厂商的设备及使用不同的成像采集设置。我们的目标是评估改变切片厚度和重建算法两个参数从CT图像计算的QIF的组间一致性。

Objectives

Radiomics utilizes quantitative image features (QIFs) to characterize tumor phenotype. In practice, radiological images are obtained from different vendors' equipment using various imaging acquisition settings. Our objective was to assess the inter-setting agreement of QIFs computed from CT images by varying two parameters, slice thickness and reconstruction algorithm.

材料和方法

IRB批准/符合HIPAA标准的32名肺癌患者的CT图像的研究评估用于分析。使用两种重建算法(Lung [L]和Standard [S])和三个切片厚度(1.25mm,2.5mm和5mm)的组合将每个扫描的原始数据重建为六个成像系列,如1.25L,1.25S,2.5L,2.5S,5L和5S。对于每个成像组,从32个肿瘤(每个患者一个肿瘤)中,每一个都计算89个明确定义的QIF。这六个组进行15种组间比较(成对组合)。进行分级群聚以减少QIF冗余。一致性相关系数(CCCs)用于评估非冗余特征组的组间一致性。每组的CCC均通过组内平均QIFs的CCC进行评估。

Material and methods

CT images from an IRB-approved/HIPAA-compliant study assessing thirty-two lung cancer patients were included for the analysis. Each scan's raw data were reconstructed into six imaging series using combinations of two reconstruction algorithms (Lung[L] and Standard[S]) and three slice thicknesses (1.25mm, 2.5mm and 5mm), i.e., 1.25L, 1.25S, 2.5L, 2.5S, 5L and 5S. For each imaging-setting, 89 well-defined QIFs were computed for each of the 32 tumors (one tumor per patient). The six settings led to 15 inter-setting comparisons (combinatorial pairs). To reduce QIF redundancy, hierarchical clustering was done. Concordance correlation coefficients (CCCs) were used to assess inter-setting agreement of the non-redundant feature groups. The CCC of each group was assessed by averaging CCCs of QIFs in the group.

结果

创建了23个非冗余特征组。在所有特征组中,最好的组间一致性(CCCs > 0.8)分别为1.25S vs 2.5S,1.25L vs 2.5L,2.5S vs 5S;最差(CCCs < 0.51)属于1.25L vs 5S和2.5L vs 5S。在所有成像组中,与尺寸、形状和粗糙纹理相关的8个特征组的平均CCC > 0.8。

Results

Twenty-three non-redundant feature groups were created. Across all feature groups, the best inter-setting agreements (CCCs>0.8) were 1.25S vs 2.5S, 1.25L vs 2.5L, and 2.5S vs 5S; the worst (CCCs<0.51) belonged to 1.25L vs 5S and 2.5L vs 5S. Eight of the feature groups related to size, shape, and coarse texture had an average CCC>0.8 across all imaging settings.

结论

当从使用不同算法和切片厚度重构的CT图像计算特征时,QIF存在不同程度的组间不一致性。我们的研究结果突出了协调成像采集的重要性,以获得一致的QIFs来研究肿瘤成像表型。

Conclusions

Varying degrees of inter-setting disagreements of QIFs exist when features are computed from CT images reconstructed using different algorithms and slice thicknesses. Our findings highlight the importance of harmonizing imaging acquisition for obtaining consistent QIFs to study tumor imaging phonotype.

阅读原文:PMID: 28033372  PMCID: PMC5199063  DOI: 10.1371/journal.pone.0166550


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