论著摘要 | 【Radiomics-CT】CT灌注图中的影像组学特征的稳定性(双语版)

2018-05-18 09:45:34 admin
标签:   影像组学 CT CT灌注 非小细胞肺癌 口咽癌 特征值标准化

Stability of radiomic features in CT perfusion maps.

发表日期: 2016.12.01   来源:Phys Med Biol. 2016 Dec 21;61(24):8736-8749.

作者:

Bogowicz M11, Riesterer O, Bundschuh RA, Veit-Haibach P, Hüllner M, Studer G, Stieb S, Glatz S, Pruschy M, Guckenberger M, Tanadini-Lang S.

作者介绍:

1. Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zürich, Switzerland.

摘要

本研究旨在确定CT灌注(CTP)图中关于CTP计算因子和图像离散化的一组稳定的影像组学参数,作为未来局部肿瘤化疗放疗反应预后模型的输入。对11例口咽癌患者和11例非小细胞肺癌(NSCLC)患者的治疗前CTP图像进行分析。每个灌注图(血量,血流量和平均通过时间)对315个影像组学参数进行了研究。使用组内相关性(ICC)研究了潜在的可标准化(图像离散化方法,Hounsfield单位(HU)阈值,体素大小和时间分辨率)和非标准化(动脉轮廓和噪声阈值)灌注计算因子的影像组学稳健性。为了获得与肿瘤体积相关的模型的影像组学参数的附加价值,从分析中排除了众所周知的局部肿瘤化疗放疗反应的预测因子。其余的稳定的影像组学参数根据参数之间的Spearman相关性进行分组,并且对于每个组,具有最高ICC的参数被包括在最终集合中。 ICC和相关性的接受程度分别为0.9和0.7。使用固定数量的排序箱或固定间隔的图像离散化方法给出了类似数量的稳定的影像组学参数(大约40%)。潜在的可标准化的因素比非标准化引入了更多的可变性影像组学参数,不稳定率分别为56-98%和43-58%。体素大小观察到最高的可变性(两个患者队列的不稳定率> 97%)。没有CTP计算因子的标准化,研究的影像组学参数没有一个是稳定的。对非标准化因素进行标准化之后,在参数间相关性校正之后,对于两个患者队列,10个影像组学参数是稳定的。必须对体素大小,图像离散化,HU阈值和时间分辨率进行标准化,以建立一个基于CTP影像组学分析的可靠预测模型。

Abstact

This study aimed to identify a set of stable radiomic parameters in CT perfusion (CTP) maps with respect to CTP calculation factors and image discretization, as an input for future prognostic models for local tumor response to chemo-radiotherapy. Pre-treatment CTP images of eleven patients with oropharyngeal carcinoma and eleven patients with non-small cell lung cancer (NSCLC) were analyzed. 315 radiomic parameters were studied per perfusion map (blood volume, blood flow and mean transit time). Radiomics robustness was investigated regarding the potentially standardizable (image discretization method, Hounsfield unit (HU) threshold, voxel size and temporal resolution) and non-standardizable (artery contouring and noise threshold) perfusion calculation factors using the intraclass correlation (ICC). To gain added value for our model radiomic parameters correlated with tumor volume, a well-known predictive factor for local tumor response to chemo-radiotherapy, were excluded from the analysis. The remaining stable radiomic parameters were grouped according to inter-parameter Spearman correlations and for each group the parameter with the highest ICC was included in the final set. The acceptance level was 0.9 and 0.7 for the ICC and correlation, respectively. The image discretization method using fixed number of bins or fixed intervals gave a similar number of stable radiomic parameters (around 40%). The potentially standardizable factors introduced more variability into radiomic parameters than the non-standardizable ones with 56-98% and 43-58% instability rates, respectively. The highest variability was observed for voxel size (instability rate  >97% for both patient cohorts). Without standardization of CTP calculation factors none of the studied radiomic parameters were stable. After standardization with respect to non-standardizable factors ten radiomic parameters were stable for both patient cohorts after correction for inter-parameter correlations. Voxel size, image discretization, HU threshold and temporal resolution have to be standardized to build a reliable predictive model based on CTP radiomics analysis.

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Keywords:

阅读原文:PMID: 27893446  DOI: 10.1088/1361-6560/61/24/8736


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