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使用3D打印CT体模对影像组学特征进行质量控制

Quality control of radiomic features using 3D-printed CT phantoms.

作者信息

Mahmood Usman, Apte Aditya, Kanan Christopher, Bates David D B, Corrias Giuseppe, Manneli Lorenzo, Oh Jung Hun, Erdi Yusuf Emre, Nguyen John, O'Deasy Joseph, Shukla-Dave Amita

机构信息

Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, United States.

Rochester Institute of Technology, Department of Imaging Science, Rochester, New York, United States.

出版信息

J Med Imaging (Bellingham). 2021 May;8(3):033505. doi: 10.1117/1.JMI.8.3.033505. Epub 2021 Jun 29.

Abstract

: The lack of standardization in quantitative radiomic measures of tumors seen on computed tomography (CT) scans is generally recognized as an unresolved issue. To develop reliable clinical applications, radiomics must be robust across different CT scan modes, protocols, software, and systems. We demonstrate how custom-designed phantoms, imprinted with human-derived patterns, can provide a straightforward approach to validating longitudinally stable radiomic signature values in a clinical setting. : Described herein is a prototype process to design an anatomically informed 3D-printed radiomic phantom. We used a multimaterial, ultra-high-resolution 3D printer with voxel printing capabilities. Multiple tissue regions of interest (ROIs), from four pancreas tumors, one lung tumor, and a liver background, were extracted from digital imaging and communication in medicine (DICOM) CT exam files and were merged together to develop a multipurpose, circular radiomic phantom (18 cm diameter and 4 cm width). The phantom was scanned 30 times using standard clinical CT protocols to test repeatability. Features that have been found to be prognostic for various diseases were then investigated for their repeatability and reproducibility across different CT scan modes. : The structural similarity index between the segment used from the patients' DICOM image and the phantom CT scan was 0.71. The coefficient variation for all assessed radiomic features was across 30 repeat scans of the phantom. The percent deviation (pDV) from the baseline value, which was the mean feature value determined from repeat scans, increased with the application of the lung convolution kernel, changes to the voxel size, and increases in the image noise. Gray level co-occurrence features, contrast, dissimilarity, and entropy were particularly affected by different scan modes, presenting with . : Previously discovered prognostic and popular radiomic features are variable in practice and need to be interpreted with caution or excluded from clinical implementation. Voxel-based 3D printing can reproduce tissue morphology seen on CT exams. We believe that this is a flexible, yet practical, way to design custom phantoms to validate and compare radiomic metrics longitudinally, over time, and across systems.

摘要

计算机断层扫描(CT)图像上肿瘤的定量放射组学测量缺乏标准化,这一问题普遍被认为尚未解决。为了开发可靠的临床应用,放射组学必须在不同的CT扫描模式、协议、软件和系统中都具有稳健性。我们展示了如何通过定制设计的、带有源自人体模式的体模,在临床环境中提供一种直接的方法来验证纵向稳定的放射组学特征值。

本文描述了一种设计具有解剖学依据的3D打印放射组学体模的原型流程。我们使用了具有体素打印功能的多材料、超高分辨率3D打印机。从医学数字成像和通信(DICOM)CT检查文件中提取了来自四个胰腺肿瘤、一个肺肿瘤和肝脏背景的多个感兴趣组织区域(ROI),并将它们合并在一起,以开发一个多用途的圆形放射组学体模(直径18厘米,宽4厘米)。使用标准临床CT协议对该体模进行了30次扫描以测试重复性。然后研究了已发现对各种疾病具有预后意义的特征在不同CT扫描模式下的重复性和再现性。

患者DICOM图像中使用的片段与体模CT扫描之间的结构相似性指数为0.71。在对体模进行30次重复扫描中,所有评估的放射组学特征的变异系数均[此处原文缺失具体数值]。相对于基线值的百分比偏差(pDV),即从重复扫描中确定的平均特征值,随着肺卷积核的应用、体素大小的变化以及图像噪声的增加而增加。灰度共生特征、对比度、差异度和熵尤其受到不同扫描模式的影响,呈现出[此处原文缺失具体情况]。

先前发现的具有预后意义且流行的放射组学特征在实际应用中存在变数,在临床应用中需要谨慎解释或排除。基于体素的3D打印可以再现CT检查中看到的组织形态。我们认为,这是一种灵活而实用的方法,用于设计定制体模,以纵向、随时间以及跨系统地验证和比较放射组学指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3383/8240751/99e458fd1d88/JMI-008-033505-g001.jpg

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