Name: guidelines and experience using imaging biomarker explorer (ibex) for radiomics
Uploaded: 2018-01-08T15:00:00
Description: Watch this Scientific Journal Video about Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics at JoVE.com

瑞秋是由罗莎莉 b ·海特研究生奖学金和美国军团辅助奖学金资助的。卡洛斯卡德纳斯已由伊尔克什坦博士在生物医学科学研究金的资助。野生山羊的发展由 R03 CA178495 的资助。

1. 安装山羊
注意: 要安装源代码版本, 请转到步骤1.1。或者, 要安装 stand-alone 版本, 请转到步骤1.2。
<ol><li>
源代码版本
	<ol>
<li>转到野山羊源代码版本网站3。下载 "IBEX_Source. zip" 和 "How_to_use pdf" 文件。在 "How_to_use pdf" 文件中查找必备使用最新的野山羊版本。 注意: 野山羊只在32位和64位 Matlab 版本中分别工作2011a 和2014b。</li>
		<li>下载完成后, 解...

<ol>
	<li>Lambin, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Radiomics:+extracting+more+information+from+medical+images+using+advanced+feature+analysis.">Radiomics: extracting more information from medical images using advanced feature analysis.</a> EJC. 48, 441-446 (2012).</li><li>Zhang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IBEX:+an+open+infrastructure+software+platform+to+facilitate+collaborative+work+in+radiomics.">IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics.</a> Med Phys. 42, 1341-1353 (2015).</li><li>Haralick, R. M., Shanmugam, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Textural+features+for+image+classification.">Textural features for image classification.</a> IEEE Trans Syst Man Cybern. , 610-621 (1973).</li><li>Galloway, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Texture+analysis+using+gray+level+run+lengths.">Texture analysis using gray level run lengths.</a> Comp Graphics and Im Proc. 4, 172-179 (1975).</li><li>Tang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Texture+information+in+run-length+matrices.">Texture information in run-length matrices.</a> IEEE Trans on Im Proc. 7, 1602-1609 (1998).</li><li>Amadasun, M., King, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Textural+features+corresponding+to+textural+properties.">Textural features corresponding to textural properties.</a> IEEE Trans Syst Man Cybern. 19, 1264-1274 (1989).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preliminary+investigation+into+sources+of+uncertainty+in+quantitative+imaging+features.">Preliminary investigation into sources of uncertainty in quantitative imaging features.</a> Comp Med Imaging Graph. 44, 54-61 (2015).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Can+radiomics+features+be+reproducibly+measured+from+CBCT+images+for+patients+with+non-small+cell+lung+cancer.">Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer.</a> Med Phys. 42, 6784-6797 (2015).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+image+preprocessing+on+the+volume+dependence+and+prognostic+potential+of+radiomics+features+in+non-small+cell+lung+cancer.">Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer.</a> Trans Cancer Res. 5, 349-363 (2016).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Delta-radiomics+features+for+the+prediction+of+patient+outcomes+in+non-small+cell+lung+cancer.">Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer.</a> Scientific Reports. 7, 588 (2017).</li><li>Fried, D. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stage+III+Non-Small+Cell+Lung+Cancer:+Prognostic+Value+of+FDG+PET+Quantitative+Imaging+Features+Combined+with+Clinical+Prognostic+Factors.">Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors.</a> Radiology. 278, 214-222 (2016).</li><li>Fried, D. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Potential+Use+of+(18)F-fluorodeoxyglucose+Positron+Emission+Tomography-Based+Quantitative+Imaging+Features+for+Guiding+Dose+Escalation+in+Stage+III+Non-Small+Cell+Lung+Cancer.">Potential Use of (18)F-fluorodeoxyglucose Positron Emission Tomography-Based Quantitative Imaging Features for Guiding Dose Escalation in Stage III Non-Small Cell Lung Cancer.</a> IJROBP. 94, 368-376 (2016).</li><li>Mackin, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+Computed+Tomography+Scanner+Variability+of+Radiomics+Features.">Measuring Computed Tomography Scanner Variability of Radiomics Features.</a> Invest radiol. 50, 757-765 (2015).</li><li>Yang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Uncertainty+analysis+of+quantitative+imaging+features+extracted+from+contrast-enhanced+CT+in+lung+tumors.">Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors.</a> Comp Med Imaging Graph. 48, 1-8 (2016).</li><li>van Rossum, P. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+incremental+value+of+subjective+and+quantitative+assessment+of+18F-FDG+PET+for+the+prediction+of+pathologic+complete+response+to+preoperative+chemoradiotherapy+in+esophageal+cancer.">The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer.</a> JNM. 57, 691-700 (2016).</li><li>Hunter, L. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NSCLC+tumor+shrinkage+prediction+using+quantitative+image+features.">NSCLC tumor shrinkage prediction using quantitative image features.</a> Comp Med Imaging Graph. 49, 29-36 (2016).</li><li>Hunter, L. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+quality+machine-robust+image+features:+identification+in+nonsmall+cell+lung+cancer+computed+tomography+images.">High quality machine-robust image features: identification in nonsmall cell lung cancer computed tomography images.</a> Med Phys. 40, 121916 (2013).</li><li>Fried, D. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prognostic+value+and+reproducibility+of+pretreatment+CT+texture+features+in+stage+III+non-small+cell+lung+cancer.">Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer.</a> IJROBP. 90, 834-842 (2014).</li><li>Gan, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MO-DE-207B-09:+A+Consistent+Test+for+Radiomics+Softwares.">MO-DE-207B-09: A Consistent Test for Radiomics Softwares.</a> Med Phys. 43, 3706-3706 (2016).</li><li>Klawikowski, S., Christian, J., Schott, D., Zhang, M., Li, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SU-D-207B-07:+Development+of+a+CT-Radiomics+Based+Early+Response+Prediction+Model+During+Delivery+of+Chemoradiation+Therapy+for+Pancreatic+Cancer.">SU-D-207B-07: Development of a CT-Radiomics Based Early Response Prediction Model During Delivery of Chemoradiation Therapy for Pancreatic Cancer.</a> Med Phys. 43, 3350-3350 (2016).</li><li>Huang, W., Tu, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SU-F-R-22:+Malignancy+Classification+for+Small+Pulmonary+Nodules+with+Radiomics+and+Logistic+Regression.">SU-F-R-22: Malignancy Classification for Small Pulmonary Nodules with Radiomics and Logistic Regression.</a> Med Phys. 43, 3377-3378 (2016).</li><li>Hanania, A. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+imaging+to+evaluate+malignant+potential+of+IPMNs.">Quantitative imaging to evaluate malignant potential of IPMNs.</a> Oncotarget. 7, 85776-85784 (2016).</li><li>Court, L. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+resources+for+radiomics.">Computational resources for radiomics.</a> Trans Cancer Res. 5, 340-348 (2016).</li><li>. Matlab Add path Available from: <a target="_blank" href="https://www.mathworks.com/help/matlab/ref/addpath.html">https://www.mathworks.com/help/matlab/ref/addpath.html</a> (2017)</li><li>Zhao, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reproducibility+of+radiomics+for+deciphering+tumor+phenotype+with+imaging.">Reproducibility of radiomics for deciphering tumor phenotype with imaging.</a> Sci Rep. 6, 23428 (2016).</li><li>Owens, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reproducibility+and+Robustness+of+Radiomic+Features+Extracted+with+Semi-Automatic+Segmentation+Tools.">Reproducibility and Robustness of Radiomic Features Extracted with Semi-Automatic Segmentation Tools.</a> Med Phys. , (2017).</li><li>Kassner, A., Liu, F., Thornhill, R. E., Tomlinson, G., Mikulis, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+hemorrhagic+transformation+in+acute+ischemic+stroke+using+texture+analysis+of+postcontrast+T1-weighted+MR+images.">Prediction of hemorrhagic transformation in acute ischemic stroke using texture analysis of postcontrast T1-weighted MR images.</a> JMRI. 30, 933-941 (2009).</li><li>. IBEX Google Forum Available from: <a target="_blank" href="https://groups.google.com/forum/#!forum/ibex_users">https://groups.google.com/forum/#!forum/ibex_users</a> (2017)</li></ol>

野山羊是医学影像 radiomics 研究的有力工具。迄今为止, 它主要用于放射肿瘤学的目的, 由 MD 安德森 radiomics 组进行的研究。野山羊允许操作的 roi 和计算功能在5主要特征类别。源代码版本的野山羊允许用户设计的应用程序, 是不是已经部分的野山羊, 如灰度级区域矩阵的功能。
roi 的主要步骤包括图像的导入、轮廓的绘制、数据集的 roi 选择以及特征集的创建。精确的轮廓是必?...

在医学上, 患者疾病诊断通常包含大量的诊断检查, 如 x 光、超声、计算机断层扫描 (CT)、磁共振成像 (MRI) 和正电子发射断层成像 (PET), 以协助确定病人护理的过程。虽然医生使用这些图像定性地评估病人的诊断, 可能有额外的数量特征, 可以提取, 以指导病人的护理。其基本原理是, 这些特征可能代表了在宏观尺度1上表达的蛋白质和基因组模式。将这些量化信息与当前的临床信息 (如、患者的人口统计数据) 相结合, 可能会允许更多的个性化病人护理。这是 radiomics 背后的理论: 体素水平上的图像特征分析。特征通常分为5大类: 灰度共生矩阵、灰度级运行长度矩阵、邻域强度差分矩阵、直方图和形状。
成像生物标记资源管理器 (野山羊) 是一个开源工具, 用于 radiomics 工作2。图形用户界面 (GUI) 是在 MD 安德森癌症中心开发的, 目的是促进定量特征的提取和计算, 以帮助癌症治疗中的决策。在线可以使用源代码3和 stand-alone4版本。野山羊计算医疗 radiomics 中使用的5种最常见的功能类别, 并可为每个功能类别设置参数。类别有: 灰度共生矩阵5、灰度级运行长度矩阵6、7、强度、邻域强度差矩阵8和形状。由于野山羊是开源的, 它允许跨机构统一的特征提取结果, 以方便地比较不同的 radiomics 研究。在张et al.的初始文件中描述了野山羊中的所有特征2
这份手稿的目的是提供指导, 如何使用野山羊和演示其应用通过同行评议发表的研究从 MD 安德森 radiomics 集团。自2015年向公众发布以来, 野山羊已被用来计算 CT、PET 和 MRI 扫描图像的特征, MD 安德森 radiomics 组, 典型的调查功能, 以改善临床生存模式9,10, 11,12,13,14,15,16,17,18,19,20并由外部机构21、22、23、24。在法院et al.中可以找到可用于 radiomics 研究中未包括在野山羊中的步骤的其他软件工具指南25
对山羊的工作流程的一般介绍将帮助组织数据正确之前开始 radiomics 项目利用野山羊。如果导入 dicom 图像, 则要求每个患者都有自己的 dicom 图像文件夹。DICOM 辐射结构集是可选的, 包括在病人的文件夹, 但建议, 而不是使用的轮廓平台在山羊。为了帮助所有病人导入一个特定的研究, 所有的病人文件夹都可以放在一个文件夹中, 以便所有的数据都可以只用一个步骤导入到野山羊中。如果从顶点导入病人, 最好是将患者计划的结构设置好。由于患者可能有多个图像集和计划在顶峰, 最好是知道哪个图像集和计划是正确的, 然后再导入。如果计算时间是一个问题, 减少病人的图像切片数量可以大大缩短时间。例如, 如果只有肝脏是研究的兴趣, 但病人有全身 CT 扫描, 减少 dicom 切片只在感兴趣的区域范围内可以缩短计算时间 (例如, 将 dicom 从300切片减少到50切片可以采取 1/6 t时间)。有不同的工具可用于执行此切片减少, 从手动到半自动。

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			<td height="42" style="height:42px;width:216px;">Excel</td>
			<td style="width:71px;">Microsoft Office</td>
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			<td style="width:167px;">Any version of excel should work.</td>
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			<td height="42" style="height:42px;width:216px;">Matlab</td>
			<td style="width:71px;">MathWorks</td>
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			<td>Only use IBEX on 32 bit Matlab 2011a or 64 bit Matlab 2014b.</td>
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guidelines and experience using imaging biomarker explorer (ibex) for radiomics

成像生物标志物探险家 (野山羊) 是一个开源工具的医学成像 radiomics 工作。本文的目的是描述如何使用山羊的图形用户界面 (GUI), 并说明如何在临床研究中使用了野山羊的计算功能。野山羊允许输入 dicom 图像与 dicom 辐射治疗结构文件或顶点文件。一旦图像被导入, 在数据选择 GUI 中, 野山羊就有工具来操纵图像的查看, 测量体素值和距离, 并创建和编辑轮廓。野山羊有27预处理和132功能选择来设计特征集。每个预处理和特征类别都有可以更改的参数。从野山羊的输出是一个电子表格, 包含: 1) 每一个功能, 从功能集计算每个轮廓的数据集, 2) 的图像信息的每个轮廓在一个数据集, 和 3) 的预处理和功能与他们所选择的使用摘要参数.从野山羊身上计算的特征已经用于研究不同成像条件和生存模式下的特征的变异性, 以改善目前的临床模型。

来自野山羊的输出是一个电子表格 (参见图 4), 其中包含3选项卡。"结果" 选项卡包含数据集中每个 ROI 的功能值 (图 4A)。"数据信息" 选项卡包含有关从数据集中的每个 ROI 中获取的图像的信息 (图 4B)。"功能信息" 选项卡包含与为特征类别选择的参数和用于该功能类别的预处理 (<strong...

Watch this Scientific Journal Video about Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics at JoVE.com

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

我们描述了一种用于医学影像 radiomics 研究的开源工具, 以及如何使用这个工具。此外, 一些已出版的作品, 已使用野山羊的不确定性分析和模型建设展示。

使用成像生物标志物探险家 (野山羊) Radiomics 的指南和经验

Imaging Biomarker Explorer (IBEX) is an open-source tool for medical imaging radiomics work. The purpose of this paper is to describe how to use IBEX's ...

Research

JoVE Journal

Engineering

Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics

Concurrent Quantitative Conductivity and Mechanical Properties Measurements of Organic Photovoltaic Materials using AFM

并行定量的导电性和力学性能测试的有机光伏材料利用原子力显微镜

Organic photovoltaic  (OPV) materials are inherently inhomogeneous at the nanometer scale. Nanoscale  inhomogeneity of OPV materials affects performance ...

科研

工程学

Spatial Separation of Molecular Conformers and Clusters

分子构象器和星团的空间分离

Gas-phase molecular physics and physical chemistry experiments commonly use supersonic expansions through pulsed valves for the production of cold ...

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

多重流体相储层孔隙条件级成像使用X射线微断层

X-ray microtomography was used to image, at a resolution of 6.6 &#181;m, the pore-scale arrangement of residual carbon dioxide ganglia in the pore-space ...

Electron Channeling Contrast Imaging for Rapid III-V Heteroepitaxial Characterization

电子通道造影的快速III-V异质特征

Misfit dislocations in heteroepitaxial layers of GaP grown on Si(001) substrates are characterized through use of electron channeling contrast imaging ...

Use of a Multi-compartment Dynamic Single Enzyme Phantom for Studies of Hyperpolarized Magnetic Resonance Agents

一个多腔动态单一酶幻影超极化磁共振剂研究的用途

Imaging of hyperpolarized substrates by magnetic resonance shows great clinical promise for assessment of critical biochemical processes in real time. Due ...

Dynamic Pore-scale Reservoir-condition Imaging of Reaction in Carbonates Using Synchrotron Fast Tomography

用同步快速反应层析成像动态孔隙规模油藏条件下成像碳酸盐

Underground storage permanence is a major concern for carbon capture and storage. Pumping CO2 into carbonate reservoirs has the potential to dissolve ...

High-resolution Thermal Micro-imaging Using Europium Chelate Luminescent Coatings

高分辨率热显微成像采用铕螯合物的荧光涂料

Micro-electronic devices often undergo significant self-heating when biased to their typical operating conditions. This paper describes a convenient ...

Advanced Compositional Analysis of Nanoparticle-polymer Composites Using Direct Fluorescence Imaging

纳米粒子 - 聚合物复合材料使用直接荧光成像的先进成分分析

The fabrication of polymer-nanoparticle composites is extremely important in the development of many functional materials. Identifying the precise ...

Nanostructured Ag-zeolite Composites as Luminescence-based Humidity Sensors

纳米银沸石复合材料作为基于发光的湿度传感器

Small silver clusters confined inside zeolite matrices have recently emerged as a novel type of highly luminescent materials. Their emission has high ...

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

流成像技术同时测量湍流和粒子运动学

Numerous problems in scientific and engineering fields involve understanding the kinematics of particles in turbulent flows, such as contaminants, marine ...