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临床试验中肿瘤负担定量评估的自动跟踪。

Automated tracking of quantitative assessments of tumor burden in clinical trials.

机构信息

Department of Radiology, Stanford University, Stanford, CA ; Department of Biomedical Informatics Research, Stanford University, Stanford, CA.

Department of Radiology, Stanford University, Stanford, CA.

出版信息

Transl Oncol. 2014 Feb 1;7(1):23-35. doi: 10.1593/tlo.13796. eCollection 2014 Feb.

Abstract

THERE ARE TWO KEY CHALLENGES HINDERING EFFECTIVE USE OF QUANTITATIVE ASSESSMENT OF IMAGING IN CANCER RESPONSE ASSESSMENT

  1. Radiologists usually describe the cancer lesions in imaging studies subjectively and sometimes ambiguously, and 2) it is difficult to repurpose imaging data, because lesion measurements are not recorded in a format that permits machine interpretation and interoperability. We have developed a freely available software platform on the basis of open standards, the electronic Physician Annotation Device (ePAD), to tackle these challenges in two ways. First, ePAD facilitates the radiologist in carrying out cancer lesion measurements as part of routine clinical trial image interpretation workflow. Second, ePAD records all image measurements and annotations in a data format that permits repurposing image data for analyses of alternative imaging biomarkers of treatment response. To determine the impact of ePAD on radiologist efficiency in quantitative assessment of imaging studies, a radiologist evaluated computed tomography (CT) imaging studies from 20 subjects having one baseline and three consecutive follow-up imaging studies with and without ePAD. The radiologist made measurements of target lesions in each imaging study using Response Evaluation Criteria in Solid Tumors 1.1 criteria, initially with the aid of ePAD, and then after a 30-day washout period, the exams were reread without ePAD. The mean total time required to review the images and summarize measurements of target lesions was 15% (P < .039) shorter using ePAD than without using this tool. In addition, it was possible to rapidly reanalyze the images to explore lesion cross-sectional area as an alternative imaging biomarker to linear measure. We conclude that ePAD appears promising to potentially improve reader efficiency for quantitative assessment of CT examinations, and it may enable discovery of future novel image-based biomarkers of cancer treatment response.
摘要

有两个关键挑战阻碍了癌症反应评估中定量成像评估的有效应用

1)放射科医生通常主观且有时模糊地描述成像研究中的癌症病变,2)难以重新利用成像数据,因为病变测量未以允许机器解释和互操作的格式记录。我们基于开放标准开发了一个免费的软件平台,即电子医生注释设备(ePAD),以两种方式解决这些挑战。首先,ePAD 帮助放射科医生在常规临床试验图像解释工作流程中进行癌症病变测量。其次,ePAD 以允许重新利用图像数据来分析替代治疗反应成像生物标志物的格式记录所有图像测量值和注释。为了确定 ePAD 对放射科医生在定量评估成像研究中的效率的影响,一位放射科医生评估了来自 20 名受试者的计算机断层扫描(CT)成像研究,这些受试者具有一个基线和三个连续的随访成像研究,有和没有 ePAD。放射科医生使用实体瘤反应评估标准 1.1 标准对每个成像研究中的靶病变进行测量,最初借助 ePAD,然后在 30 天的洗脱期后,在没有 ePAD 的情况下重新阅读这些检查。使用 ePAD 比不使用该工具审查图像和总结靶病变测量值所需的平均总时间缩短了 15%(P <.039)。此外,还可以快速重新分析图像以探索病变截面积作为替代线性测量的成像生物标志物。我们得出结论,ePAD 有望提高 CT 检查定量评估的读者效率,并且它可能能够发现未来的新型基于图像的癌症治疗反应生物标志物。

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本文引用的文献

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3D Slicer as an image computing platform for the Quantitative Imaging Network.
Magn Reson Imaging. 2012 Nov;30(9):1323-41. doi: 10.1016/j.mri.2012.05.001. Epub 2012 Jul 6.
3
Informatics methods to enable sharing of quantitative imaging research data.
Magn Reson Imaging. 2012 Nov;30(9):1249-56. doi: 10.1016/j.mri.2012.04.007. Epub 2012 Jul 6.
4
Informatics in radiology: improving clinical work flow through an AIM database: a sample web-based lesion tracking application.
Radiographics. 2012 Sep-Oct;32(5):1543-52. doi: 10.1148/rg.325115752. Epub 2012 Jun 27.
7
Leveraging Internet technologies with DICOM WADO.
J Digit Imaging. 2012 Oct;25(5):646-52. doi: 10.1007/s10278-012-9469-3.
8
Automated temporal tracking and segmentation of lymphoma on serial CT examinations.
Med Phys. 2011 Nov;38(11):5879-86. doi: 10.1118/1.3643027.
9
Informatics in radiology: automated structured reporting of imaging findings using the AIM standard and XML.
Radiographics. 2011 May-Jun;31(3):881-7. doi: 10.1148/rg.313105195. Epub 2011 Feb 25.

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