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开发和验证放射治疗中交互式复发分析的自动工具:局部晚期胰腺癌治疗算法的优化。

Development and validation of automatic tools for interactive recurrence analysis in radiation therapy: optimization of treatment algorithms for locally advanced pancreatic cancer.

机构信息

Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg, 69120, Germany.

出版信息

Radiat Oncol. 2013 Jun 7;8:138. doi: 10.1186/1748-717X-8-138.

DOI:10.1186/1748-717X-8-138
PMID:24499557
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3682901/
Abstract

BACKGROUND

In radiation oncology recurrence analysis is an important part in the evaluation process and clinical quality assurance of treatment concepts. With the example of 9 patients with locally advanced pancreatic cancer we developed and validated interactive analysis tools to support the evaluation workflow.

METHODS

After an automatic registration of the radiation planning CTs with the follow-up images, the recurrence volumes are segmented manually. Based on these volumes the DVH (dose volume histogram) statistic is calculated, followed by the determination of the dose applied to the region of recurrence and the distance between the boost and recurrence volume. We calculated the percentage of the recurrence volume within the 80%-isodose volume and compared it to the location of the recurrence within the boost volume, boost + 1 cm, boost + 1.5 cm and boost + 2 cm volumes.

RESULTS

Recurrence analysis of 9 patients demonstrated that all recurrences except one occurred within the defined GTV/boost volume; one recurrence developed beyond the field border/outfield. With the defined distance volumes in relation to the recurrences, we could show that 7 recurrent lesions were within the 2 cm radius of the primary tumor. Two large recurrences extended beyond the 2 cm, however, this might be due to very rapid growth and/or late detection of the tumor progression.

CONCLUSION

The main goal of using automatic analysis tools is to reduce time and effort conducting clinical analyses. We showed a first approach and use of a semi-automated workflow for recurrence analysis, which will be continuously optimized. In conclusion, despite the limitations of the automatic calculations we contributed to in-house optimization of subsequent study concepts based on an improved and validated target volume definition.

摘要

背景

在放射肿瘤学中,复发分析是评估过程和治疗理念临床质量保证的重要组成部分。以 9 例局部晚期胰腺癌患者为例,我们开发并验证了交互式分析工具,以支持评估工作流程。

方法

在自动配准放射治疗计划 CT 与随访图像后,手动对复发体积进行分割。基于这些体积,计算剂量体积直方图(DVH)统计量,然后确定复发区域的剂量以及增敏区与复发体积之间的距离。我们计算了复发体积在 80%-等剂量体积内的百分比,并将其与复发在增敏区、增敏区+1cm、增敏区+1.5cm 和增敏区+2cm 内的位置进行了比较。

结果

对 9 例患者的复发分析表明,除 1 例外,所有复发均发生在定义的 GTV/增敏区内;1 例复发发生在靶区边界外。通过与复发相关的定义距离体积,我们可以看出 7 个复发病灶位于原发性肿瘤 2cm 半径范围内。两个较大的复发灶延伸超出了 2cm,但这可能是由于肿瘤的快速生长和/或晚期检测。

结论

使用自动分析工具的主要目标是减少进行临床分析的时间和精力。我们展示了一种半自动化工作流程的初步方法和复发分析的应用,该方法将不断得到优化。尽管自动计算存在局限性,但我们还是根据改进和验证后的靶区定义,为后续研究概念的内部优化做出了贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c303/3682901/d639b9a37a3c/1748-717X-8-138-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c303/3682901/1f7b50bb98b4/1748-717X-8-138-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c303/3682901/232aa761a6d1/1748-717X-8-138-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c303/3682901/d639b9a37a3c/1748-717X-8-138-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c303/3682901/1f7b50bb98b4/1748-717X-8-138-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c303/3682901/232aa761a6d1/1748-717X-8-138-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c303/3682901/d639b9a37a3c/1748-717X-8-138-3.jpg

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过去十年电子、临床数据收集及文档系统发展综述——我们是否已为常规医疗保健中的大数据做好准备?
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