...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Radiotherapy and oncology: Journal of the European Society for Therapeutic Radiology and Oncology >The use of probability maps to deal with the uncertainties in prostate cancer delineation.
【24h】

The use of probability maps to deal with the uncertainties in prostate cancer delineation.

机译：使用概率图来处理前列腺癌描绘中的不确定性。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

BACKGROUND AND PURPOSE: The use of dynamic contrast-enhanced (DCE) imaging for delineation of prostate tumors requires that decisions are made on a voxel wise basis about the presence of tumor. While the sensitivity and specificity of this technique is high, we propose a probabilistic approach to deal with the intrinsic imaging uncertainty. MATERIAL AND METHODS: Twenty-nine patients with biopsy-proven prostate cancer underwent a DCE-CT exam prior to radiotherapy. From a logistic regression on K(trans) values from healthy and diseased appearing prostate regions we obtained a probability function for the presence of tumor. K(trans) parameter maps were converted into probability maps and a stratification was applied at the 5% and 95% probability level, to identify low-, intermediate-, and high-risk areas for the presence of tumor. RESULTS: In all patients, regions with high-, intermediate-, and low-risk were identified, with median volume percentages of 7.6%, 40.0%, and 52.1%, respectively. The contiguous areas that resulted from the voxel wise stratification can be interpreted as GTV, high-risk CTV, and CTV. CONCLUSIONS: K(trans) parameter maps from a DCE-CT exam can be converted into probability maps for the presence of tumor. In this way, the intrinsic uncertainty that a voxel contains tumor can be incorporated into the treatment planning process.
机译：背景和目的：使用动态对比增强（DCE）成像来描绘前列腺肿瘤需要根据体素明智地确定肿瘤的存在。虽然该技术的灵敏度和特异性很高，但我们提出了一种概率方法来处理固有的成像不确定性。材料与方法：29名经活检证实为前列腺癌的患者在放疗前接受了DCE-CT检查。通过对来自健康和患病的出现前列腺区域的K（trans）值进行逻辑回归，我们获得了肿瘤存在的概率函数。将K（trans）参数图转换为概率图，并以5％和95％概率级别进行分层，以识别存在肿瘤的低，中和高风险区域。结果：在所有患者中，确定了高，中和低风险区域，中位体积百分比分别为7.6％，40.0％和52.1％。由体素明智分层产生的连续区域可以解释为GTV，高风险CTV和CTV。结论：DCE-CT检查的K（trans）参数图可以转换为肿瘤存在的概率图。这样，可以将体素包含肿瘤的内在不确定性纳入治疗计划过程。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号