• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Radiology >Validation of quantitative analysis of multiparametric prostate MR images for prostate cancer detection and aggressiveness assessment: A cross-imager study
【24h】

Validation of quantitative analysis of multiparametric prostate MR images for prostate cancer detection and aggressiveness assessment: A cross-imager study

机译：多参数前列腺MR图像用于前列腺癌检测和侵袭性评估的定量分析的有效性：一项交叉成像研究

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

Purpose: To validate three previously identified quantitative image features across multiparametric magnetic resonance (MR) images acquired with imagers made by two different manufacturers to differentiate prostate cancer (PC) from normal prostatic tissue and to assess cancer aggressiveness. Materials and Methods: This study was HIPAA-compliant and approved by the institutional review board. Preoperative 1.5-T multiparametric endorectal MR images of 119 PC patients (dataset A, 71 patients; dataset B, 48 patients) were analyzed, and 265 PC and normal peripheral zone regions of interests (ROIs) were identified through histologic and MR consensus review. The 10th percentile average apparent diffusion coefficient (ADC) value, average ADC value, and skewness of T2-weighted signal-intensity histogram were evaluated with area under the receiver operating characteristic curve (AUC). The image features were combined with a linear discriminant analysis classifier and evaluated both on the image dataset of each type of imager alone (leave-one-patient-out evaluation) and across the datasets (training on one dataset, testing on the other). Spearman correlation coefficient was calculated between the image features and ROI-specific Gleason scores. Results: AUC values of the image features combined were 0.95 ± 0.02 (standard error) and 0.88 ± 0.03 on dataset B and dataset A alone, respectively, and 0.96 ± 0.02 and 0.89 ± 0.03 when training on dataset A and testing on dataset B and vice versa, respectively. Spearman correlation coefficients between Gleason scores and the ADC features were between 20.27 and 20.34. Conclusion: Consistently across images from datasets A and B, the 10th percentile ADC value, average ADC value, and T2-weighted skewness can distinguish PC from normal-tissue ROIs, and ADC features correlate moderately with ROIspecific Gleason scores.
机译：目的：验证由两个不同制造商制造的成像仪采集的多参数磁共振（MR）图像中三个先前确定的定量图像特征，以区分前列腺癌（PC）与正常前列腺组织并评估癌症侵袭性。材料和方法：这项研究符合HIPAA，并得到机构审查委员会的批准。分析了119例PC患者（数据集A，71例患者;数据集B，48例患者）的术前1.5-T多参数直肠内MR图像，并通过组织学和MR共识性检查确定了265例PC和正常周围区域（ROI）。使用接收器工作特性曲线（AUC）下的面积评估了T2加权信号强度直方图的第10个百分位数平均表观扩散系数（ADC）值，平均ADC值和偏度。将图像特征与线性判别分析分类器组合在一起，并分别在每种类型的成像仪的图像数据集上进行评估（留一人耐心评估），并在整个数据集上进行评估（对一个数据集进行训练，对另一数据集进行测试）。在图像特征和ROI特定的Gleason得分之间计算Spearman相关系数。结果：仅在数据集B和数据集A上组合的图像特征的AUC值分别为0.95±0.02（标准误差）和0.88±0.03，在数据集A上进行训练并在数据集B和数据集上进行测试时，分别为0.96±0.02和0.89±0.03。反之亦然。格里森得分与ADC特征之间的Spearman相关系数在20.27至20.34之间。结论：在数据集A和B的图像中，ADC值，平均ADC值和T2加权偏斜的第10个百分数始终可以将PC与正常组织的ROI区分开，并且ADC特征与ROI特定的格里森评分有适度的相关性。

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