Sampling the spatial patterns of cancer: optimized biopsy procedures for estimating prostate cancer volume and Gleason Score.
Sampling the spatial patterns of cancer: optimized biopsy procedures for estimating prostate cancer volume and Gleason Score.
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DOI：
10.1016/j.media.2009.05.002
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发表时间：
2009-08
影响因子：
10.9
通讯作者：
Davatzikos, Christos
中科院分区：
文献类型：
--
作者：
Ou, Yangming;Shen, Dinggang;Zeng, Jianchao;Sun, Leon;Moul, Judd;Davatzikos, Christos
关键词：
前列腺活检是目前诊断前列腺癌的金标准程序。现有的前列腺活检程序主要集中在检测癌症的存在。然而，他们往往忽略了活检用于估计癌症体积(CV)和Gleason评分(GS，癌症分级描述符)的可能性，这两个指标是衡量癌症侵袭性的替代指标，也是治疗计划的两个关键因素。为了填补这一空白，本文假设并证明，通过对癌症的空间模式进行最佳采样，可以专门设计活组织检查程序来估计CV和GS。我们的方法在基于地图集的人口研究中结合了图像分析和机器学习工具，该研究包括三个步骤。首先，通过从已知癌症基本事实的前列腺癌标本的组织图像构建统计图谱，了解癌症在患者群体中的空间分布。然后，在特征选择公式中确定最佳活检位置，以便可以使用这些位置的活检结果(癌症存在或不存在)以最佳速度区分具有不同(高与低)CV/GS值的现有标本。最后，基于二进制分类公式，利用优化的活检位置来估计新来的前列腺癌患者的CV/GS值是高还是低。在交叉验证中，通过分类率和相关的接收机工作特性(ROC)曲线来评估估计精度和泛化能力。优化的活检程序也被设计成对临床实践中几乎不可避免的针位移错误具有健壮性，并且被发现对优化参数以及培训人群的变化具有健壮性。
Prostate biopsy is the current gold-standard procedure for prostate cancer diagnosis. Existing prostate biopsy procedures have been mostly focusing on detecting cancer presence. However, they often ignore the potential use of biopsy to estimate cancer volume (CV) and Gleason Score (GS, a cancer grade descriptor), the two surrogate markers for cancer aggressiveness and the two crucial factors for treatment planning. To fill up this vacancy, this paper assumes and demonstrates that, by optimally sampling the spatial patterns of cancer, biopsy procedures can be specifically designed for estimating CV and GS. Our approach combines image analysis and machine learning tools in an atlas-based population study that consists of three steps. First, the spatial distributions of cancer in a patient population are learned, by constructing statistical atlases from histological images of prostate specimens with known cancer ground truths. Then, the optimal biopsy locations are determined in a feature selection formulation, so that biopsy outcomes (either cancer presence or absence) at those locations could be used to differentiate, at the best rate, between the existing specimens having different (high v.s. low) CV/GS values. Finally, the optimized biopsy locations are utilized to estimate whether a new-coming prostate cancer patient has high or low CV/GS values, based on a binary classification formulation. The estimation accuracy and the generalization ability are evaluated by the classification rates and the associated receiver-operating-characteristic (ROC) curves in cross validations. The optimized biopsy procedures are also designed to be robust to the almost inevitable needle displacement errors in clinical practice, and are found to be robust to variations in the optimization parameters as well as the training populations.
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DOI：
10.1073/pnas.97.1.262
发表时间：
2000-01-04
影响因子：
11.1
作者：
Brown, MPS;Grundy, WN;Haussler, D
通讯作者：
Haussler, D
影响因子：
6.6
作者：
Epstein, JI;Walsh, PC;Carter, HB
通讯作者：
Carter, HB
影响因子：
4.8
作者：
Burges, CJC
通讯作者：
Burges, CJC
影响因子：
10.6
作者：
Fan, Yong;Shen, Dinggang;Davatzikos, Christos
通讯作者：
Davatzikos, Christos
影响因子：
10.6
作者：
Frangi, AF;Rueckert, D;Niessen, WJ
通讯作者：
Niessen, WJ