论著摘要 |【Radiomics-MR】通过全面的多参数磁共振成像纹理特征模型自动检测前列腺癌(双语版)

2018-06-15 10:42:24 admin
标签:   影像组学 MR 前列腺癌 扩散张量成像

Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature models.

发表日期: 2015.08.05   来源:BMC Med Imaging. 2015 Aug 5;15:27.

作者:

Khalvati F1,21,2, Wong A33, Haider MA4,54,5.

作者介绍:

1. Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. farzad.khalvati@sri.utoronto.ca.

2. Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada. farzad.khalvati@sri.utoronto.ca.

3. Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada. alexander.wong@uwaterloo.ca.

4. Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. m.haider@utoronto.ca.

5. Physical Sciences, Sunnybrook Research Institute, Toronto, ON, Canada. m.haider@utoronto.ca.

摘要

Abstact

背景

前列腺癌是北美最常见的癌症形式,也是癌症死亡的第二大原因。前列腺癌的自动检测可以在早期检测前列腺癌中起主要作用,这对患者存活率具有显着影响。虽然多参数磁共振成像(MP-MRI)在前列腺癌的诊断中显示出前景,但是现有的自动检测算法不能充分利用MP-MRI中的大量数据来提高检测的准确性。本研究的目标是通过利用MP-MRI数据设计基于影像学的自动检测前列腺癌的方法。

Background

Prostate cancer is the most common form of cancer and the second leading cause of cancer death in North America. Auto-detection of prostate cancer can play a major role in early detection of prostate cancer, which has a significant impact on patient survival rates. While multi-parametric magnetic resonance imaging (MP-MRI) has shown promise in diagnosis of prostate cancer, the existing auto-detection algorithms do not take advantage of abundance of data available in MP-MRI to improve detection accuracy. The goal of this research was to design a radiomics-based auto-detection method for prostate cancer via utilizing MP-MRI data.

方法

在这项工作中,我们提出了新的MP-MRI纹理特征模型,用于前列腺癌的基于影像学的检测。除了常规MP-MRI中的常用非侵入性成像序列,即T2加权MRI(T2w)和弥散加权成像(DWI)之外,我们提出的MP-MRI纹理特征模型包含计算的高b DWI(CHB-DWI)和称为相关扩散成像(CDI)的新的扩散成像模态。此外,所提出的纹理特征模型结合了来自单个b值图像的特征。针对传统的MP-MRI和新的MP-MRI纹理特征模型计算了一组全面的纹理特征。我们对每个模态进行特征选择分析,然后结合每种模态的最佳特征来构建优化的纹理特征模型。

Methods

In this work, we present new MP-MRI texture feature models for radiomics-driven detection of prostate cancer. In addition to commonly used non-invasive imaging sequences in conventional MP-MRI, namely T2-weighted MRI (T2w) and diffusion-weighted imaging (DWI), our proposed MP-MRI texture feature models incorporate computed high-b DWI (CHB-DWI) and a new diffusion imaging modality called correlated diffusion imaging (CDI). Moreover, the proposed texture feature models incorporate features from individual b-value images. A comprehensive set of texture features was calculated for both the conventional MP-MRI and new MP-MRI texture feature models. We performed feature selection analysis for each individual modality and then combined best features from each modality to construct the optimized texture feature models.

结果

所提出的MP-MRI纹理特征模型的性能通过使用在从真实临床MP-MRI数据集获得的40975个癌症和健康组织样本上训练的支持向量机(SVM)分类器通过留一患者交叉验证来评估。所提出的MP-MRI纹理特征模型在癌症检测准确度方面优于常规模型(即,T2w + DWI)。

Results

The performance of the proposed MP-MRI texture feature models was evaluated via leave-one-patient-out cross-validation using a support vector machine (SVM) classifier trained on 40,975 cancerous and healthy tissue samples obtained from real clinical MP-MRI datasets. The proposed MP-MRI texture feature models outperformed the conventional model (i.e., T2w+DWI) with regard to cancer detection accuracy.

结论

综合纹理特征模型被开发用于使用MP-MRI的改进的基于前列腺癌的基于半导体的检测。使用全面的纹理特征和特征选择方法,构建了优化的纹理特征模型,与传统的MP-MRI纹理特征模型相比,显着改善了前列腺癌自动检测。

Conclusions

Comprehensive texture feature models were developed for improved radiomics-driven detection of prostate cancer using MP-MRI. Using a comprehensive set of texture features and a feature selection method, optimal texture feature models were constructed that improved the prostate cancer auto-detection significantly compared to conventional MP-MRI texture feature models.

阅读原文:PMID: 26242589  PMCID: PMC4524105   DOI: 10.1186/s12880-015-0069-9


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