论著摘要 |【Radiomics-CT】鉴定肺癌组织学里影像组学分类器的探索性研究(双语版)

2018-05-16 15:06:21 admin
标签:   影像组学 肺癌 计算科学 组织学亚型

Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology.

发表日期: 2016.03.01   来源:Front Oncol. 2016 Mar 30;6:71.

作者:

Wu W11, Parmar C22, Grossmann P33, Quackenbush J44, Lambin P55, Bussink J66, Mak R77, Aerts HJ33.

作者介绍:

1. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

2. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Research Institute GROW, Maastricht University, Maastricht, Netherlands.

3. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.

4. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.

5. Research Institute GROW, Maastricht University , Maastricht , Netherlands.

6. Department of Radiation Oncology, Radboud University Medical Center , Nijmegen , Netherlands.

7. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School , Boston, MA , USA.

摘要

Abstact

背景

影像组学可以通过将特征算法应用于医学成像数据来非侵略性地量化肿瘤表型特征。在对肺癌患者的这项研究中,我们研究了影像组学特征与肿瘤组织学亚型(腺癌和鳞状细胞癌)之间的关系。此外,为了预测组织学亚型,我们采用机器学习方法,独立评估其预测性能。

Background

Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study of lung cancer patients, we investigated the association between radiomic features and the tumor histologic subtypes (adenocarcinoma and squamous cell carcinoma). Furthermore, in order to predict histologic subtypes, we employed machine-learning methods and independently evaluated their prediction performance.

方法

我们的分析中包括两个独立的影像组学分组,总共350名患者。从治疗前CT图像中分割的肿瘤体积中提取总共440个影像组学特征。这些影像组学特征使用肿瘤形状和大小,强度统计学和纹理来量化医学图像上的肿瘤表型特征。进行单变量分析以评估每个特征与组织学亚型的关联。在我们的多变量分析中,我们研究了24种特征选择方法和3种分类方法来进行组织学预测。在实验组中使用多变量模型,并使用验证组ROC曲线下面积(AUC)对其进行评估。组织学从手术标本中获取。

Methods

Two independent radiomic cohorts with a combined size of 350 patients were included in our analysis. A total of 440 radiomic features were extracted from the segmented tumor volumes of pretreatment CT images. These radiomic features quantify tumor phenotypic characteristics on medical images using tumor shape and size, intensity statistics, and texture. Univariate analysis was performed to assess each feature's association with the histological subtypes. In our multivariate analysis, we investigated 24 feature selection methods and 3 classification methods for histology prediction. Multivariate models were trained on the training cohort and their performance was evaluated on the independent validation cohort using the area under ROC curve (AUC). Histology was determined from surgical specimen.

结果

在我们的单变量分析中,我们观察到53个影像组学特征与肿瘤组织学显著相关。在多变量分析中,与其他方法相比,ReliefF及其变体的特征选择方法显示出更高的预测精度。我们发现Naive Baye的分类器优于其他分类器,并且具有五个特征的最高AUC(0.72; p值 = 2.3×10-7):统计最小值,小波HLL变换rlgl低灰度级重点,小波HHL变换后统计中位数,小波HLL变换后统计偏差和小波HLH灰度共生矩阵聚合阴影。

Results

In our univariate analysis, we observed that fifty-three radiomic features were significantly associated with tumor histology. In multivariate analysis, feature selection methods ReliefF and its variants showed higher prediction accuracy as compared to other methods. We found that Naive Baye's classifier outperforms other classifiers and achieved the highest AUC (0.72; p-value = 2.3 × 10(-7)) with five features: Stats_min, Wavelet_HLL_rlgl_lowGrayLevelRunEmphasis, Wavelet_HHL_stats_median, Wavelet_HLL_stats_skewness, and Wavelet_HLH_glcm_clusShade.

结论

组织学亚型可影响肺癌患者治疗方案的选择。我们观察到影像组学特征与肺肿瘤组织学显著相关。此外,基于影像组学的多变量分类器经独立验证可用于组织学亚型的预测。尽管达到的低于最佳预测精度(AUC 0.72),但我们的分析突出了非侵入性和成本效益的精密医学影像组学的潜力。在这方面的进一步研究可能带领我们获得最佳性能,从而提高临床适用性,从而提高癌症护理的效率和疗效。

Conclusions

Histological subtypes can influence the choice of a treatment/therapy for lung cancer patients. We observed that radiomic features show significant association with the lung tumor histology. Moreover, radiomics-based multivariate classifiers were independently validated for the prediction of histological subtypes. Despite achieving lower than optimal prediction accuracy (AUC 0.72), our analysis highlights the impressive potential of non-invasive and cost-effective radiomics for precision medicine. Further research in this direction could lead us to optimal performance and therefore to clinical applicability, which could enhance the efficiency and efficacy of cancer care.

关键词:

计算科学,特征选择,肺癌病理,定量影像,影像组学

Keywords:

computational science; feature selection; lung cancer histology; quantitative imaging; radiomics

阅读原文:PMID: 27064691  PMCID: PMC4811956  DOI: 10.3389/fonc.2016.00071


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