论著摘要 |【多模态】联合FDG-PET和MRI纹理特征的影像组学模型用来预测四肢软组织肉瘤中的肺转移(双语版)

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A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities.

发表日期: 2015.06.29   来源:Physics in Medicine & Biology. 2015 Jul 21;60(14):5471-96.

作者:

M Vallières1, C R Freeman2, S R Skamene2 and I El Naqa1,2.

作者介绍:

1. Medical Physics Unit, McGill University, 845 Rue Sherbrooke O, Montreal QC H3A 0G4, Canada.

2. Radiation Oncology, McGill University Health Centre, 1547 Pine Avenue West, Montreal Qc H3G 1B3, Canada.

摘要

本研究旨在开发一种联合了FDG-PET和MRI,基于纹理的模型,用于评估早期软组织肉瘤(STSs)中的肺转移风险。我们研究如果从FDG-PET和MR成像信息中创建新的综合的纹理是否可以更好地识别侵袭性肿瘤。为了实现这一目标,我们再次评估了51名患有组织学上已证实是四肢STSs的患者队列。所有患者均进行了预先的FDG-PET和MRI扫描,包括T1加权和T2加权脂肪抑制序列(T2FS)。 从单独的(FDG-PET,T1和T2FS)和融合(FDG-PET / T1和FDG-PET / T2FS)扫描的肿瘤区域中提取了九个非纹理特征(SUV度量和形状特征)和四十一个纹理特征。使用小波变换实现FDG-PET和MRI扫描的体积融合。研究了六种不同提取参数对纹理预测值的影响。使用逻辑回归使特征并入多变量模型。多变量建模策略涉及到不平衡-引导程序采样,按照以下四个步骤进行最终预测模型的构建:(1)特征集减少; (2)特征选择; (3)预测性能估计;和(4)模型系数的计算。单变量分析显示,提取纹理特征的各向同性体素大小对预测值影响最大。在多变量分析中,从融合扫描中提取的纹理特征明显优于来自肺转移预测估计的单独扫描。使用从FDG-PET / T1和FDG-PET / T2FS扫描中提取的四个纹理特征的组合表现的最好。该模型达到0.984±0.002的受体-工作特征曲线下的面积,灵敏度为0.955±0.006,引导评价中的特异度为0.926±0.004。最终,STSs诊断中的肺转移风险评估可以通过更好的适应性治疗来改善患者的预后。

Abstact

This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.

阅读原文:PMID: 26119045  DOI: 10.1088/0031-9155/60/14/5471


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