Multi-Level PET and CT Fusion Radiomics-based Survival Analysis of NSCLC Patients

Mehdi Amini, M. Nazari, Isaac Shiri, G. Hajianfar, M. Deevband, H. Abdollahi, H. Zaidi
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引用次数: 4

Abstract

To provide a comprehensive characterization of intra-tumor heterogeneity, this study proposes multi-level multimodality radiomic models derived from 18F-FDG PET and CT images by feature- and image-level fusion. Specifically, we developed fusion radiomic models to improve overall survival prediction of NSCLC patients. In this work, a NSCLC dataset including patients from two different institutions (86 patients used as training and 95 patients used as testing) was included. By extracting 225 features from CT, PET, and fused images, radiomics analysis was used to build single-modality and multimodality models where the fused images are constructed by 3D-wavelet transform fusion (WF). Two models were also developed using two feature-level fusion strategies of feature concatenation (ConFea) and feature averaging (AvgFea). Cox proportional hazard (Cox PH) regression was utilized for survival analysis. Spearman's correlation was utilized as a measure of redundancy, and then best combination of 10 most related features (ranked by univariate Cox PH) were fed into multivariate Cox model. Moreover, the median prognostic score captured from training cohort was used as an untouched threshold in the test cohort to stratify patients into low- and high-risk groups. The difference between groups was assessed using log-rank test. Among all models, WF (C-index=0.708) had the highest index and significantly outperformed CT and PET (C-index = 0.616, 0.572, respectively). Image-level fusion model also outperformed feature-level fusion models ConFea and AvgFea (C-indices = 0.581, 0.641, respectively). Our results demonstrate that multimodal radiomics models especially models fused at image-level have the potential to improve prognosis by combining information from different tumor characteristics, including anatomical and metabolic captured by different imaging modalities.
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基于多层次PET和CT融合放射组学的非小细胞肺癌患者生存分析
为了提供肿瘤内异质性的全面表征,本研究通过特征和图像级融合,提出了从18F-FDG PET和CT图像中提取的多层次多模态放射学模型。具体来说,我们开发了融合放射学模型来提高非小细胞肺癌患者的总体生存预测。在这项工作中,纳入了一个包括来自两个不同机构的患者的NSCLC数据集(86名患者作为培训,95名患者作为测试)。通过从CT、PET和融合图像中提取225个特征,利用放射组学分析构建单模态和多模态模型,其中融合图像通过3d -小波变换融合(WF)构建。采用两种特征级融合策略:特征拼接(ConFea)和特征平均(AvgFea)建立了两个模型。采用Cox比例风险(Cox PH)回归进行生存分析。利用Spearman相关性作为冗余度量,然后将10个最相关特征(按单变量Cox PH排序)的最佳组合输入多变量Cox模型。此外,从训练队列中获得的中位预后评分被用作测试队列中未受影响的阈值,以将患者分为低危组和高危组。组间差异采用log-rank检验。在所有模型中,WF (C-index=0.708)的指数最高,显著优于CT和PET (C-index分别为0.616、0.572)。图像级融合模型也优于特征级融合模型ConFea和AvgFea (c指数分别为0.581、0.641)。我们的研究结果表明,多模态放射组学模型,特别是在图像水平上融合的模型,通过结合不同肿瘤特征的信息,包括不同成像方式捕获的解剖和代谢信息,有可能改善预后。
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