Radiomics is an emerging field of medical image analysis research where quantitative measurements are obtained from radiological images that can be utilized to predict patient outcomes and inform treatment decisions. Cancer patients routinely undergo radiological evaluations when images of various modalities including computed tomography, positron emission tomography, and magnetic resonance images are collected for diagnosis and for evaluation of disease progression. Tumor characteristics, often referred to as measures of tumor heterogeneity, can be computed using these clinical images and used as predictors of disease progression and patient survival. Several approaches for quantifying tumor heterogeneity have been proposed, including intensity histogram‐based measures, shape and volume‐based features, and texture analysis. Taking into account the topology of the tumors we propose a statistical framework for estimating tumor heterogeneity using clustering based on Markov random field theory. We model the voxel intensities using a Gaussian mixture model using a Gibbs prior to incorporate voxel neighborhood information. We propose a novel approach to choosing the number of mixture components. Subsequently, we show that the proposed procedure outperforms the existing approaches when predicting lung cancer survival.

中文翻译：

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肿瘤放射学的肿瘤异质性估计。

Radiomics是医学图像分析研究的新兴领域，其中放射学图像获得了定量测量结果，可用于预测患者预后并为治疗决策提供依据。当收集包括计算机断层扫描，正电子发射断层扫描和磁共振图像在内的各种形式的图像以进行诊断和评估疾病进展时，癌症患者通常会接受放射学评估。肿瘤特征，通常被称为肿瘤异质性的量度可以使用这些临床图像进行计算，并用作疾病进展和患者生存的预测指标。已经提出了几种量化肿瘤异质性的方法，包括基于强度直方图的度量，基于形状和体积的特征以及纹理分析。考虑到肿瘤的拓扑结构，我们提出了一个基于马尔可夫随机场理论的聚类估计肿瘤异质性的统计框架。在合并体素邻域信息之前，我们使用Gibbs使用高斯混合模型对体素强度进行建模。我们提出了一种新颖的方法来选择混合组分的数量。随后，我们表明，在预测肺癌存活率时，所提出的程序优于现有方法。