Cancers can manifest large variations in tumor phenotypes due to genetic and microenvironmental factors, which has motivated the development of quantitative radiomics-based image analysis with the aim to robustly classify tumor phenotypes in vivo. Positron emission tomography (PET) imaging can be particularly helpful in elucidating the metabolic profiles of tumors. However, the relatively low resolution, high noise, and limited PET data availability make it difficult to study the relationship between the microenvironment properties and metabolic tumor phenotype as seen on the images. Most of previously proposed digital PET phantoms of tumors are static, have an over-simplified morphology, and lack the link to cellular biology that ultimately governs the tumor evolution. In this work, we propose a novel method to investigate the relationship between microscopic tumor parameters and PET image characteristics based on the computational simulation of tumor growth. We use a hybrid, multiscale, stochastic mathematical model of cellular metabolism and proliferation to generate simulated cross-sections of tumors in vascularized normal tissue on a microscopic level. The generated longitudinal tumor growth sequences are converted to PET images with realistic resolution and noise. By changing the biological parameters of the model, such as the blood vessel density and conditions for necrosis, distinct tumor phenotypes can be obtained. The simulated cellular maps were compared to real histology slides of SiHa and WiDr xenografts imaged with Hoechst 33342 and pimonidazole. As an example application of the proposed method, we simulated six tumor phenotypes that contain various amounts of hypoxic and necrotic regions induced by a lack of oxygen and glucose, including phenotypes that are distinct on the microscopic level but visually similar in PET images. We computed 22 standardized Haralick texture features for each phenotype, and identified the features that could best discriminate the phenotypes with varying image noise levels. We demonstrated that “cluster shade” and “difference entropy” are the most effective and noise-resilient features for microscopic phenotype discrimination. Longitudinal analysis of the simulated tumor growth showed that radiomics analysis can be beneficial even in small lesions with a diameter of 3.5–4 resolution units, corresponding to 8.7–10.0 mm in modern PET scanners. Certain radiomics features were shown to change non-monotonically with tumor growth, which has implications for feature selection for tracking disease progression and therapy response.
癌症因遗传和微环境因素可表现出显著的肿瘤表型变异，这推动了基于定量放射组学的图像分析技术的发展，旨在实现对体内肿瘤表型的稳健分类。正电子发射断层扫描（PET）成像在阐明肿瘤代谢特征方面尤为有效。然而，相对较低的分辨率、高噪声以及有限的PET数据可用性，使得研究图像中观察到的微环境特性与代谢肿瘤表型之间的关系变得困难。先前提出的大多数数字肿瘤PET模型是静态的，形态过于简化，且缺乏与最终调控肿瘤演化的细胞生物学的联系。本研究提出了一种基于肿瘤生长计算模拟的新方法，用于探究微观肿瘤参数与PET图像特征之间的关系。我们采用一种混合、多尺度、随机的细胞代谢与增殖数学模型，在微观水平上生成血管化正常组织中肿瘤的模拟横截面。生成的纵向肿瘤生长序列被转换为具有真实分辨率和噪声的PET图像。通过改变模型的生物学参数，如血管密度和坏死条件，可以获得不同的肿瘤表型。模拟的细胞图谱与使用Hoechst 33342和哌莫硝唑成像的SiHa和WiDr异种移植物的真实组织学切片进行了比较。作为该方法的应用示例，我们模拟了六种肿瘤表型，这些表型包含因缺氧和葡萄糖缺乏诱导的不同程度的缺氧和坏死区域，包括在微观水平上不同但在PET图像中视觉上相似的表型。我们为每种表型计算了22个标准化的Haralick纹理特征，并确定了在不同图像噪声水平下最能区分表型的特征。我们证明“聚类阴影”和“差异熵”是微观表型区分中最有效且对噪声最具鲁棒性的特征。对模拟肿瘤生长的纵向分析表明，放射组学分析即使在直径为3.5-4个分辨率单位（对应于现代PET扫描仪中的8.7-10.0毫米）的小病灶中也可能有益。某些放射组学特征被证明随肿瘤生长呈非单调变化，这对追踪疾病进展和治疗反应的特征选择具有启示意义。
肿瘤(癌症)患者之家