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首页> 外文会议>IEEE Workshop on complexity in engineering >Exposing Cancer's Complexity Using Radiomics in Clinical Imaging An Investigation on the Role of Histogram Analysis as Imaging Biomarker to Unravel Intra-Tumour Heterogeneity
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Exposing Cancer's Complexity Using Radiomics in Clinical Imaging An Investigation on the Role of Histogram Analysis as Imaging Biomarker to Unravel Intra-Tumour Heterogeneity

机译：在临床影像学中使用放射组学揭示癌症的复杂性对直方图分析作为影像学生物标志物揭示肿瘤内异质性的作用的研究

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Thanks to the most advanced investigation techniques, cancer is showing to be something more complex than we ever imagined. Genomic pattern, epigenetic modifications, environmental and life-style influences leads to subjective expression of the disease. In addition, cancer can be extremely heterogeneous intrinsically, and does not stand still but changes over time. These hallmarks can explain how cancer adapts to therapies, evolving to something than can be totally different from the beginning of the disease. It's an expression of Darwin evolution. Spatial heterogeneity can be found among different tumors and within each lesion, which manifests at genomic, phenotypic, and physiologic levels. Today we know that heterogeneity is a hallmark of malignant tumors. Usually intratumor heterogeneity tends to increase as tumors grow and may increase or decrease following the response to the therapy. This means that tumor heterogeneity must be explored as prognostic tool, but how do we measure this heterogeneity? Clinical imaging allows to quantify this heterogeneity thanks to Radiomics, which extracts quantitative features from images (especially from computed tomography [CT], magnetic resonance [MR], and positron emission tomography [PET] images). The link of these imaging parameters to different phenotypes or genotypes enables the mapping of biologic heterogeneity of tumors, from which inference on gene expression, signaling pathway activity, and tumor microenvironment features can be obtained. These features have the potentiality to become a powerful tool to unravel tumor, providing quantitative information that allows a better phenotypization. In this work we want to show how a subset of radiomic features connected to histogram analysis, in particular skewness, kurtosis and Shannon entropy, evaluated in images of patients with different kinds of cancer, show diagnostic power to differentiate healthy from ill tissues. We will conclude introducing problems linked to the lack of connection between the complexity estimated with radiomics and the underlying biological model.
机译：得益于最先进的调查技术，癌症显示出比我们想象的还要复杂的东西。基因组模式，表观遗传修饰，环境和生活方式的影响导致疾病的主观表达。此外，癌症在本质上可能极为不同，并且不会停滞不前，而是会随着时间而变化。这些标志可以解释癌症如何适应疗法，演变成与疾病开始时完全不同的事物。这是达尔文进化的一种表达。空间异质性可以在不同的肿瘤之间和每个病变内发现，表现在基因组，表型和生理水平。今天，我们知道异质性是恶性肿瘤的标志。通常，肿瘤内异质性倾向于随着肿瘤的生长而增加，并可能在对治疗产生反应后增加或减少。这意味着必须将肿瘤的异质性作为预后工具，但是我们如何测量这种异质性呢？临床影像技术可以通过Radiomics量化这种异质性，Radimics可以从图像（特别是从计算机断层扫描[CT]，磁共振[MR]和正电子发射断层扫描[PET]图像）中提取定量特征。这些成像参数与不同表型或基因型的联系使得能够绘制出肿瘤的生物学异质性，从而可以推断出基因表达，信号通路活性和肿瘤微环境特征。这些功能具有成为揭开肿瘤的强大工具的潜力，可提供定量信息以更好地表型化。在这项工作中，我们想展示与直方图分析相关的放射特征的子集，特别是偏斜度，峰度和香农熵，如何在患有不同类型癌症的患者的图像中进行评估，从而显示出将健康与疾病组织区分开的诊断能力。我们将得出结论，介绍与放射线学估计的复杂性与基础生物学模型之间缺乏联系有关的问题。

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