Background

¹⁸F-FDG positron emission tomography/computed tomography (PET/CT) is a successfully used imaging modality in oncology. The aim of the study was to investigate a connection of epithelial tumour differentiation grade with both semiquantitative and quantitative metabolic PET data focusing on creation of multiparametric model of tumour grade prediction utilising both standardised uptake value-based and texture-based ¹⁸F-FDG PET parameters and to investigate an influence of different image segmentation techniques on these parameters and modelling.

Methods

¹⁸F-FDG PET/CT data from 84 patients with epithelial malignant tumours was retrospectively analysed to create sets of both conventional semiquantitative (based on standardised uptake values), volumetric, and quantitative texture metabolic parameters of primary tumours with four different segmentation techniques.

Results

Most of the calculated volumetric and texture parameters showed to be influenced by segmentation technique. There was no significant difference in values of only three parameters, in all four segmentation methods: homogeneity, energy, and sphericity. Almost every extracted parameter in all segmentation technique subsets showed significant ability to discriminate individual tumour grade versus the subset of remaining two tumour grades. No parameters were able to discriminate all three tumour grades separately simultaneously or without the overlapping of threshold values. Group method of data handling (GMDH) modelling included all the above-mentioned extracted parameters. The highest value to discriminate tumour grade was achieved using ITK-SNAP segmentation, with an accuracy ranging from 91 to 100%.

Conclusions

Multiparametric modelling with GMDH utilising both semiquantitative and quantitative texture metabolic PET parameters seems to be an interesting tool for non-invasive malignant epithelial tumours grade differentiation.

中文翻译：

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多参数定量和质地18F-FDG PET / CT分析用于原发性恶性肿瘤分级鉴别。

背景

¹⁸ F-FDG正电子发射断层扫描/计算机断层扫描（PET / CT）是肿瘤学中成功使用的一种成像方式。这项研究的目的是研究上皮肿瘤分化程度与半定量和定量代谢PET数据之间的联系，重点在于利用标准化摄取值和基于质地的¹⁸ F-FDG PET参数创建肿瘤等级预测的多参数模型并研究不同图像分割技术对这些参数和建模的影响。

方法

回顾性分析了84例上皮性恶性肿瘤患者的¹⁸ F-FDG PET / CT数据，以四种不同的分割技术创建了一组传统的半定量（基于标准化摄取值），原发性肿瘤的体积和定量质地代谢参数。

结果

大部分计算的体积和纹理参数显示受分割技术的影响。在所有四种分割方法中，只有三个参数的值没有显着差异：同质性，能量和球形度。在所有分割技术子集中，几乎每个提取的参数都具有显着的能力来区分单个肿瘤等级与其余两个肿瘤等级的子集。没有参数能够同时或没有阈值重叠的情况下分别区分所有三个肿瘤等级。数据处理（GMDH）建模的分组方法包括所有上述提取的参数。使用ITK-SNAP分割技术可实现最高的区分肿瘤等级的值，其准确度范围为91％至100％。

结论

使用半定量和定量纹理代谢PET参数的GMDH进行多参数建模似乎是用于非侵入性恶性上皮肿瘤等级分化的有趣工具。