Background

Radiomic features, extracted from positron emission tomography, aim to characterize tumour biology based on tracer intensity, tumour geometry and/or tracer uptake heterogeneity. Currently, radiomic features are derived from static images. However, temporal changes in tracer uptake might reveal new aspects of tumour biology. This study aims to explore additional information of these novel dynamic radiomic features compared to those derived from static or metabolic rate images.

Methods

Thirty-five patients with non-small cell lung carcinoma underwent dynamic [¹⁸F]FDG PET/CT scans. Spatial intensity, shape and texture radiomic features were derived from volumes of interest delineated on static PET and parametric metabolic rate PET. Dynamic grey level cooccurrence matrix (GLCM) and grey level run length matrix (GLRLM) features, assessing the temporal domain unidirectionally, were calculated on eight and sixteen time frames of equal length. Spearman’s rank correlations of parametric and dynamic features with static features were calculated to identify features with potential additional information. Survival analysis was performed for the non-redundant temporal features and a selection of static features using Kaplan-Meier analysis.

Results

Three out of 90 parametric features showed moderate correlations with corresponding static features (ρ≥0.61), all other features showed high correlations (ρ>0.7). Dynamic features are robust independent of frame duration. Five out of 22 dynamic GLCM features showed a negligible to moderate correlation with any static feature, suggesting additional information. All sixteen dynamic GLRLM features showed high correlations with static features, implying redundancy. Log-rank analyses of Kaplan-Meier survival curves for all features dichotomised at the median were insignificant.

Conclusion

This study suggests that, compared to static features, some dynamic GLCM radiomic features show different information, whereas parametric features provide minimal additional information. Future studies should be conducted in larger populations to assess whether there is a clinical benefit of radiomics using the temporal domain over traditional radiomics.

中文翻译：

---

将时域添加到PET放射特征中。

背景

从正电子发射断层扫描中提取的放射学特征旨在基于示踪剂强度，肿瘤几何形状和/或示踪剂摄取异质性表征肿瘤生物学。当前，放射学特征源自静态图像。但是，示踪剂摄取的时间变化可能揭示了肿瘤生物学的新方面。这项研究旨在探索这些新颖的动态放射学特征与静态或代谢率图像衍生的特征相比的其他信息。

方法

35例非小细胞肺癌患者接受了动态[ ¹⁸ F] FDG PET / CT扫描。空间强度，形状和质地的放射特征来自静态PET和参数代谢率PET上描述的目标体积。动态灰度共生矩阵（GLCM）和灰度游程长度矩阵（GLRLM）功能，在相同长度的八个和十六个时间帧上，单向评估时域。计算参数和动态要素与静态要素的Spearman等级相关性，以识别具有潜在附加信息的要素。对非冗余时态特征和静态选择进行生存分析 使用Kaplan-Meier分析的特征。

结果

在90个参数特征中，有3个与相应的静态特征具有中等相关性（ρ≥0.61），所有其他特征都具有较高的相关性（ρ> 0.7）。动态功能具有鲁棒性，与帧持续时间无关。22个动态GLCM功能中有5个显示与任何静态功能的中度相关性可忽略不计，这提示了其他信息。所有16个动态GLRLM功能均显示与静态功能的高度相关性，这意味着冗余。在中位数二等分的所有特征的Kaplan-Meier生存曲线的对数秩分析微不足道。

结论

这项研究表明，与静态特征相比，某些动态GLCM放射特征显示出不同的信息，而参数特征提供的附加信息最少。未来的研究应该在更大的人群中进行，以评估使用时域的放射性药物相对于传统放射性药物是否具有临床益处。