Padova Neuroscience Center, University of Padova, Padova, Italy; Department of Information Engineering, University of Padova, Padova, Italy.
Department of Computer Science, University of Verona, Italy.
Neuroimage Clin. 2022;34:102968. doi: 10.1016/j.nicl.2022.102968. Epub 2022 Feb 18.
Diffusion-based biophysical models have been used in several recent works to study the microenvironment of brain tumours. While the pathophysiological interpretation of the parameters of these models remains unclear, their use as signal representations may yield useful biomarkers for monitoring the treatment and the progression of this complex and heterogeneous disease. Up to now, however, no study was devoted to assessing the mathematical stability of these approaches in cancerous brain regions. To this end, we analyzed in 11 brain tumour patients the fitting results of two microstructure models (Neurite Orientation Dispersion and Density Imaging and the Spherical Mean Technique) and of a signal representation (Diffusion Kurtosis Imaging) to compare the reliability of their parameter estimates in the healthy brain and in the tumoral lesion. The framework of our between-tissue analysis included the computation of 1) the residual sum of squares as a goodness-of-fit measure 2) the standard deviation of the models' derived metrics and 3) models' sensitivity functions to analyze the suitability of the employed protocol for parameter estimation in the different microenvironments. Our results revealed no issues concerning the fitting of the models in the tumoral lesion, with similar goodness of fit and parameter precisions occurring in normal appearing and pathological tissues. Lastly, with the aim of highlight possible biomarkers, in our analysis we briefly discuss the correlation between the metrics of the three techniques, identifying groups of indices which are significantly collinear in all tissues and thus provide no additional information when jointly used in data-driven analyses.
基于扩散的生物物理模型已在最近的几项研究中用于研究脑肿瘤的微环境。虽然这些模型的参数的病理生理学解释尚不清楚，但它们作为信号表示的使用可能会为监测这种复杂且异质疾病的治疗和进展提供有用的生物标志物。然而，到目前为止，还没有研究致力于评估这些方法在癌性脑区的数学稳定性。为此，我们在 11 名脑肿瘤患者中分析了两种微结构模型（神经纤维取向弥散和密度成像和球形均值技术）和一种信号表示（扩散峰度成像）的拟合结果，以比较其在健康脑组织和肿瘤病变中的参数估计的可靠性。我们的组织间分析框架包括计算 1）残差平方和作为拟合优度度量 2）模型导出度量的标准偏差和 3）模型的灵敏度函数，以分析所采用的协议在不同微环境中进行参数估计的适用性。我们的结果表明，模型在肿瘤病变中的拟合没有问题，正常和病理组织中出现相似的拟合优度和参数精度。最后，为了突出可能的生物标志物，我们在分析中简要讨论了三种技术的指标之间的相关性，确定了在所有组织中都显著共线性的指数组，因此在数据驱动分析中联合使用时没有提供额外的信息。
