DataSheet_1_The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study.pdf

Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman’s correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as “virtual biopsy”, introducing new insights for omics integration toward a personalized medicine approach.

多形性胶质母细胞瘤（Glioblastoma multiforme, GBM）在微观与影像学分辨率尺度下通常展现出显著的瘤内异质性。弥散加权成像（Diffusion Weighted Imaging, DWI）与动态对比增强磁共振成像（dynamic contrast-enhanced magnetic resonance imaging, DCE MRI）是临床中常用于评估GBM肿瘤特征的两类功能磁共振成像技术。本研究旨在探究从术前表观扩散系数（apparent diffusion coefficient, ADC）图与增强后T1（post-contrast T1, T1C）图像中提取的放射组学特征，是否与数字化苏木精-伊红（hematoxylin-eosin, H&E）病理图像所获得的病理组学特征存在关联，并报告了相关初步研究结果。本研究纳入了公开可用的CPTAC-GBM数据库中的48例患者，所有患者均同时具备影像学与病理图像数据。研究团队借助PyRadiomics工具，从ADC图与增强后T1图像中提取了91项放射组学特征；从H&E图像的细胞检测结果中提取了65项病理组学特征；此外，还从四种不同分辨率的H&E图像细胞密度图中提取了91项特征。本研究通过斯皮尔曼相关系数（Spearman’s correlation, ρ）与因子分析评估了放射病理组学关联，并采用错误发现率校正（false discovery rate adjustment）对多重相关的P值进行调整。研究发现，病理组学与ADC成像之间存在显著的跨尺度关联：在特征层面（共186项，|ρ|介于0.45至0.74之间）与因子层面（共5项，|ρ|介于0.48至0.54之间）均观测到此类关联。针对病理组学与放射组学特征的关联分析，则仅发现了较少数量的显著相关结果：特征层面共53项，|ρ|介于0.5至0.65之间；因子层面共2项，|ρ|分别为0.63与0.53。本研究结果表明，数字化病理成像与ADC、T1C成像之间可能存在跨尺度关联。这一发现不仅有助于加深对GBM瘤内异质性的认知，还能强化放射组学方法作为“虚拟活检”在临床实践中的应用价值与验证效果，为组学整合以实现个性化医疗提供新的研究思路。