Ruusuvuori Pekka, Valkonen Masi, Kartasalo Kimmo, Valkonen Mira, Visakorpi Tapio, Nykter Matti, Latonen Leena
Institute of Biomedicine, University of Turku, Turku, Finland.
Faculty of Medicine and Health Technology, Tampere University, Finland.
Heliyon. 2022 Jan 14;8(1):e08762. doi: 10.1016/j.heliyon.2022.e08762. eCollection 2022 Jan.
Histological changes in tissue are of primary importance in pathological research and diagnosis. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue. Conventional histopathological assessments are performed from individual tissue sections, leading to the loss of three-dimensional context of the tissue. Yet, the tissue context and spatial determinants are critical in several pathologies, such as in understanding growth patterns of cancer in its local environment. Here, we develop computational methods for visualization and quantitative assessment of histopathological alterations in three dimensions. First, we reconstruct the 3D representation of the whole organ from serial sectioned tissue. Then, we proceed to analyze the histological characteristics and regions of interest in 3D. As our example cases, we use whole slide images representing hematoxylin-eosin stained whole mouse prostates in a mouse prostate tumor model. We show that quantitative assessment of tumor sizes, shapes, and separation between spatial locations within the organ enable characterizing and grouping tumors. Further, we show that 3D visualization of tissue with computationally quantified features provides an intuitive way to observe tissue pathology. Our results underline the heterogeneity in composition and cellular organization within individual tumors. As an example, we show how prostate tumors have nuclear density gradients indicating areas of tumor growth directions and reflecting varying pressure from the surrounding tissue. The methods presented here are applicable to any tissue and different types of pathologies. This work provides a proof-of-principle for gaining a comprehensive view from histology by studying it quantitatively in 3D.
组织学变化在病理学研究和诊断中至关重要。自动化组织学分析需要具备从计算上区分病理改变与正常组织的能力。传统的组织病理学评估是从单个组织切片进行的，这导致组织三维背景信息的丢失。然而，组织背景和空间决定因素在多种病理学中至关重要，比如在理解癌症在其局部环境中的生长模式时。在此，我们开发了用于三维可视化和定量评估组织病理学改变的计算方法。首先，我们从连续切片的组织重建整个器官的三维表示。然后，我们着手在三维空间中分析组织学特征和感兴趣区域。作为示例案例，我们使用代表小鼠前列腺肿瘤模型中苏木精 - 伊红染色的整个小鼠前列腺的全切片图像。我们表明，对肿瘤大小、形状以及器官内空间位置之间的间隔进行定量评估能够对肿瘤进行特征描述和分组。此外，我们表明具有计算量化特征的组织三维可视化提供了一种直观观察组织病理学的方法。我们的结果强调了单个肿瘤内成分和细胞组织的异质性。例如，我们展示了前列腺肿瘤如何具有核密度梯度，这表明肿瘤生长方向区域并反映来自周围组织的不同压力。这里介绍的方法适用于任何组织和不同类型的病理学。这项工作通过在三维空间中对组织学进行定量研究，为全面了解组织学提供了原理证明。
