Deep learning and topological approaches to identify kidney tissue features associated with adverse outcomes after nephrectomy

摘要 肾活检组织的病理评估仍然是预测慢性肾功能衰竭患者不良结局的最佳指标 肾脏疾病。这些特征在很大程度上与疾病病因无关，在非 侵入性检查(如血清肌酐和蛋白尿)。这些参数的定量评估是时间 肾组织内病理特征的异质性消耗并可能存在缺陷。我们建议 评估和优化支持大块切片病理分析的计算图像分析方法 我们收集的肾切除患者的无癌肾组织标本(n&gt；220)。 对这些样本中肾小球表型的计算机辅助分析表明， 无明显病理改变的肾小球先于已有的病理改变。我们假设这一评估 无癌肾组织的情况将提示剩余肾脏的亚临床损害。 以及相关的病理和临床参数。我们建议评估肾小球、动脉和肾小管，以及 确定所评估的特征在肾组织内的空间相互关系。 对比针刺活组织检查大得多的肾组织的检查允许 包括20倍以上的肾小球(肾切除样本：AVRG。肾小球256个/标本；针刺活检：AVRG。 13个/样本)，根据标准病理标准，绝大多数被认为是“正常外观”。在……里面 此外，这些样本包括明显更多的血管(肾切除样本：AVRG。18 动脉/样本；针刺活检：AVRG。1个/样本)，允许对血管系统进行更可靠的评估。我们 建议应用和优化我们的检测和分割方法来检测肾小球、动脉和 管状分段来训练卷积神经网络，并使用拓扑图像分析来自动化 识别视觉和亚视觉特征。此外，我们还将评估空间关系 单个特征(肾小球、动脉和肾小管段)以及同一类别的特征，即全局特征 肾小球硬化、动脉透明质变、肾小管萎缩)。确定…的重现性 我们的方法，我们将评估来自同一样本的单独部分的第二个组织切片。具体来说， 我们提出了一种基于深度学习的肾脏特征检测和表征算法，它是一种拓扑结构 图像分析用于在肾组织图像中发现新的亚视觉特征并确定空间 这些特征的关联性。 如果成功，我们将在未来的独立研究中验证我们的分析方法。为此，我们将 已经前瞻性地收集了同意接受手术的患者的肾组织和纵向临床数据 肾切除术，允许将特定特征与临床相关结果联系起来。

ABSTRACT Pathologic assessment of kidney biopsy tissue remains the best predictor of adverse outcomes in patients with kidney diseases. These features are largely independent of disease etiology and are not well reflected in non- invasive tests (e.g. serum creatinine and albuminuria). Quantitative assessment of these parameters is time consuming and maybe flawed by heterogeneity of pathologic features within kidney tissue. We propose to evaluate and optimize computational image analysis approaches to support pathologic analysis of large pieces of cancer-free kidney tissue from patients who underwent nephrectomy which we have collected (n > 220). Computer-assisted analysis of glomerular phenotypes in these samples show that morphometric features in glomeruli without obvious pathology precede established pathologic changes. We hypothesize that evaluation of cancer-free kidney tissue will inform about subclinical damage in the remaining kidney which is associated with relevant pathologic and clinical parameters. We propose to assess glomeruli, arteries and tubuli, and determine the spatial inter-relationship of the assessed features within the kidney tissue. The examination of significantly larger pieces of kidney tissue than those obtained by needle biopsy allows to include 20 times more glomeruli (nephrectomy samples: avrg. 256 glomeruli/sample; needle biopsy: avrg. 13/sample) with the vast majority considered “normal appearing” as per standard pathologic criteria. In addition, these samples include a significant larger number of blood vessels (nephrectomy samples: avrg. 18 arteries/sample; needle biopsy: avrg. 1/sample) allowing a more robust evaluation of the vasculature. We propose to apply and optimize our detection and segmentation approach to detect glomeruli, arteries and tubular segments to train convolutional neural networks and use topological image analysis to automate the identification of visual and sub-visual features. In addition, we will assess the spatial relationship between individual features (glomeruli, arteries and tubular segments and features of the same category, i.e. globally sclerosed glomeruli, arteries with hyalinosis, atrophied tubuli) within the section. To determine reproducibility of our approach, we will assess a second tissue section from a separate part of the same samples. Specifically, we propose an algorithmic detection and characterization of kidney features using deep learning, a topological image analysis for discovery of novel sub-visual features in kidney tissue images and to determine spatial relatedness of these features. If successful, we will validate our analytical approach in future independent studies. For this purpose, we are already prospectively collecting kidney tissue and longitudinal clinical data from consented patients undergoing nephrectomies, allowing association of specific features with clinical relevant outcomes.