Grupo de Xenética e Bioloxía do Desenvolvemento das Enfermidades Renais, Laboratorio de Nefroloxía (No. 11), Instituto de Investigación Sanitaria de Santiago (IDIS), Complexo Hospitalario de Santiago de Compostela (CHUS), Santiago de Compostela, Spain.
Grupo de Medicina Xenómica, Complexo Hospitalario de Santiago de Compostela (CHUS), Santiago de Compostela, Spain.
PLoS Comput Biol. 2020 Oct 22;16(10):e1008337. doi: 10.1371/journal.pcbi.1008337. eCollection 2020 Oct.
The Polycystic Kidney Disease (PKD) is characterized by progressive renal cyst development and other extrarenal manifestation including Polycystic Liver Disease (PLD). Phenotypical characterization of animal models mimicking human diseases are commonly used, in order to, study new molecular mechanisms and identify new therapeutic approaches. The main biomarker of disease progression is total volume of kidney and liver in both human and mouse, which correlates with organ function. For this reason, the estimation of the number and area of the tissue occupied by cysts, is critical for the understanding of physiological mechanisms underlying the disease. In this regard, cystic index is a robust parameter commonly used to quantify the severity of the disease. To date, the vast majority of biomedical researchers use ImageJ as a software tool to estimate the cystic index by quantifying the cystic areas of histological images after thresholding. This tool has imitations of being inaccurate, largely due to incorrectly identifying non-cystic regions. We have developed a new software, named CystAnalyser (register by Universidade de Santiago de Compostela-USC, and Fundación Investigación Sanitaria de Santiago-FIDIS), that combines automatic image processing with a graphical user friendly interface that allows investigators to oversee and easily correct the image processing before quantification. CystAnalyser was able to generate a cystic profile including cystic index, number of cysts and cyst size. In order to test the CystAnalyser software, 795 cystic kidney, and liver histological images were analyzed. Using CystAnalyser there were no differences calculating cystic index automatically versus user input, except in specific circumstances where it was necessary for the user to distinguish between mildly cystic from non-cystic regions. The sensitivity and specificity of the number of cysts detected by the automatic quantification depends on the type of organ and cystic severity, with values 76.84-78.59% and 76.96-89.66% for the kidney and 87.29-93.80% and 63.42-86.07% for the liver. CystAnalyser, in addition, provides a new tool for estimating the number of cysts and a more specific measure of the cystic index than ImageJ. This study proposes CystAnalyser is a new robust and freely downloadable software tool for analyzing the severity of disease by quantifying histological images of cystic organs for routine biomedical research. CystAnalyser can be downloaded from https://citius.usc.es/transferencia/software/cystanalyser (for Windows and Linux) for research purposes.
多囊肾病（PKD）的特征是进行性肾囊肿发育和其他肾外表现，包括多囊肝病（PLD）。为了研究新的分子机制和确定新的治疗方法，通常使用模拟人类疾病的动物模型进行表型特征描述。疾病进展的主要生物标志物是人类和小鼠的肾脏和肝脏的总容积，这与器官功能相关。因此，估计组织中囊肿的数量和面积对于理解疾病的生理机制至关重要。在这方面，囊肿指数是一种常用的稳健参数，用于量化疾病的严重程度。迄今为止，绝大多数生物医学研究人员使用 ImageJ 作为软件工具，通过对组织学图像进行阈值处理来量化囊肿面积，从而估计囊肿指数。该工具存在不准确的局限性，主要是因为它不能正确识别非囊肿区域。我们开发了一种新软件，命名为 CystAnalyser（由圣地亚哥德孔波斯特拉大学-USC 和圣地亚哥卫生研究基金会-FIDIS 注册），它将自动图像处理与图形用户友好界面相结合，允许研究人员在量化之前监督和轻松纠正图像处理。CystAnalyser 能够生成包括囊肿指数、囊肿数量和囊肿大小的囊肿特征。为了测试 CystAnalyser 软件，分析了 795 个囊性肾脏和肝脏组织学图像。使用 CystAnalyser 自动计算和用户输入计算囊肿指数没有差异，除非在需要用户区分轻度囊性和非囊性区域的特定情况下。自动定量检测到的囊肿数量的灵敏度和特异性取决于器官类型和囊肿严重程度，肾脏为 76.84-78.59%和 76.96-89.66%，肝脏为 87.29-93.80%和 63.42-86.07%。CystAnalyser 还为估计囊肿数量和比 ImageJ 更具体的囊肿指数测量提供了一种新工具。这项研究提出 CystAnalyser 是一种新的强大且可免费下载的软件工具，可用于通过量化囊性器官的组织学图像来分析疾病的严重程度，适用于常规生物医学研究。CystAnalyser 可从 https://citius.usc.es/transferencia/software/cystanalyser（适用于 Windows 和 Linux）下载，仅供研究使用。
