Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies.
Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies.
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DOI：
10.34067/kid.0000000000000116
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发表时间：
2023-05-01
期刊：
影响因子：
--
通讯作者：
Barisoni L
中科院分区：
文献类型：
--
作者：
Chen Y;Zee J;Janowczyk AR;Rubin J;Toro P;Lafata KJ;Mariani LH;Holzman LB;Hodgin JB;Madabhushi A;Barisoni L
计算图像分析允许从具有潜在临床相关性的全幻灯片图像中提取新信息。小管周围毛细血管（PTC）密度在间质纤维化和小管萎缩区域降低。肾小球疾病的PTC形状（宽高比）与临床预后相关。应用免疫组织化学技术研究了多种肾脏疾病中小管周围毛细血管（PTC）密度与疾病进展之间的关系。然而，PTC的其他属性，如PTC形状，还没有被探索。计算机视觉技术的最新发展为使用常规染色和整片图像量化PTC属性提供了机会。为了探讨PTC特征与临床预后的关系，我们对来自肾病综合征研究网络数字病理存储库的280例周期性酸-希夫染色肾活检（88例微小病变，109例局灶节段性肾小球硬化，46例膜性肾病，37例IgA肾病）进行了计算分析。将先前验证的深度学习模型应用于皮质ptc的分割。计算每次活检的平均PTC宽高比（PTC长轴比）、大小（每PTC分割的PTC像素）和密度（每单位皮质面积的PTC像素）。Cox比例风险模型用于评估这些PTC参数与结果（40% eGFR下降或肾衰竭）之间的关系。比较间质纤维化和小管萎缩（IFTA）区和无IFTA区皮质PTC特征和间质间隙PTC密度。当归一化PTC纵横比低于0.6，即0.1时，归一化PTC纵横比的增加与疾病进展显著相关，其风险比（95%可信区间）为1.28 (1.04 ~ 1.59)(P = 0.019)，而PTC密度和大小与预后无显著相关性。与非IFTA地区相比，IFTA地区的间隙分数空间PTC密度较低。计算图像分析可以量化肾脏微血管的状态，并发现以前未被识别的临床结果的PTC生物标志物（宽高比）。
Computational image analysis allows for the extraction of new information from whole-slide images with potential clinical relevance. Peritubular capillary (PTC) density is decreased in areas of interstitial fibrosis and tubular atrophy when measured in interstitial fractional space. PTC shape (aspect ratio) is associated with clinical outcome in glomerular diseases. The association between peritubular capillary (PTC) density and disease progression has been studied in a variety of kidney diseases using immunohistochemistry. However, other PTC attributes, such as PTC shape, have not been explored yet. The recent development of computer vision techniques provides the opportunity for the quantification of PTC attributes using conventional stains and whole-slide images. To explore the relationship between PTC characteristics and clinical outcome, n=280 periodic acid–Schiff-stained kidney biopsies (88 minimal change disease, 109 focal segmental glomerulosclerosis, 46 membranous nephropathy, and 37 IgA nephropathy) from the Nephrotic Syndrome Study Network digital pathology repository were computationally analyzed. A previously validated deep learning model was applied to segment cortical PTCs. Average PTC aspect ratio (PTC major to minor axis ratio), size (PTC pixels per PTC segmentation), and density (PTC pixels per unit cortical area) were computed for each biopsy. Cox proportional hazards models were used to assess associations between these PTC parameters and outcome (40% eGFR decline or kidney failure). Cortical PTC characteristics and interstitial fractional space PTC density were compared between areas of interstitial fibrosis and tubular atrophy (IFTA) and areas without IFTA. When normalized PTC aspect ratio was below 0.6, a 0.1, increase in normalized PTC aspect ratio was significantly associated with disease progression, with a hazard ratio (95% confidence interval) of 1.28 (1.04 to 1.59) (P = 0.019), while PTC density and size were not significantly associated with outcome. Interstitial fractional space PTC density was lower in areas of IFTA compared with non-IFTA areas. Computational image analysis enables quantification of the status of the kidney microvasculature and the discovery of a previously unrecognized PTC biomarker (aspect ratio) of clinical outcome.
影响因子：
4.1
作者：
Li X;Sun Q;Zhang M;Xie K;Chen J;Liu Z
通讯作者：
Liu Z