Henry H L Wu, Yandong Lang, Shannon Handley, Aline Knab, Adnan Agha, Yuan Tian, Akanksha Bhargava, Ewa M Goldys, Carol A Pollock, Sonia Saad
{"title":"无创评估尿近端小管细胞脱落的多光谱自体荧光可以区分肾移植功能障碍的原因。","authors":"Henry H L Wu, Yandong Lang, Shannon Handley, Aline Knab, Adnan Agha, Yuan Tian, Akanksha Bhargava, Ewa M Goldys, Carol A Pollock, Sonia Saad","doi":"10.34067/KID.0000000879","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Complications relating to delayed or deteriorating graft function following kidney transplantation are common. There is no validated method apart from transplant kidney biopsy which can accurately identify between the histopathological causes of graft dysfunction. Considering an unmet critical need for a non-invasive approach to reliably diagnose kidney transplant complications, this work proposes a novel methodology based on the assessment of exfoliated proximal tubule cells (PTCs) extracted from urine of kidney transplant recipients by using their multispectral autofluorescence features.</p><p><strong>Methods: </strong>Three groups of 10 patients who have undergone clinically indicated transplant kidney biopsy and was subsequently diagnosed with either acute tubular necrosis (ATN), graft rejection or non-rejection associated interstitial fibrosis and tubular atrophy (IFTA) took part in this study. Exfoliated PTCs from urine collected prior to transplant biopsy were extracted using a validated immunomagnetic separation method based on anti-CD13 and anti-SGLT2 antibodies. Imaging was performed on a custom-made multispectral autofluorescence microscopy and camera system. Multispectral autofluorescence images of PTCs were quantitatively analysed by using optimised small sets of features to prevent overfitting. Binary classification was carried out by a random forest classifier, and the AutoGluon machine learning software. Results were validated by 5-fold cross validation.</p><p><strong>Results: </strong>For random forest classification, features were selected using entropy-based feature selection, resulting in AUC values of 0.92 (ATN versus graft rejection), 0.86 (ATN versus IFTA) and 0.62 (graft rejection versus IFTA) respectively. The AutoGluon classifier optimisation for the same features resulted in AUC values of 0.95 (ATN versus graft rejection), 0.92 (ATN versus IFTA) and 0.91 (graft rejection vs IFTA).</p><p><strong>Conclusions: </strong>Our results demonstrate a proof-of-concept that measurement of autofluorescent features from urinary exfoliated PTCs multispectral autofluorescence could differentiate between patient groups with ATN, graft rejection and IFTA in kidney transplant recipients to an excellent degree of accuracy using AutoGluon classifier optimisation.</p>","PeriodicalId":17882,"journal":{"name":"Kidney360","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Non-invasive Assessment of Urinary Exfoliated Proximal Tubule Cell Multispectral Autofluorescence May Differentiate Between Causes of Kidney Transplant Dysfunction.\",\"authors\":\"Henry H L Wu, Yandong Lang, Shannon Handley, Aline Knab, Adnan Agha, Yuan Tian, Akanksha Bhargava, Ewa M Goldys, Carol A Pollock, Sonia Saad\",\"doi\":\"10.34067/KID.0000000879\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Complications relating to delayed or deteriorating graft function following kidney transplantation are common. There is no validated method apart from transplant kidney biopsy which can accurately identify between the histopathological causes of graft dysfunction. Considering an unmet critical need for a non-invasive approach to reliably diagnose kidney transplant complications, this work proposes a novel methodology based on the assessment of exfoliated proximal tubule cells (PTCs) extracted from urine of kidney transplant recipients by using their multispectral autofluorescence features.</p><p><strong>Methods: </strong>Three groups of 10 patients who have undergone clinically indicated transplant kidney biopsy and was subsequently diagnosed with either acute tubular necrosis (ATN), graft rejection or non-rejection associated interstitial fibrosis and tubular atrophy (IFTA) took part in this study. Exfoliated PTCs from urine collected prior to transplant biopsy were extracted using a validated immunomagnetic separation method based on anti-CD13 and anti-SGLT2 antibodies. Imaging was performed on a custom-made multispectral autofluorescence microscopy and camera system. Multispectral autofluorescence images of PTCs were quantitatively analysed by using optimised small sets of features to prevent overfitting. Binary classification was carried out by a random forest classifier, and the AutoGluon machine learning software. Results were validated by 5-fold cross validation.</p><p><strong>Results: </strong>For random forest classification, features were selected using entropy-based feature selection, resulting in AUC values of 0.92 (ATN versus graft rejection), 0.86 (ATN versus IFTA) and 0.62 (graft rejection versus IFTA) respectively. The AutoGluon classifier optimisation for the same features resulted in AUC values of 0.95 (ATN versus graft rejection), 0.92 (ATN versus IFTA) and 0.91 (graft rejection vs IFTA).</p><p><strong>Conclusions: </strong>Our results demonstrate a proof-of-concept that measurement of autofluorescent features from urinary exfoliated PTCs multispectral autofluorescence could differentiate between patient groups with ATN, graft rejection and IFTA in kidney transplant recipients to an excellent degree of accuracy using AutoGluon classifier optimisation.</p>\",\"PeriodicalId\":17882,\"journal\":{\"name\":\"Kidney360\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2025-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Kidney360\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.34067/KID.0000000879\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"UROLOGY & NEPHROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Kidney360","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34067/KID.0000000879","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"UROLOGY & NEPHROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景:肾移植术后延迟或移植物功能恶化的并发症是常见的。除了移植肾活检外,没有一种有效的方法可以准确地识别移植肾功能障碍的组织病理学原因。考虑到对可靠诊断肾移植并发症的非侵入性方法的未满足的关键需求,本研究提出了一种基于评估肾移植受者尿液中提取的脱落近端小管细胞(ptc)的新方法,该方法利用其多光谱自身荧光特征。方法:三组10例接受临床指示的移植肾活检并随后诊断为急性肾小管坏死(ATN)、移植排斥或非排斥相关性间质纤维化和肾小管萎缩(IFTA)的患者参加了本研究。采用基于抗cd13和抗sglt2抗体的有效免疫磁分离方法,从移植活检前收集的尿液中提取剥离的ptc。在定制的多光谱自动荧光显微镜和相机系统上进行成像。通过使用优化的小特征集来定量分析ptc的多光谱自荧光图像,以防止过拟合。采用随机森林分类器和AutoGluon机器学习软件进行二值分类。结果经5倍交叉验证。结果:对于随机森林分类,使用基于熵的特征选择来选择特征,得到AUC值分别为0.92 (ATN vs .移植排斥)、0.86 (ATN vs . IFTA)和0.62(移植排斥vs . IFTA)。AutoGluon分类器优化相同的特征导致AUC值为0.95 (ATN与移植排斥),0.92 (ATN与IFTA)和0.91(移植排斥与IFTA)。结论:我们的研究结果证明了一个概念验证,即使用AutoGluon分类器优化,测量尿脱落ptc多光谱自身荧光特征可以区分肾移植受者的ATN、移植排斥和IFTA患者组,准确度很高。
Non-invasive Assessment of Urinary Exfoliated Proximal Tubule Cell Multispectral Autofluorescence May Differentiate Between Causes of Kidney Transplant Dysfunction.
Background: Complications relating to delayed or deteriorating graft function following kidney transplantation are common. There is no validated method apart from transplant kidney biopsy which can accurately identify between the histopathological causes of graft dysfunction. Considering an unmet critical need for a non-invasive approach to reliably diagnose kidney transplant complications, this work proposes a novel methodology based on the assessment of exfoliated proximal tubule cells (PTCs) extracted from urine of kidney transplant recipients by using their multispectral autofluorescence features.
Methods: Three groups of 10 patients who have undergone clinically indicated transplant kidney biopsy and was subsequently diagnosed with either acute tubular necrosis (ATN), graft rejection or non-rejection associated interstitial fibrosis and tubular atrophy (IFTA) took part in this study. Exfoliated PTCs from urine collected prior to transplant biopsy were extracted using a validated immunomagnetic separation method based on anti-CD13 and anti-SGLT2 antibodies. Imaging was performed on a custom-made multispectral autofluorescence microscopy and camera system. Multispectral autofluorescence images of PTCs were quantitatively analysed by using optimised small sets of features to prevent overfitting. Binary classification was carried out by a random forest classifier, and the AutoGluon machine learning software. Results were validated by 5-fold cross validation.
Results: For random forest classification, features were selected using entropy-based feature selection, resulting in AUC values of 0.92 (ATN versus graft rejection), 0.86 (ATN versus IFTA) and 0.62 (graft rejection versus IFTA) respectively. The AutoGluon classifier optimisation for the same features resulted in AUC values of 0.95 (ATN versus graft rejection), 0.92 (ATN versus IFTA) and 0.91 (graft rejection vs IFTA).
Conclusions: Our results demonstrate a proof-of-concept that measurement of autofluorescent features from urinary exfoliated PTCs multispectral autofluorescence could differentiate between patient groups with ATN, graft rejection and IFTA in kidney transplant recipients to an excellent degree of accuracy using AutoGluon classifier optimisation.