Dataset Information

Computational Segmentation and Classification of Diabetic Glomerulosclerosis.

ABSTRACT: BACKGROUND:Pathologists use visual classification of glomerular lesions to assess samples from patients with diabetic nephropathy (DN). The results may vary among pathologists. Digital algorithms may reduce this variability and provide more consistent image structure interpretation. METHODS:We developed a digital pipeline to classify renal biopsies from patients with DN. We combined traditional image analysis with modern machine learning to efficiently capture important structures, minimize manual effort and supervision, and enforce biologic prior information onto our model. To computationally quantify glomerular structure despite its complexity, we simplified it to three components consisting of nuclei, capillary lumina and Bowman spaces; and Periodic Acid-Schiff positive structures. We detected glomerular boundaries and nuclei from whole slide images using convolutional neural networks, and the remaining glomerular structures using an unsupervised technique developed expressly for this purpose. We defined a set of digital features which quantify the structural progression of DN, and a recurrent network architecture which processes these features into a classification. RESULTS:Our digital classification agreed with a senior pathologist whose classifications were used as ground truth with moderate Cohen's kappa ? = 0.55 and 95% confidence interval [0.50, 0.60]. Two other renal pathologists agreed with the digital classification with ?1 = 0.68, 95% interval [0.50, 0.86] and ?2 = 0.48, 95% interval [0.32, 0.64]. Our results suggest computational approaches are comparable to human visual classification methods, and can offer improved precision in clinical decision workflows. We detected glomerular boundaries from whole slide images with 0.93±0.04 balanced accuracy, glomerular nuclei with 0.94 sensitivity and 0.93 specificity, and glomerular structural components with 0.95 sensitivity and 0.99 specificity. CONCLUSIONS:Computationally derived, histologic image features hold significant diagnostic information that may augment clinical diagnostics.

SUBMITTER: Ginley B

PROVIDER: S-EPMC6779352 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Computational Segmentation and Classification of Diabetic Glomerulosclerosis.

Ginley Brandon B Lutnick Brendon B Jen Kuang-Yu KY Fogo Agnes B AB Jain Sanjay S Rosenberg Avi A Walavalkar Vighnesh V Wilding Gregory G Tomaszewski John E JE Yacoub Rabi R Rossi Giovanni Maria GM Sarder Pinaki P

Journal of the American Society of Nephrology : JASN 20190905 10

<h4>Background</h4>Pathologists use visual classification of glomerular lesions to assess samples from patients with diabetic nephropathy (DN). The results may vary among pathologists. Digital algorithms may reduce this variability and provide more consistent image structure interpretation.<h4>Methods</h4>We developed a digital pipeline to classify renal biopsies from patients with DN. We combined traditional image analysis with modern machine learning to efficiently capture important structures ...[more]

PMID: 31488606

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Project description:BackgroundDiabetic nephropathy (DN) is among the most common microvascular complications of diabetes resulting in end-stage renal disease and therefore search for candidates which can ameliorate the kidney function is needed simultaneously with standard diabetic pharmacotherapy. The current study was aimed to investigate the effect of long term sacubitril/valsartan therapy (LCZ696) in diabetic rats to assess its ameliorative impact against various pathological parameters such as oxidative stress, inflammation and glomerulosclerosis associated with chronic DN.MethodsA single dose (60 mg/kg/day) of STZ was used to induce type 1 diabetes in adult male wistar rats. 2 weeks after diabetes induction, these rats were treated orally with valsartan (31 mg/kg) or LCZ696 (68 mg/kg) for 6 weeks. At end of the treatment period, serum and kidney samples were collected and analyzed. The serum levels of glucose, insulin, urea, creatinine, TNF-?, IL-1?, IL-6 and IL-10 levels were estimated. In renal tissue homogenate, the levels of inflammatory markers such as TNF-?, IL-1?, IL-6, NF-kB along with oxidative stress biomarkers including thiobarbituric acid-reacting substances (TBARs), glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), glutathione S-transferase (GST) were assessed. Histological changes were observed in kidney.ResultsTime course therapy withLCZ696 and valsartan in diabetic rats resulted in significant reduction of serum glucose, urea and creatinine levels (P < 0.05). Additionally, serum of treated diabetic rats showed a diminution in inflammatory (TNF-?, IL-1?, IL-6) and increment in anti-inflammatory (IL-10) cytokines levels (P < 0.05). Tissue homogenate of the kidney extracted from LCZ696 and valsartan treated diabetic rats revealed a substantial reduction in the levels of inflammatory markers such as TNF-?, IL-1?, IL-6, NF-kB and sufficient restoration of anti-oxidant enzyme levels (P < 0.05). Finally, in the histological sections of the kidney, prevention of renal injury was observed with limited necrosis and inflammatory cells infiltration.ConclusionPresent data suggest that LCZ696 has sufficient therapeutic potential to restrict DN progression through inhibiting inflammation, oxidative stress and glomerulosclerosis.

Dataset Information

Computational Segmentation and Classification of Diabetic Glomerulosclerosis.

Publications

Computational Segmentation and Classification of Diabetic Glomerulosclerosis.

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