Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning
| Autor: | Chuanfeng Lv, Shaoshan Liang, Qunjuan Lei, WeiLi Luo, Fengyi Li, Caihong Zeng, Feng Xu, Dandan Liang, Xiaoshuai Hou, Guotong Xie, Yang Nan, Xiang Li, Zhihong Liu, Bin Lv, Tingyu Chen |
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| Rok vydání: | 2020 |
| Předmět: |
IgAN
Adult Male 0301 basic medicine Pathology medicine.medical_specialty Cell type Kidney Glomerulus Mesangial hypercellularity glomerular lesion classification urologic and male genital diseases Pathology and Forensic Medicine 03 medical and health sciences Deep Learning 0302 clinical medicine medicine Humans Diagnosis Computer-Assisted Pathological intrinsic glomerular cells identification Original Paper Kidney Podocytes business.industry Glomerulosclerosis Glomerulonephritis IGA medicine.disease Original Papers mesangial hypercellularity score assessment Confidence interval 030104 developmental biology medicine.anatomical_structure Renal pathology 030220 oncology & carcinogenesis Mesangial Cells Female Kidney Diseases Neural Networks Computer business Kappa computational pathology |
| Zdroj: | The Journal of Pathology |
| ISSN: | 1096-9896 0022-3417 |
| DOI: | 10.1002/path.5491 |
| Popis: | Identification of glomerular lesions and structures is a key point for pathological diagnosis, treatment instructions, and prognosis evaluation in kidney diseases. These time‐consuming tasks require a more accurate and reproducible quantitative analysis method. We established derivation and validation cohorts composed of 400 Chinese patients with immunoglobulin A nephropathy (IgAN) retrospectively. Deep convolutional neural networks and biomedical image processing algorithms were implemented to locate glomeruli, identify glomerular lesions (global and segmental glomerular sclerosis, crescent, and none of the above), identify and quantify different intrinsic glomerular cells, and assess a network‐based mesangial hypercellularity score in periodic acid–Schiff (PAS)‐stained slides. Our framework achieved 93.1% average precision and 94.9% average recall for location of glomeruli, and a total Cohen's kappa of 0.912 [95% confidence interval (CI), 0.892–0.932] for glomerular lesion classification. The evaluation of global, segmental glomerular sclerosis, and crescents achieved Cohen's kappa values of 1.0, 0.776, 0.861, and 95% CI of (1.0, 1.0), (0.727, 0.825), (0.824, 0.898), respectively. The well‐designed neural network can identify three kinds of intrinsic glomerular cells with 92.2% accuracy, surpassing the about 5–11% average accuracy of junior pathologists. Statistical interpretation shows that there was a significant difference (P value |
| Databáze: | OpenAIRE |
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