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Zeng C, Nan Y, Xu F, Lei Q, Li F, Chen T, Liang S, Hou X, Lv B, Liang D, Luo W, Lv C, Li X, Xie G, Liu Z. Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning. J Pathol 2020; 252:53-64. [PMID: 32542677 PMCID: PMC7496925 DOI: 10.1002/path.5491] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Revised: 05/26/2020] [Accepted: 06/05/2020] [Indexed: 12/14/2022]
Abstract
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 < 0.0001) between this analytic renal pathology system (ARPS) and four junior pathologists for identifying mesangial and endothelial cells, while that for podocytes was similar, with P value = 0.0602. In addition, this study indicated that the ratio of mesangial cells, endothelial cells, and podocytes within glomeruli from IgAN was 0.41:0.36:0.23, and the performance of mesangial score assessment reached a Cohen's kappa of 0.42 and 95% CI (0.18, 0.69). The proposed computer‐aided diagnosis system has feasibility for quantitative analysis and auxiliary recognition of glomerular pathological features. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Caihong Zeng
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Yang Nan
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Feng Xu
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Qunjuan Lei
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Fengyi Li
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Tingyu Chen
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - Shaoshan Liang
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | | | - Bin Lv
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Dandan Liang
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
| | - WeiLi Luo
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Chuanfeng Lv
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Xiang Li
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Guotong Xie
- Ping An Healthcare Technology, Shang Hai, PR China
| | - Zhihong Liu
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, PR China
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