Artificial intelligence assists identification and pathologic classification of glomerular lesions in patients with diabetic nephropathy.

BACKGROUND: Glomerular lesions are the main injuries of diabetic nephropathy (DN) and are used as a crucial index for pathologic classification. Manual quantification of these morphologic features currently used is semi-quantitative and time-consuming. Automatically quantifying glomerular morphologic features is urgently needed. METHODS: A series of convolutional neural networks (CNN) were designed to identify and classify glomerular morphologic features in DN patients. Associations of these digital features with pathologic classification and prognosis were further analyzed. RESULTS: Our CNN-based model achieved a 0.928 F1-score for global glomerulosclerosis and 0.953 F1-score for Kimmelstiel-Wilson lesion, further obtained a dice of 0.870 for the mesangial area and F1-score beyond 0.839 for three glomerular intrinsic cells. As the pathologic classes increased, mesangial cell numbers and mesangial area increased, and podocyte numbers decreased (p for all < 0.001), while endothelial cell numbers remained stable (p = 0.431). Glomeruli with Kimmelstiel-Wilson lesion showed more severe podocyte deletion compared to those without (p < 0.001). Furthermore, CNN-based classifications showed moderate agreement with pathologists-based classification, the kappa value between the CNN model 3 and pathologists reached 0.624 (ranging from 0.529 to 0.688, p < 0.001). Notably, CNN-based classifications obtained equivalent performance to pathologists-based classifications on predicting baseline and long-term renal function. CONCLUSION: Our CNN-based model is promising in assisting the identification and pathologic classification of glomerular lesions in DN patients.

Identification of glomerular lesions and intrinsic glomerular cell types in kidney diseases via deep learning.

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.

The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge.

There is a large body of literature linking anatomic and geometric characteristics of kidney tumors to perioperative and oncologic outcomes. Semantic segmentation of these tumors and their host kidneys is a promising tool for quantitatively characterizing these lesions, but its adoption is limited due to the manual effort required to produce high-quality 3D segmentations of these structures. Recently, methods based on deep learning have shown excellent results in automatic 3D segmentation, but they require large datasets for training, and there remains little consensus on which methods perform best. The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) which sought to address these issues and stimulate progress on this automatic segmentation problem. A training set of 210 cross sectional CT images with kidney tumors was publicly released with corresponding semantic segmentation masks. 106 teams from five continents used this data to develop automated systems to predict the true segmentation masks on a test set of 90 CT images for which the corresponding ground truth segmentations were kept private. These predictions were scored and ranked according to their average Sørensen-Dice coefficient between the kidney and tumor across all 90 cases. The winning team achieved a Dice of 0.974 for kidney and 0.851 for tumor, approaching the inter-annotator performance on kidney (0.983) but falling short on tumor (0.923). This challenge has now entered an "open leaderboard" phase where it serves as a challenging benchmark in 3D semantic segmentation.

Correlation-Guided Network for Fine-Grained Classification of Glomerular lesions in Kidney Histopathology Images.

Chronic Kidney Disease has become a worldwide public health problem which demands careful assessments by pathologists. In this paper, we propose a novel architecture for fine-grained classification of glomerular lesions in renal pathology images sampling from patients with IgA nephropathy. The adversarial correlation loss is innovatively presented to guide a parallel convolutional neural network. In this well- designed loss function, bias between the prediction and the label was take into account while the relationship among different categories is well-aligned. Glomerular lesions in this study are divided into five subcategories, Neg (Negative samples such as tubule and artery), SS (sclerosis involving a portion of the glomerular tuft), GS (sclerosis involving 100% of the tuft), C (build-up of more than two layers of cells within Bowman's space, often with fibrin and collagen deposition) and NOA (none of above). Our model with 93.0% accuracy and 92.9% Fl-score for these five categories has proved superior to other models through experimental results.