Deep Learning-Based Quantitative Assessment of Renal Chronicity Indices in Lupus Nephritis
Background: Renal chronicity indices (CI) have been identified as strong predictors of long-term outcomes in lupus nephritis (LN) patients. However, assessment by pathologists is hindered by challenges such as substantial time requirements, high interobserver variation, and susceptibility to fatigue. This study aims to develop an effective deep learning (DL) pipeline that automates the assessment of CI and provides valuable prognostic insights from a disease-specific perspective. Methods: We curated a dataset comprising 282 slides obtained from 141 patients across two independent cohorts with a complete 10-years follow-up. Our DL pipeline was developed on 60 slides (22,410 patch images) from 30 patients in the training cohort and evaluated on both an internal testing set (148 slides, 77,605 patch images) and an external testing set (74 slides, 27,522 patch images). Results: The study included two cohorts with slight demographic differences, particularly in age and hemoglobin levels. The DL pipeline showed high segmentation performance across tissue compartments and histopathologic lesions, outperforming state-of-the-art methods. The DL pipeline also demonstrated a strong correlation with pathologists in assessing CI, significantly improving interobserver agreement. Additionally, the DL pipeline enhanced prognostic accuracy, particularly in outcome prediction, when combined with clinical parameters and pathologist-assessed CIs Conclusions: The DL pipeline demonstrated accuracy and efficiency in assessing CI in LN, showing promise in improving interobserver agreement among pathologists. It also exhibited significant value in prognostic analysis and enhancing outcome prediction in LN patients, offering a valuable tool for clinical decision-making.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Classification of histopathology images using ConvNets to detect Lupus Nephritis
Systemic lupus erythematosus (SLE) is an autoimmune disease in which the immune system of the patient starts attacking healthy tissues of the body. Lupus Nephritis (LN) refers to the inflammation of kidney tissues result…
Classificationwhole slide imagesKidney segmentation using 3D U-Net localized with Expectation Maximization
Kidney volume is greatly affected in several renal diseases. Precise and automatic segmentation of the kidney can help determine kidney size and evaluate renal function. Fully convolutional neural networks have been used…
SegmentationClinical-Injection Transformer with Domain-Adapted MAE for Lupus Nephritis Prognosis Prediction
Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus that affects pediatric patients with significantly greater severity and worse renal outcomes compared to adults. Despite the urgent clinical n…
Dithymoquinone as a novel inhibitor for 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) to prevent renal failure
3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) is a major endogenous ligand found in the human serum albumin (HSA) of renal failure patients. It gets accumulated in the HSA and its concentration in sera of pati…
Molecular DockingCross-modal ultra-scale learning with tri-modalities of renal biopsy images for glomerular multi-disease auxiliary diagnosis
Constructing a multi-modal automatic classification model based on three types of renal biopsy images can assist pathologists in glomerular multi-disease identification. However, the substantial scale difference between …