ArticleScientific reports2018
Lupus nephritis pathology prediction with clinical indices.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Artificial neural network - an effective tool for predicting the lupus nephritis outcome.BMC nephrology · 2022Trial
- Prediction of 1-Year Activity in Systemic Lupus Erythematosus: Hierarchical Machine Learning Approach.JMIR formative research · 2025Article
- Combined Network Pharmacology, Transcriptomics and Metabolomics Strategies Reveal the Mechanism of Action of Lang Chuang Wan to Ameliorate Lupus Nephritis in MRL/lpr Mice.Pharmaceuticals (Basel, Switzerland) · 2025Article
- The relevance of complement levels in assessing the activity of lupus nephritis of different pathological types.Clinical rheumatology · 2025Article
- Machine learning in lupus nephritis: bridging prediction models and clinical decision-making towards personalized nephrology.Frontiers in medicine · 2025Review
- Machine learning-based identification of novel hub genes associated with oxidative stress in lupus nephritis: implications for diagnosis and therapeutic targets.Lupus science & medicine · 2024Article
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Machine learning models predicts risk of proliferative lupus nephritis.Frontiers in immunology · 2024Article
- Renal Fibrosis in Lupus Nephritis.International journal of molecular sciences · 2022Review
- Assessment of glomerular morphological patterns by deep learning algorithms.Journal of nephrology · 2022Article
- Systemic sclerosis complicated with renal thrombotic microangiopathy: a case report and literature review.BMC nephrology · 2022Review
- Silent Lupus Nephritis: Renal Histopathological Profile and Early Detection with Urinary Monocyte Chemotactic Protein 1.Open access rheumatology : research and reviews · 2022Article
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- Development of a lupus nephritis suboptimal response prediction tool using renal histopathological and clinical laboratory variables at the time of diagnosis.Lupus science & medicine · 2021Article
- Association of Lupus Nephritis Histopathologic Classification With Venous Thromboembolism-Modification by Age at Biopsy.Kidney international reports · 2021Article
- Interleukin-12 exacerbates symptoms in an MRL/MpJ-Faslpr mouse model of systemic lupus erythematosus.Experimental and therapeutic medicine · 2021Article
- Analysis of clinical and laboratory characteristics and pathology of lupus nephritis-based on 710 renal biopsies in China.Clinical rheumatology · 2020Article
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Downregulated miR-203 attenuates IL-β, IL-6, and TNF-α activation in TRAF6-treated human renal mesangial and tubular epithelial cells.International journal of clinical and experimental pathology · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective treatment of lupus nephritis and assessment of patient prognosis depend on accurate pathological classification and careful use of acute and chronic pathological indices. Renal biopsy can provide most reliable predicting power. However, clinicians still need auxiliary tools under certain circumstances. Comprehensive statistical analysis of clinical indices may be an effective support and supplementation for biopsy. In this study, 173 patients with lupus nephritis were classified based on histology and scored on acute and chronic indices. These results were compared against machine learning predictions involving multilinear regression and random forest analysis. For three class random forest analysis, total classification accuracy was 51.3% (class II 53.7%, class III&IV 56.2%, class V 40.1%). For two class random forest analysis, class II accuracy reached 56.2%; class III&IV 63.7%; class V 61%. Additionally, machine learning selected out corresponding important variables for each class prediction. Multiple linear regression predicted the index of chronic pathology (CI) (Q
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