Deep Learning-Based Quantitative Assessment of Renal Chronicity Indices in Lupus Nephritis

• URL: http://arxiv.org/abs/2503.21818v1
• Date: Wed, 26 Mar 2025 04:20:59 GMT
• Title: Deep Learning-Based Quantitative Assessment of Renal Chronicity Indices in Lupus Nephritis
• Authors: Tianqi Tu, Hui Wang, Jiangbo Pei, Xiaojuan Yu, Aidong Men, Suxia Wang, Qingchao Chen, Ying Tan, Feng Yu, Minghui Zhao,
• Abstract summary: Renal chronicity indices (CI) have been identified as strong predictors of long-term outcomes in lupus nephritis (LN) patients.<n>This study aims to develop a deep learning pipeline that automates the assessment of CI and provides valuable prognostic insights from a disease-specific perspective.
• Score: 13.486989114405974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references.<n>We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey.<n>Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• Testing Causal Explanations: A Case Study for Understanding the Effect of Interventions on Chronic Kidney Disease [1.2449538970962482]
  We developed a methodology that uses a large observational electronic health record dataset. Principles of regression discontinuity were used to derive randomized data subsets to test expert-driven interventions. This methodology demonstrates how real-world EHR data can be used to provide population-level insights to inform improved healthcare delivery.
  arXiv  Detail & Related papers  (2024-10-15T20:34:36Z)
• Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach [7.212939068975618]
  We utilized data about 10,326 CKD patients, combining their clinical and claims information from 2009 to 2018.<n>A 24-month observation window was identified as optimal for balancing early detection and prediction accuracy.<n>The 2021 eGFR equation improved prediction accuracy and reduced racial bias, notably for African American patients.
  arXiv  Detail & Related papers  (2024-10-02T03:21:01Z)
• AI Age Discrepancy: A Novel Parameter for Frailty Assessment in Kidney Tumor Patients [3.2441121935479877]
  This paper introduces AI Age Discrepancy, a novel metric derived from machine learning analysis of preoperative abdominal CT scans. A higher AI Age Discrepancy is significantly associated with longer hospital stays and lower overall survival rates, independent of established factors.
  arXiv  Detail & Related papers  (2024-06-29T13:15:05Z)
• TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network [54.332862955411656]
  Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials. We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
  arXiv  Detail & Related papers  (2023-07-19T12:35:09Z)
• Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention [0.6091702876917281]
  Long COVID-19 is a general term of post-acute sequel of COVID-19. Identifying the cohorts with severe long-term complications in COVID-19 could benefit the treatment planning and resource arrangement. It is difficult to predict outcomes from longitudinal data. A proposed aaetemporal attention mechanism to weigh importance jointly from the temporal dimension and feature space.
  arXiv  Detail & Related papers  (2023-07-07T19:38:45Z)
• A causal learning framework for the analysis and interpretation of COVID-19 clinical data [7.256237785391623]
  The workflow consists in a multi-step approach that goes from identifying the main causes of patient's outcome through BSL. We evaluate our approach on a feature-rich COVID-19 dataset, showing that the proposed framework provides a schematic overview of the multi-factorial processes that jointly contribute to the outcome. Our approach yields to a highly interpretable tool correctly predicting the outcome of 85% of subjects based exclusively on 3 features.
  arXiv  Detail & Related papers  (2021-05-14T15:58:18Z)
• Classification supporting COVID-19 diagnostics based on patient survey data [82.41449972618423]
  logistic regression and XGBoost classifiers, that allow for effective screening of patients for COVID-19 were generated. The obtained classification models provided the basis for the DECODE service (decode.polsl.pl), which can serve as support in screening patients with COVID-19 disease. This data set consists of more than 3,000 examples is based on questionnaires collected at a hospital in Poland.
  arXiv  Detail & Related papers  (2020-11-24T17:44:01Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)
• COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity [58.23203766439791]
  Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity. In this study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system.
  arXiv  Detail & Related papers  (2020-05-26T16:33:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.