Deep Learning-Based Quantitative Assessment of Renal Chronicity Indices in Lupus Nephritis
Journal:
arXiv
Published Date:
Mar 26, 2025
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.
