用深度学习自动评估狼疮性肾炎慢性指数，提升诊断一致性和预后预测准确率。
Deep Learning-Based Quantitative Assessment of Renal Chronicity Indices in Lupus Nephritis
- 构建深度学习流水线，自动分割肾组织并识别病变区域。
- 在内外部测试集上与病理科医生评估高度一致，显著降低判读差异。
- 结合临床数据可更好预测患者10年预后，适合临床辅助决策。
背景：肾慢性指数（CI）是狼疮性肾炎（LN）患者长期预后的强预测因子。但病理学家评估受耗时、观察者间差异大及疲劳影响。本研究旨在开发一种高效的深度学习（DL）流程，实现CI自动化评估，并提供疾病特异性预后信息。方法：我们收集了来自两个独立队列共282张切片，涵盖141名患者，具有完整的10年随访数据。DL流程在训练队列30例患者的60张切片（22,410个图像块）上训练，于内部测试集（148张切片，77,605个图像块）和外部测试集（74张切片，27,522个图像块）上评估。结果：两队列在年龄和血红蛋白水平上略有差异。该DL流程在组织分区和病理病变识别上表现优异，优于现有最先进方法。其对CI的评估与病理科医生高度相关，显著提升观察者间一致性。此外，结合临床参数和病理科医生评估的CI，DL流程显著提高结局预测准确性。结论：该DL流程在评估LN慢性指数方面具备高精度与高效性，有望改善病理学家间的判断一致性，并在预后分析中展现重要价值，为临床决策提供有力工具。
原文摘要 · Abstract (English)
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.
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