基于深度学习的狼疮性肾炎肾脏慢性化指数定量评估
Deep Learning-Based Quantitative Assessment of Renal Chronicity Indices in Lupus Nephritis
AI总结：
本研究开发了一种深度学习流程，用于自动化评估狼疮性肾炎的肾脏慢性化指数，在分割性能、观察者一致性及预后预测方面均优于现有方法，为临床决策提供有力支持。
摘要：
背景：肾脏慢性化指数（CI）已被确定为狼疮性肾炎（LN）患者长期结局的强预测因子。然而，病理学家的评估受到诸多挑战的阻碍，如大量时间需求、观察者间差异大以及易受疲劳影响。本研究旨在开发一种有效的深度学习（DL）流程，以自动化CI评估，并从疾病特异性角度提供有价值的预后见解。方法：我们构建了一个数据集，包含来自两个独立队列的141名患者的282张切片，并具有完整的10年随访。我们的DL流程在训练队列中30名患者的60张切片（22,410个斑块图像）上开发，并在内部测试集（148张切片，77,605个斑块图像）和外部测试集（74张切片，27,522个斑块图像）上进行评估。结果：该研究包括两个队列，在人口统计学特征上略有差异，特别是在年龄和血红蛋白水平方面。DL流程在组织区室和组织病理学病变中显示出高分割性能，优于最先进的方法。DL流程在评估CI方面也与病理学家表现出强相关性，显著提高了观察者间一致性。此外，当与临床参数和病理学家评估的CI结合时，DL流程增强了预后准确性，特别是在结局预测方面。结论：DL流程在评估LN中的CI方面表现出准确性和效率，显示出改善病理学家之间观察者间一致性的潜力。它还在LN患者的预后分析和结局预测增强方面表现出显著价值，为临床决策提供了有价值的工具。
英文摘要：
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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