Background

The incidence of non-alcoholic fatty liver disease (NAFLD) and its progressive form, non-alcoholic steatohepatitis (NASH), has been increasing for decades. Since the mainstay is lifestyle modification in this mainly asymptomatic condition, there is a need for accurate diagnostic methods.

Objectives

This study proposes a method with a computer-aided diagnosis (CAD) system to predict NAFLD Activity score (NAS scores-steatosis, lobular inflammation, and ballooning) and fibrosis stage from histopathology slides.

Methods

A total of 87 pathology slides pairs (H&E and Trichrome-stained) were used for the study. Ground-truth NAS scores and fibrosis stages were previously identified by a pathologist. Each slide was split into 224 × 224 patches and fed into a feature extraction network to generate local features. These local features were processed and aggregated to obtain a global feature to predict the slide's scores. The effects of different training strategies, as well as training data with different staining and magnifications were explored. Four-fold cross validation was performed due to the small data size. Area Under the Receiver Operating Curve (AUROC) was utilized to evaluate the prediction performance of the machine-learning algorithm.

Results

Predictive accuracy for all subscores was high in comparison with pathologist assessment. There was no difference among the 3 magnifications (5x, 10x, 20x) for NAS-steatosis and fibrosis stage tasks. A larger magnification (20x) achieved better performance for NAS-lobular scores. Middle-level magnification was best for NAS-ballooning task. Trichrome slides are better for fibrosis stage prediction and NAS-ballooning score prediction task. NAS-steatosis prediction had the best performance (AUC 90.48%) in the model. A good performance was observed with fibrosis stage prediction (AUC 83.85%) as well as NAS-ballooning prediction (AUC 81.06%).

Conclusions

These results were robust. The method proposed proved to be effective in predicting NAFLD Activity score and fibrosis stage from histopathology slides. The algorithms are an aid in having an accurate and systematic diagnosis in a condition that affects hundreds of millions of patients globally.

中文翻译：

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训练计算算法以从肝脏组织病理学切片预测 NAFLD 活动评分和纤维化阶段

背景

非酒精性脂肪性肝病 (NAFLD) 及其进行性形式非酒精性脂肪性肝炎 (NASH) 的发病率几十年来一直在增加。由于主要是在这种主要无症状的情况下改变生活方式，因此需要准确的诊断方法。

目标

本研究提出了一种具有计算机辅助诊断 (CAD) 系统的方法，可根据组织病理学切片预测 NAFLD 活动评分（NAS 评分-脂肪变性、小叶炎症和气球样变）和纤维化阶段。

方法

总共 87 个病理载玻片对（H&E 和三色染色）用于研究。Ground-truth NAS 评分和纤维化阶段先前由病理学家确定。每张幻灯片被分成 224 × 224 块，并输入特征提取网络以生成局部特征。这些局部特征被处理和聚合以获得全局特征来预测幻灯片的分数。探索了不同训练策略以及具有不同染色和放大倍数的训练数据的效果。由于数据量小，进行了四折交叉验证。接收器操作曲线下面积 (AUROC) 用于评估机器学习算法的预测性能。

结果

与病理学家评估相比，所有分项的预测准确度都很高。NAS 脂肪变性和纤维化阶段任务的 3 倍放大倍数（5 倍、10 倍、20 倍）之间没有差异。更大的放大倍数 (20 倍) 为 NAS 小叶评分实现了更好的性能。中级放大最适合 NAS 气球任务。三色幻灯片更适合纤维化阶段预测和 NAS 气球评分预测任务。NAS-脂肪变性预测在模型中具有最佳性能（AUC 90.48%）。纤维化阶段预测 (AUC 83.85%) 和 NAS 气球膨胀预测 (AUC 81.06%) 均观察到了良好的性能。

结论

这些结果是稳健的。所提出的方法被证明可有效地从组织病理学切片预测 NAFLD 活动评分和纤维化阶段。这些算法有助于在影响全球数亿患者的情况下进行准确和系统的诊断。