Abstract
Grading inflammatory bowel disease (IBD) activity using standardized histopathological scoring systems remains challenging due to resource constraints and inter-observer variability. In this study, we developed a deep learning model to classify activity grades in hematoxylin and eosin-stained whole slide images (WSIs) from patients with IBD, offering a robust approach for general pathologists. We utilized 2,077 WSIs from 636 patients treated at Dartmouth-Hitchcock Medical Center in 2018 and 2019, scanned at 40x magnification (0.25 micron/pixel). Board-certified gastrointestinal pathologists categorized the WSIs into four activity classes: inactive, mildly active, moderately active, and severely active. A transformer-based model was developed and validated using five-fold cross-validation to classify IBD activity. Using HoVerNet, we examined neutrophil distribution across activity grades. Attention maps from our model highlighted areas contributing to its prediction. The model classified IBD activity with weighted averages of 0.871 [95% Confidence Interval (CI): 0.860-0.883] for the area under the curve, 0.695 [95% CI: 0.674-0.715] for precision, 0.697 [95% CI: 0.678-0.716] for recall, and 0.695 [95% CI: 0.674-0.714] for F1-score. Neutrophil distribution was significantly different across activity classes. Qualitative evaluation of attention maps by a gastrointestinal pathologist suggested their potential for improved interpretability. Our model demonstrates robust diagnostic performance and could enhance consistency and efficiency in IBD activity assessment.
Abstract (translated)
使用标准化的组织病理学评分系统对炎症性肠病（IBD）的活动程度进行分级仍然具有挑战性，这主要由于资源限制和观察者之间的变异。在本研究中，我们开发了一种深度学习模型，用于根据来自IBD患者的苏木精-伊红染色全幻灯片图像（WSIs）来分类活动等级，为普通病理学家提供了一种稳健的方法。我们利用了2018年和2019年间在达特茅斯哈奇逊医疗中心治疗的636名患者中的2,077张WSIs，这些图片以40倍放大（每像素0.25微米）扫描而成。认证的胃肠病理学家将这些WSIs分为四个活动等级：不活跃、轻度活跃、中度活跃和重度活跃。我们通过五折交叉验证开发并验证了一个基于变换器的模型来分类IBD活动情况。利用HoVerNet，我们研究了不同活动级别下中性粒细胞的分布情况。我们的模型生成的关注图高亮显示了对其预测有贡献的区域。该模型对IBD活动程度进行了分类，其加权平均值为：曲线下面积（AUC）0.871 [95%置信区间（CI）: 0.860-0.883]，精确度0.695 [95% CI: 0.674-0.715]，召回率0.697 [95% CI: 0.678-0.716]，F1得分0.695 [95% CI: 0.674-0.714]。不同活动级别的中性粒细胞分布存在显著差异。胃肠病理学家对注意力图的定性评估表明它们有可能提高解释能力。我们的模型展示了强大的诊断性能，并可能改善IBD活动程度评估的一致性和效率。
URL
https://arxiv.org/abs/2410.19690
