Eosinophilic esophagitis (EoE) is a chronic, food antigen-driven, allergic
inflammatory condition of the esophagus associated with elevated esophageal
eosinophils. EoE is a top cause of chronic dysphagia after GERD. Diagnosis of
EoE relies on counting eosinophils in histological slides, a manual and
time-consuming task that limits the ability to extract complex
patient-dependent features. The treatment of EoE includes medication and food
elimination. A personalized food elimination plan is crucial for engagement and
efficiency, but previous attempts failed to produce significant results. In
this work, on the one hand, we utilize AI for inferring histological features
from the entire biopsy slide, features that cannot be extracted manually. On
the other hand, we develop causal learning models that can process this wealth
of data. We applied our approach to the 'Six-Food vs. One-Food Eosinophilic
Esophagitis Diet Study', where 112 symptomatic adults aged 18-60 years with
active EoE were assigned to either a six-food elimination diet (6FED) or a
one-food elimination diet (1FED) for six weeks. Our results show that the
average treatment effect (ATE) of the 6FED treatment compared with the 1FED
treatment is not significant, that is, neither diet was superior to the other.
We examined several causal models and show that the best treatment strategy was
obtained using T-learner with two XGBoost modules. While 1FED only and 6FED
only provide improvement for 35%-38% of the patients, which is not
significantly different from a random treatment assignment, our causal model
yields a significantly better improvement rate of 58.4%. This study illustrates
the significance of AI in enhancing treatment planning by analyzing molecular
features' distribution in histological slides through causal learning. Our
approach can be harnessed for other conditions that rely on histology for
diagnosis and treatment.

这项研究利用 AI 推断组织学特征，并引入因果学习模型，应用于以组织学诊断为基础的食物消除计划中。研究结果表明，相比于只有一种食物消除计划，六种食物消除计划并不能提供更好的治疗效果，而且单独的食物消除计划对 35% 至 38% 的患者有改善作用，而本研究推出的因果模型治疗策略可以提供 58.4% 的改善率。

利用数字病理学和因果学习改善嗜酸性食管炎饮食治疗分配

Harnessing Digital Pathology And Causal Learning To Improve Eosinophilic  Esophagitis Dietary Treatment Assignment

In digital pathology, whole slide images (WSIs) are widely used for
applications such as cancer diagnosis and prognosis prediction. Visual
transformer models have recently emerged as a promising method for encoding
large regions of WSIs while preserving spatial relationships among patches.
However, due to the large number of model parameters and limited labeled data,
applying transformer models to WSIs remains challenging. Inspired by masked
language models, we propose a pretext task for training the transformer model
without labeled data to address this problem. Our model, MaskHIT, uses the
transformer output to reconstruct masked patches and learn representative
histological features based on their positions and visual features. The
experimental results demonstrate that MaskHIT surpasses various multiple
instance learning approaches by 3% and 2% on survival prediction and cancer
subtype classification tasks, respectively. Furthermore, MaskHIT also
outperforms two of the most recent state-of-the-art transformer-based methods.
Finally, a comparison between the attention maps generated by the MaskHIT model
with pathologist's annotations indicates that the model can accurately identify
clinically relevant histological structures in each task.

该论文提出了一种名为 MaskHIT 的模型，它使用预训练的 transformer 模型来重建被遮盖的 WSI 区域并学习组织学特征，进而在癌症诊断和预测任务中表现出比其他方法更好的效果。