Lan Yadi, Han Bing, Zhai Tianyu, Xu Qianqian, Li Zhiwei, Liu Mingyue, Xue Yining, Xu Hongwei
Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China.
Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Front Oncol. 2024 Feb 27;14:1289265. doi: 10.3389/fonc.2024.1289265. eCollection 2024.
The diagnosis of gastric atrophy is highly subjective, and we aimed to establish a model of gastric atrophy based on pathological features to improve diagnostic consistency.
We retrospectively collected the HE-stained pathological slides of gastric biopsies and used CellProfiler software for image segmentation and feature extraction of ten representative images for each sample. Subsequently, we employed the Least absolute shrinkage and selection operator (LASSO) to select features and different machine learning (ML) algorithms to construct the diagnostic models for gastric atrophy.
We selected 289 gastric biopsy specimens for training, testing, and external validation. We extracted 464 pathological features and screened ten features by LASSO to establish the diagnostic model for moderate-to-severe atrophy. The range of area under the curve (AUC) for various machine learning algorithms was 0.835-1.000 in the training set, 0.786-0.949 in the testing set, and 0.689-0.818 in the external validation set. LR model had the highest AUC value, with 0.900 (95% CI: 0.852-0.947) in the training set, 0.901 (95% CI: 0.807-0.996) in the testing set, and 0.818 (95% CI: 0.714-0.923) in the external validation set. The atrophy pathological score based on the LR model was associated with endoscopic atrophy grading (Z=-2.478, P=0.013) and gastric cancer (GC) (OR=5.70, 95% CI: 2.63-12.33, P<0.001).
The ML model based on pathological features could improve the diagnostic consistency of gastric atrophy, which is also associated with endoscopic atrophy grading and GC.
胃萎缩的诊断具有高度主观性，我们旨在建立一种基于病理特征的胃萎缩模型，以提高诊断的一致性。
我们回顾性收集了胃活检的苏木精-伊红（HE）染色病理切片，并使用CellProfiler软件对每个样本的十张代表性图像进行图像分割和特征提取。随后，我们采用最小绝对收缩和选择算子（LASSO）来选择特征，并使用不同的机器学习（ML）算法构建胃萎缩的诊断模型。
我们选择了289例胃活检标本进行训练、测试和外部验证。我们提取了464个病理特征，并通过LASSO筛选出十个特征，以建立中重度萎缩的诊断模型。各种机器学习算法在训练集中的曲线下面积（AUC）范围为0.835 - 1.000，在测试集中为0.786 - 0.949，在外部验证集中为0.689 - 0.818。逻辑回归（LR）模型的AUC值最高，在训练集中为0.900（95%置信区间：0.852 - 0.947），在测试集中为0.901（95%置信区间：0.807 - 0.996），在外部验证集中为0.818（95%置信区间：0.714 - 0.923）。基于LR模型的萎缩病理评分与内镜下萎缩分级（Z = -2.478，P = 0.013）和胃癌（GC）（比值比=5.70，95%置信区间：2.63 - 12.33，P < 0.001）相关。
基于病理特征的ML模型可以提高胃萎缩的诊断一致性，且与内镜下萎缩分级和GC相关。
