Department of General Surgery, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Pathology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
J Crohns Colitis. 2024 May 31;18(5):727-737. doi: 10.1093/ecco-jcc/jjad196.
Myenteric plexitis is correlated with postoperative recurrence of Crohn's disease when relying on traditional statistical methods. However, comprehensive assessment of myenteric plexus remains challenging. This study aimed to develop and validate a deep learning system to predict postoperative recurrence through automatic screening and identification of features of the muscular layer and myenteric plexus.
We retrospectively reviewed 205 patients who underwent bowel resection surgery from two hospitals. Patients were divided into a training cohort [n = 108], an internal validation cohort [n = 47], and an external validation cohort [n = 50]. A total of 190 960 patches from 278 whole-slide images of surgical specimens were analysed using the ResNet50 encoder, and 6144 features were extracted after transfer learning. We used five robust algorithms to construct classification models. The performances of the models were evaluated based on the area under the receiver operating characteristic curve [AUC] in three cohorts.
The stacking model achieved satisfactory accuracy in predicting postoperative recurrence of CD in the training cohort (AUC: 0.980; 95% confidence interval [CI] 0.960-0.999), internal validation cohort [AUC: 0.908; 95% CI 0.823-0.992], and external validation cohort [AUC: 0.868; 95% CI 0.761-0.975]. The accuracy for identifying the severity of myenteric plexitis was 0.833, 0.745, and 0.694 in the training, internal validation and external validation cohorts, respectively.
Our work initially established an interpretable stacking model based on features of the muscular layer and myenteric plexus extracted from histological images to identify the severity of myenteric plexitis and predict postoperative recurrence of CD.
依靠传统的统计方法，肌间神经丛炎与克罗恩病的术后复发相关。然而，对肌间神经丛的全面评估仍然具有挑战性。本研究旨在开发和验证一种深度学习系统，通过自动筛选和识别肌层和肌间神经丛的特征来预测术后复发。
我们回顾性分析了来自两家医院的 205 例接受肠切除术的患者。患者分为训练队列[ n = 108]、内部验证队列[ n = 47]和外部验证队列[ n = 50]。使用 ResNet50 编码器分析了 278 张手术标本全切片图像的 190960 个斑块，经过迁移学习后提取了 6144 个特征。我们使用 5 种稳健的算法构建分类模型。基于三个队列中的接收者操作特征曲线[ AUC]下面积评估模型的性能。
堆叠模型在训练队列（AUC：0.980；95%置信区间[CI] 0.960-0.999）、内部验证队列（AUC：0.908；95%CI 0.823-0.992）和外部验证队列（AUC：0.868；95%CI 0.761-0.975）中对 CD 术后复发的预测具有令人满意的准确性。在训练、内部验证和外部验证队列中，识别肌间神经丛炎严重程度的准确性分别为 0.833、0.745 和 0.694。
我们的工作最初建立了一个基于从组织学图像中提取的肌层和肌间神经丛特征来识别肌间神经丛炎严重程度和预测 CD 术后复发的可解释堆叠模型。
