Image-based deep learning model to predict stoma-site incisional hernia in patients with temporary ileostomy: A retrospective study.

IF 4.6 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

iScience Pub Date : 2024-10-22 eCollection Date: 2024-11-15 DOI:10.1016/j.isci.2024.111235

Zhongyi Dong, Jianhua Cai, Haigang Geng, Bo Ni, Mengqing Yuan, Yeqian Zhang, Xiang Xia, Haoyu Zhang, Jie Zhang, Chunchao Zhu, Un Wai Choi, Aksara Regmi, Cheok I Chan, Cara Kou Yan, Yan Gu, Hui Cao, Zizhen Zhang

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引用次数: 0

Abstract

The prophylactic implantation of biological mesh can effectively prevent the occurrence of stoma-site incisional hernia (SSIH) in patients undergoing stoma retraction. Therefore, our study prospectively established and validated a mixed model, which combined radiomics, stepwise regression, and deep learning for the prediction of SSIH in patients with temporary ileostomy. The mixed model showed good discrimination of the SSIH patients on all cohorts, which outperformed deep learning, radiomics, and clinical models alone (overall area under the curve [AUC]: 0.947 in the primary cohort, 0.876 in the external validation cohort 1, and 0.776 in the external validation cohort 2). Moreover, the sensitivity, specificity, and precision for predicting SSIH were improved in the mixed model. Thus, the mixed model can provide more information for SSIH precaution and clinical decision-making.

查看原文本刊更多论文

基于图像的深度学习模型预测临时回肠造口术患者的造口切口疝：一项回顾性研究。

在接受造口回缩术的患者中，预防性植入生物网片可有效预防造口部位切口疝（SSIH）的发生。因此，我们的研究前瞻性地建立并验证了一个混合模型，该模型结合了放射组学、逐步回归和深度学习，用于预测临时回肠造口患者的SSIH。该混合模型对所有队列中的 SSIH 患者都显示出良好的分辨能力，优于单独的深度学习、放射组学和临床模型（总体曲线下面积 [AUC]：主队列为 0.947，副队列为 0.947）：初级队列为 0.947，外部验证队列 1 为 0.876，外部验证队列 2 为 0.776）。此外，混合模型预测 SSIH 的灵敏度、特异性和精确度都有所提高。因此，混合模型可为 SSIH 预防和临床决策提供更多信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

iScience Multidisciplinary-Multidisciplinary

CiteScore

7.20

自引率

1.70%

发文量

1972

审稿时长

6 weeks

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