A computed tomography-based radiomic model for the prediction of strangulation risk in patients with acute intestinal obstruction

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Zhibo Wang , Ruiqing Liu , Shunli Liu , Baoying Sun , Wentao Xie , Dongsheng Wang , Yun Lu
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引用次数: 0

Abstract

Background

We created and validated a computed tomography (CT)-based radiomic model using both clinical factors and the radiomic signature for assessing the strangulation risk of acute intestinal obstruction. This would assist surgeons in accurately predicting intestinal ischemia and strangulation in patients with intestinal obstruction.

Methods

We recruited 289 patients with acute intestinal obstruction admitted in the Affiliated Hospital of Qingdao University from January 2019 to February 2022. The patients were allocated to a training (n = 226) and validation cohort (n = 63). Radiomic features were collected from CT images, and the radiomic signature was extracted and used to calculate a radiomic score (Rad-score). A nomogram was constructed using the clinical features and the Rad-score, and the performance of the clinical, radiomics, and nomogram models was assessed in the two cohorts.

Results

Six robust features were used to construct the radiomic signature. The nomogram incorporating hemoglobin levels, C-reactive protein levels, American Society of Anesthesiologists score, time of obstruction, CT image of mesenteric fluid (P < 0.05), and the signature demonstrated good predictive ability for intestinal ischemia in patients with acute intestinal obstruction, with areas under the curve of 0.892 (95% confidence interval, 0.837–0.947) and 0.781 (95% confidence interval, 0.619–0.944) for the training and validation sets, respectively. The decision curve analysis showed that this model outperformed the clinical and radiomic signature models.

Conclusion

The radiomic nomogram may effectively predict intestinal ischemia in patients with acute intestinal disease and may assist clinical decision-making.

基于CT的放射学模型预测急性肠梗阻患者绞杀风险
背景我们创建并验证了一种基于计算机断层扫描(CT)的放射学模型,该模型利用临床因素和放射学特征评估急性肠梗阻的绞窄风险。这将有助于外科医生准确预测肠梗阻患者的肠缺血和绞窄情况。方法 我们招募了 2019 年 1 月至 2022 年 2 月在青岛大学附属医院住院的 289 名急性肠梗阻患者。患者被分配到训练队列(226 人)和验证队列(63 人)。从CT图像中收集放射学特征,提取放射学特征并用于计算放射学评分(Rad-score)。使用临床特征和 Rad 评分构建了一个提名图,并在两个队列中评估了临床、放射组学和提名图模型的性能。包含血红蛋白水平、C 反应蛋白水平、美国麻醉医师协会评分、梗阻时间、肠系膜积液 CT 图像(P < 0.05)和特征的提名图对急性肠梗阻患者肠缺血具有良好的预测能力,训练集和验证集的曲线下面积分别为 0.892(95% 置信区间,0.837-0.947)和 0.781(95% 置信区间,0.619-0.944)。结论放射学提名图可有效预测急性肠道疾病患者的肠缺血情况,并有助于临床决策。
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
Intelligent medicine
Intelligent medicine Surgery, Radiology and Imaging, Artificial Intelligence, Biomedical Engineering
CiteScore
5.20
自引率
0.00%
发文量
19
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