Segmentation-model-based framework to detect aortic dissection on non-contrast CT images: a retrospective study.

IF 4.5 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Insights into Imaging Pub Date : 2025-09-25 DOI:10.1186/s13244-025-02098-z

Qidong Wang, Shan Huang, Weifeng Pan, Zhan Feng, Lei Lv, Dongwei Guan, Zhiwen Yang, Yimin Huang, Wei Liu, Weiwei Shui, Mingliang Ying, Wenbo Xiao

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引用次数: 0

Abstract

Objectives: To develop an automated deep learning framework for detecting aortic dissection (AD) and visualizing its morphology and extent on non-contrast CT (NCCT) images.

Materials and methods: This retrospective study included patients who underwent aortic CTA from January 2021 to January 2023 at two tertiary hospitals. Demographic data, medical history, and CT scans were collected. A segmentation-based deep learning model was trained to identify true and false lumens on NCCT images, with performance evaluated on internal and external test sets. Segmentation accuracy was measured using the Dice coefficient, while the intraclass correlation coefficient (ICC) assessed consistency between predicted and ground-truth false lumen volumes. Receiver operating characteristic (ROC) analysis evaluated the model's predictive performance.

Results: Among 701 patients (median age, 53 years, IQR: 41-64, 486 males), data from Center 1 were split into training (439 cases: 318 non-AD, 121 AD) and internal test sets (106 cases: 77 non-AD, 29 AD) (8:2 ratio), while Center 2 served as the external test set (156 cases: 80 non-AD, 76 AD). The ICC for false lumen volume was 0.823 (95% CI: 0.750-0.880) internally and 0.823 (95% CI: 0.760-0.870) externally. The model achieved an AUC of 0.935 (95% CI: 0.894-0.968) in the external test set, with an optimal cutoff of 7649 mm³ yielding 88.2% sensitivity, 91.3% specificity, and 89.0% negative predictive value.

Conclusions: The proposed deep learning framework accurately detects AD on NCCT and effectively visualizes its morphological features, demonstrating strong clinical potential.

Critical relevance statement: This deep learning framework helps reduce the misdiagnosis of AD in emergencies with limited time. The satisfactory results of presenting true/false lumen on NCCT images benefit patients with contrast media contraindications and promote treatment decisions.

Key points: False lumen volume was used as an indicator for AD. NCCT detects AD via this segmentation model. This framework enhances AD diagnosis in emergencies, reducing unnecessary contrast use.

查看原文本刊更多论文

基于分割模型的框架在非对比CT图像上检测主动脉夹层：回顾性研究。

目的：开发一个自动深度学习框架，用于检测主动脉夹层（AD）并在非对比CT （NCCT）图像上显示其形态和范围。材料和方法：本回顾性研究纳入了2021年1月至2023年1月在两家三级医院接受主动脉CTA治疗的患者。收集了人口统计资料、病史和CT扫描。训练基于分割的深度学习模型来识别NCCT图像上的真假流明，并在内部和外部测试集上评估性能。使用Dice系数测量分割精度，而类内相关系数（ICC）评估预测和真实假腔体积之间的一致性。受试者工作特征（ROC）分析评估了模型的预测性能。结果：701例患者（中位年龄53岁，IQR: 41-64，男性486例）中，中心1的数据分为训练组（439例：非AD 318例，AD 121例）和内部测试组（106例：非AD 77例，AD 29例）（8:2比例），中心2作为外部测试组（156例：非AD 80例，AD 76例）。假流明容积的内部ICC为0.823 (95% CI: 0.750-0.880)，外部ICC为0.823 （95% CI: 0.760-0.870）。该模型在外部测试集中的AUC为0.935 (95% CI: 0.894-0.968)，最佳截止值为7649 mm3，敏感性为88.2%，特异性为91.3%，阴性预测值为89.0%。结论：所提出的深度学习框架能够准确检测NCCT上的AD，并有效地将其形态特征可视化，具有很强的临床应用潜力。关键相关性声明：该深度学习框架有助于在有限的时间内减少紧急情况下对AD的误诊。在NCCT图像上显示真/假腔的令人满意的结果有利于有造影剂禁忌症的患者，并促进治疗决策。重点：假腔容积作为AD的指标。NCCT通过该分割模型检测AD。该框架加强了对紧急情况下AD的诊断，减少了不必要的对比剂使用。

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

Insights into Imaging Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

7.30

自引率

4.30%

发文量

182

审稿时长

13 weeks

期刊介绍： Insights into Imaging (I³) is a peer-reviewed open access journal published under the brand SpringerOpen. All content published in the journal is freely available online to anyone, anywhere! I³ continuously updates scientific knowledge and progress in best-practice standards in radiology through the publication of original articles and state-of-the-art reviews and opinions, along with recommendations and statements from the leading radiological societies in Europe. Founded by the European Society of Radiology (ESR), I³ creates a platform for educational material, guidelines and recommendations, and a forum for topics of controversy. A balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes I³ an indispensable source for current information in this field. I³ is owned by the ESR, however authors retain copyright to their article according to the Creative Commons Attribution License (see Copyright and License Agreement). All articles can be read, redistributed and reused for free, as long as the author of the original work is cited properly. The open access fees (article-processing charges) for this journal are kindly sponsored by ESR for all Members. The journal went open access in 2012, which means that all articles published since then are freely available online.