Split-bolus CTA for mesenteric ischemia with a single scan opacifying arterial and mesenteric venous systems

Radiological diagnosis of acute mesenteric ischemia in adult patients: a systematic review and meta-analysis

Computed tomography (CT) is widely used in diagnosing acute mesenteric ischemia (AMI), but robust identification of distinctive subtypes and stages of progression is lacking. Systematic literature search in PubMed, Cochrane Library, Web of Science and Scopus was conducted in May 2024. Studies including at least 10 adult patients and reporting radiological diagnosis of AMI versus no AMI or transmural ischemia versus no transmural ischemia were included. Meta-analyses on sensitivity and specificity of different radiological features in diagnosing AMI were conducted. From 2628 titles, 490 studies underwent full text review, and 81 were included in 14 meta-analyses. Diagnostic accuracy of CT angiography (CTA) was high - sensitivity of 92.0% and specificity of 98.8% (I2 45% and 79%, respectively), but lower for other CT protocols (sensitivity 75.8 and specificity 90.5; I2 83%). In most included studies, distinction of subtypes and severity of AMI (non-transmural or transmural) was not possible. Amongst the non-vascular features, absent/reduced bowel wall enhancement provided the best prognostic value (sensitivity 57.9 and specificity 90.1). CTA is the method of choice for diagnosing AMI with high diagnostic accuracy. None of the non-vascular features alone is sufficiently reliable to diagnose AMI or its progression to transmural necrosis, whereas a combination of different radiological features conveys a potential.

Deep learning-assisted diagnosis of acute mesenteric ischemia based on CT angiography images

Purpose

Acute Mesenteric Ischemia (AMI) is a critical condition marked by restricted blood flow to the intestine, which can lead to tissue necrosis and fatal outcomes. We aimed to develop a deep learning (DL) model based on CT angiography (CTA) imaging and clinical data to diagnose AMI.

Methods

A retrospective study was conducted on 228 patients suspected of AMI, divided into training and test sets. Clinical data (medical history and laboratory indicators) was included in a multivariate logistic regression analysis to identify the independent factors associated with AMI and establish a clinical factors model. The arterial and venous CTA images were utilized to construct DL model. A Fusion Model was constructed by integrating clinical factors into the DL model. The performance of the models was assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA).

Results

Albumin and International Normalized Ratio (INR) were associated with AMI by univariate and multivariate logistic regression (P < 0.05). In the test set, the area under ROC curve (AUC) of the clinical factor model was 0.60 (sensitivity 0.47, specificity 0.86). The AUC of the DL model based on CTA images reached 0.90, which was significantly higher than the AUC values of the clinical factor model, as confirmed by the DeLong test (P < 0.05). The Fusion Model also showed exceptional performance in terms of AUC, accuracy, sensitivity, specificity, and precision, with values of 0.96, 0.94, 0.94, 0.95, and 0.98, respectively. DCA indicated that the Fusion Model provided a greater net benefit than those of models based solely on imaging and clinical information across the majority of the reasonable threshold probabilities.

Conclusion

The incorporation of CTA images and clinical information into the model markedly enhances the diagnostic accuracy and efficiency of AMI. This approach provides a reliable tool for the early diagnosis of AMI and the subsequent implementation of appropriate clinical intervention.

Deep learning algorithm applied to plain CT images to identify superior mesenteric artery abnormalities

OBJECTIVES

Atypical presentations, lack of biomarkers, and low sensitivity of plain CT can delay the diagnosis of superior mesenteric artery (SMA) abnormalities, resulting in poor clinical outcomes. Our study aims to develop a deep learning (DL) model for detecting SMA abnormalities in plain CT and evaluate its performance in comparison with a clinical model and radiologist assessment.

MATERIALS AND METHODS

A total of 1048 patients comprised the internal (474 patients with SMA abnormalities, 474 controls) and external testing (50 patients with SMA abnormalities, 50 controls) cohorts. The internal cohort was divided into the training cohort (n = 776), validation cohort (n = 86), and internal testing cohort (n = 86). A total of 5 You Only Look Once version 8 (YOLOv8)-based DL submodels were developed, and the performance of the optimal submodel was compared with that of a clinical model and of experienced radiologists.

RESULTS

Of the submodels, YOLOv8x had the best performance. The area under the curve (AUC) of the YOLOv8x submodel was higher than that of the clinical model (internal test set: 0.990 vs 0.878, P =.002; external test set: 0.967 vs 0.912, P =.140) and that of all radiologists (P <.001). The YOLOv8x submodel, when compared with radiologist assessment, demonstrated higher sensitivity (internal test set: 100.0 % vs 70.7 %, P =.002; external test set: 96.0 % vs 68.8 %, P <.001) and specificity (internal test set: 90.7 % vs 66.0 %, P =.025; external test set: = 88.0 % vs 66.0 %, P <.001).

CONCLUSION

Using plain CT images, YOLOv8x was able to efficiently identify cases of SMA abnormalities. This could potentially improve early diagnosis accuracy and thus improve clinical outcomes.

Imaging of Bowel Ischemia: An Update, From the AJR Special Series on Emergency Radiology

Acute mesenteric ischemia is a life-threatening condition that results from abrupt reduction in or cessation of blood flow to the bowel. Characterized by nonspecific abdominal symptoms, mesenteric ischemia is infrequently encountered and commonly misdiagnosed, with potentially catastrophic consequences. Prompt clinical diagnosis and early implementation of therapeutic interventions are critical to improving patient outcomes. Because cross-sectional imaging plays a key role in the diagnosis of mesenteric ischemia, radiologists must be familiar with the varied imaging manifestations of intestinal ischemia. Thus, the objectives of this article are to review the various types and common causes of mesenteric ischemia and to describe its spectrum of multimodality imaging findings, with special attention to novel imaging techniques and emerging diagnoses.

[Gas in superior mesenteric artery and celiac axis as a rare CT-sign of extensive bowel necrosis]

The authors report endovascular treatment of acute thromboembolic occlusion of superior mesenteric artery in a 75-year-old patient whose postoperative period was complicated by massive reperfusion and translocation syndrome. Contrast-enhanced CT in 12 hours after successful thrombectomy from superior mesenteric artery revealed CT signs of irreversible bowel lesion, i.e. gas in hepatic veins, intestinal wall and mesenteric veins, bowel wall thinning. In addition, CT revealed extremely rare sign of severe acute mesenteric ischemia (gas in superior mesenteric artery and celiac axis). We found no description of gas in celiac axis following acute mesenteric ischemia in available literature.