CT of blunt splenic injuries: what the trauma team wants to know from the radiologist

Association of Contrast Extravasation Grade With Massive Transfusion in Pediatric Blunt Liver and Spleen Injuries: A Multicenter Retrospective Cohort Study

BACKGROUND

This study aimed to assess whether the grade of contrast extravasation (CE) on CT scans was associated with massive transfusion (MT) requirements in pediatric blunt liver and/or spleen injuries (BLSI).

METHODS

This multicenter retrospective cohort study included pediatric patients (≤16 years old) who sustained BLSI between 2008 and 2019. MT was defined as transfusion of all blood products ≥40 mL/kg within the first 24 h of admission. Associations between CE and MT requirements were assessed using multivariate logistic regression analysis with cluster-adjusted robust standard errors to calculate the adjusted odds ratio (AOR).

RESULTS

A total of 1407 children (median age: 9 years) from 83 institutions were included in the analysis. Overall, 199 patients (14 %) received MT. CT on admission revealed that 54 patients (3.8 %) had CE within the subcapsular hematoma, 100 patients (7.1 %) had intraparenchymal CE, and 86 patients (6.1 %) had CE into the peritoneal cavity among the overall cohort. Multivariate analysis, adjusted for age, sex, age-adjusted shock index, injury severity, and laboratory and imaging factors, showed that intraparenchymal CE and CE into the peritoneal cavity were significantly associated with the need for MT (AOR: 2.50; 95 % CI, 1.50-4.16 and AOR: 4.98; 95 % CI, 2.75-9.02, respectively both p < 0.001). The latter significant association persisted in the subgroup of patients with spleen and liver injuries.

CONCLUSION

Active CE into the free peritoneal cavity on admission CT was independently associated with a greater probability of receiving MT in pediatric BLSI. The CE grade may help clinicians plan blood transfusion strategies.

LEVEL OF EVIDENCE

Level 4; Therapeutic/Care management.

MDCT Findings of Splenic Pathology

The delineation and characterization of splenic lesions and other abnormalities can be challenging on computed tomography. Many splenic lesions are incidentally found, imaging features tend to overlap, and without the appropriate clinical context, differentials can range from benign to malignant. Radiologists should be familiar with the wide variety of pathologies seen on computed tomography as it is often the first imaging modality a splenic lesion is seen. The purpose of this MDCT-focused review is to understand normal splenic anatomy and its variants, to illustrate and describe typical and atypical imaging patters of inflammatory, infectious, vascular, traumatic, benign, and malignant tumors of the spleen and provide clues in reaching the appropriate differential diagnosis and management.

Unidentified bright objects of spleen on arterial phase CT: mimicker of splenic vascular injury in blunt abdominal trauma

PURPOSE

We have described unidentified bright objects of spleen (UBOS), a hitherto undescribed entity, as hyperdense areas on arterial phase (AP) computed tomography (CT) seen in relation to splenic lacerations and are isodense to the normal parenchyma on portal venous phase with no correlate on digital subtraction angiography (DSA). UBOS mimic splenic vascular injuries like active contrast extravasation and pseudoaneurysm and need to be differentiated from them as it would have implications on patient management. We undertook this study to identify CT features of UBOS that can differentiate them from splenic vascular injuries and to calculate their diagnostic accuracy.

METHODS

This retrospective study was approved by the institutional ethical committee and the need for informed consent was waived. Patients with splenic injury who had undergone dual-phase CT and DSA were included. All the lesions that were hyperdense on AP were evaluated for their outline, their relation to the adjacent/parallel margins of a laceration (margin sign), string of beads appearance, and the presence of adjacent normal parenchyma (adjacent parenchyma sign). The Hounsfield unit (HU) of the lesion and the aorta on the AP were also noted. The diagnostic accuracy of various signs for distinguishing UBOS from splenic vascular injuries was calculated using DSA as the reference standard.

RESULTS

Of 48 patients, 5 were excluded due to suboptimal quality of the examination or a time difference of more than 6 hours between the CT and DSA. A total of 54 hyperdense lesions were detected on AP in 43 patients. These were classified as vascular injuries (pseudoaneurysm, n=11; active contrast extravasation, n=11) and UBOS (n=32) based on DSA. The margin sign, string of beads appearance, and ill-defined outline had high specificity (95%, 86%, and 82%, respectively) but low sensitivity (50%, 65%, and 63%, respectively). The adjacent parenchyma sign had a moderate sensitivity and specificity of 84% and 77%, respectively. ROC analysis showed that a difference of 50 HU between the aorta and the lesion had a high sensitivity and specificity of 88.9% and 90.6%, respectively, with an area under the curve of 0.90.

CONCLUSION

An attenuation difference of over 50 HU between the aorta and the lesion and the presence of normal adjacent parenchyma had the highest diagnostic accuracy, while an ill-defined outline, string of beads appearance, and margin sign had high specificity but low sensitivity for differentiating UBOS from splenic vascular injuries.

Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning

The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM, k-nearest neighbors (k-NN) ensemble, and subspace discriminant ensemble models trained via 5-fold cross validation. Of these models, random forest performed the best, achieving an Area Under the receiver operating characteristic Curve (AUC) of 0.91 and an F1 score of 0.80 on the test set. These results suggest that an automated, quantitative assessment of traumatic spleen injury has the potential to enable faster triage and improve patient outcomes.

Expert consensus on interventional therapy for traumatic splenic bleeding

This study aims to introduce the diagnosis and treatment processes of traumatic splenic bleeding and explain its emergency, medical, interventional, and surgical treatments. Furthermore, this study aims to summarize the indications and contraindications of splenic artery embolization, interventional procedures, and precautions of complications.