Blunt polytrauma: evaluation with 64-section whole-body CT angiography

Trauma and 'Whole' Body Computed Tomography: Role, Protocols, Appropriateness, and Evidence to Support its Use and When

Imaging plays a crucial role in the immediate evaluation of the trauma patient, particularly using multi-detector computed tomography (CT), and especially in moderately to severely injured trauma patients. There are specific areas of relative consensus, while other aspects of whole-body computed tomography (WB-CT) use remain controversial and are subject to opinion/debate based on the current literature. Even a few hours of a delayed diagnosis may result in a detrimental outcome for the patient. One must utilize all the tools available to enhance the interpretation of images. It is also important to recognize imaging pitfalls and artifacts to avoid unnecessary intervention.

Retrospective Observational Analysis of Computed Tomography Scans of Trauma Patients in Overseas Operations (SCANOPEX Study)

INTRODUCTION

High-intensity conflicts are on Europe's doorstep. The French expertise in the medical management of frontline casualties in overseas operations is well established. However, in the management of severe trauma, we lack data on the injuries identified by body scanners in the field. Understanding the associations between injury mechanisms and radiological lesions would enable us to anticipate medical and surgical management. To study this possible link, we collected and interpreted scanogaphic data and analyzed them according to lesion mechanisms, following the algorithm MARCH used to implement the concept of Damage Control Resuscitation, which includes life-saving measures to ensure that the wounded reach medical-surgical facilities alive.

MATERIALS AND METHODS

This retrospective monocentric study collected data from body scanners performed in overseas operations between June 2011 and September 2023. Inclusion criteria were to be French military personnel and to have undergone a whole-body scanner in a theater of overseas operations. Exclusion criteria were to have died before the scan, to be foreign, non-military and a minor. Of 164 available files, 96 were eligible, 1 patient declared aged 70 years was excluded, and 95 files were retained.

RESULTS

In our population, 18% of injured patients had a spinal fracture. Compared with road traffic accident casualties, improvised explosive device casualties were the most severely injured patients arriving alive at computed tomography, with a relative risk of Injury Severity Score > 8 of 2.29 [1.09-4.80] (P = .019). Improvised explosive device casualties had a relative risk of airway injuries of 2.57 [1.03-6.39] (P = .030), injuries leading to functional impairment of 3.21 [1.17-8.82] (P =.013), injuries leading to infection of 2.14 [1.21-3.76] (P = .0045), and injuries leading to shock of 3.21 [0.96-10.70] (P = .039). Deep metal splinters were only found in the improvised explosive device group.

CONCLUSION

Preparing the medical corps to deal with war casualties is fundamental. Our study shows that it is essential to consider the mechanism of injury to understand the casualty better and predict potential injuries. In addition, the study of postmortem scans could greatly help analyze potentially avoidable deaths.

Automated Association for Osteosynthesis Foundation and Orthopedic Trauma Association classification of pelvic fractures on pelvic radiographs using deep learning

High-energy impacts, like vehicle crashes or falls, can lead to pelvic ring injuries. Rapid diagnosis and treatment are crucial due to the risks of severe bleeding and organ damage. Pelvic radiography promptly assesses fracture extent and location, but struggles to diagnose bleeding. The AO/OTA classification system grades pelvic instability, but its complexity limits its use in emergency settings. This study develops and evaluates a deep learning algorithm to classify pelvic fractures on radiographs per the AO/OTA system. Pelvic radiographs of 773 patients with pelvic fractures and 167 patients without pelvic fractures were retrospectively analyzed at a single center. Pelvic fractures were classified into types A, B, and C using medical records categorized by an orthopedic surgeon according to the AO/OTA classification system. Accuracy, Dice Similarity Coefficient (DSC), and F1 score were measured to evaluate the diagnostic performance of the deep learning algorithms. The segmentation model showed high performance with 0.98 accuracy and 0.96-0.97 DSC. The AO/OTA classification model demonstrated effective performance with a 0.47-0.80 F1 score and 0.69-0.88 accuracy. Additionally, the classification model had a macro average of 0.77-0.94. Performance evaluation of the models showed relatively favorable results, which can aid in early classification of pelvic fractures.

Machine Learning Detection and Characterization of Splenic Injuries on Abdominal Computed Tomography

BACKGROUND

Multi-detector contrast-enhanced abdominal computed tomography (CT) allows for the accurate detection and classification of traumatic splenic injuries, leading to improved patient management. Their effective use requires rapid study interpretation, which can be a challenge on busy emergency radiology services. A machine learning system has the potential to automate the process, potentially leading to a faster clinical response. This study aimed to create such a system.

METHOD

Using the American Association for the Surgery of Trauma (AAST), spleen injuries were classified into 3 classes: normal, low-grade (AAST grade I-III) injuries, and high-grade (AAST grade IV and V) injuries. Employing a 2-stage machine learning strategy, spleens were initially segmented from input CT images and subsequently underwent classification via a 3D dense convolutional neural network (DenseNet).

RESULTS

This single-centre retrospective study involved trauma protocol CT scans performed between January 1, 2005, and July 31, 2021, totaling 608 scans with splenic injuries and 608 without. Five board-certified fellowship-trained abdominal radiologists utilizing the AAST injury scoring scale established ground truth labels. The model achieved AUC values of 0.84, 0.69, and 0.90 for normal, low-grade injuries, and high-grade splenic injuries, respectively.

CONCLUSIONS

Our findings demonstrate the feasibility of automating spleen injury detection using our method with potential applications in improving patient care through radiologist worklist prioritization and injury stratification. Future endeavours should concentrate on further enhancing and optimizing our approach and testing its use in a real-world clinical environment.

Traumatic Cervical Cerebrovascular Injury and the Role of CTA: AJR Expert Panel Narrative Review

Traumatic cerebrovascular injury (CVI) involving the cervical carotid and vertebral arteries is rare but can lead to stroke, hemodynamic compromise, and mortality in the absence of early diagnosis and treatment. The diagnosis of both blunt cerebrovascular injury (BCVI) and penetrating CVI is based on cerebrovascular imaging. The most commonly used screening criteria for BCVI include the expanded Denver criteria and the Memphis criteria, each providing varying thresholds for subsequent imaging. Neck CTA has supplanted catheter-based digital subtraction angiography as the preferred screening modality for CVI in patients with trauma. This AJR Expert Panel Narrative Review describes the current state of CTA-based cervical imaging in trauma. We review the most common screening criteria for BCVI, discuss BCVI grading scales that are based on neck CTA, describe the diagnostic performance of CTA in the context of other imaging modalities and evolving treatment strategies, and provide a practical guide for neck CTA implementation.

CTA Imaging of Peripheral Arterial Injuries

Traumatic vascular injuries consist of direct or indirect damage to arteries and/or veins and account for 3% of all traumatic injuries. Typical consequences are hemorrhage and ischemia. Vascular injuries of the extremities can occur isolated or in association with major trauma and other organ injuries. They account for 1-2% of patients admitted to emergency departments and for approximately 50% of all arterial injuries. Lower extremities are more frequently injured than upper ones in the adult population. The outcome of vascular injuries is strictly correlated to the environment and the time background. Treatment can be challenging, notably in polytrauma because of the dilemma of which injury should be prioritized, and treatment delay can cause disability or even death, especially for limb vascular injury. Our purposes are to discuss the role of computed tomography angiography (CTA) in the diagnosis of vascular trauma and its optimized protocol to achieve a definitive diagnosis and to assess the radiological signs of vascular injuries and the possible pitfalls.

An ASER AI/ML expert panel formative user research study for an interpretable interactive splenic AAST grading graphical user interface prototype

PURPOSE

The AAST Organ Injury Scale is widely adopted for splenic injury severity but suffers from only moderate inter-rater agreement. This work assesses SpleenPro, a prototype interactive explainable artificial intelligence/machine learning (AI/ML) diagnostic aid to support AAST grading, for effects on radiologist dwell time, agreement, clinical utility, and user acceptance.

METHODS

Two trauma radiology ad hoc expert panelists independently performed timed AAST grading on 76 admission CT studies with blunt splenic injury, first without AI/ML assistance, and after a 2-month washout period and randomization, with AI/ML assistance. To evaluate user acceptance, three versions of the SpleenPro user interface with increasing explainability were presented to four independent expert panelists with four example cases each. A structured interview consisting of Likert scales and free responses was conducted, with specific questions regarding dimensions of diagnostic utility (DU); mental support (MS); effort, workload, and frustration (EWF); trust and reliability (TR); and likelihood of future use (LFU).

RESULTS

SpleenPro significantly decreased interpretation times for both raters. Weighted Cohen's kappa increased from 0.53 to 0.70 with AI/ML assistance. During user acceptance interviews, increasing explainability was associated with improvement in Likert scores for MS, EWF, TR, and LFU. Expert panelists indicated the need for a combined early notification and grading functionality, PACS integration, and report autopopulation to improve DU.

CONCLUSIONS

SpleenPro was useful for improving objectivity of AAST grading and increasing mental support. Formative user research identified generalizable concepts including the need for a combined detection and grading pipeline and integration with the clinical workflow.

A vendor-agnostic, PACS integrated, and DICOM-compatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow

Background

Reproducible approaches are needed to bring AI/ML for medical image analysis closer to the bedside. Investigators wishing to shadow test cross-sectional medical imaging segmentation algorithms on new studies in real-time will benefit from simple tools that integrate PACS with on-premises image processing, allowing visualization of DICOM-compatible segmentation results and volumetric data at the radiology workstation.

Purpose

In this work, we develop and release a simple containerized and easily deployable pipeline for shadow testing of segmentation algorithms within the clinical workflow.

Methods

Our end-to-end automated pipeline has two major components- 1. A router/listener and anonymizer and an OHIF web viewer backstopped by a DCM4CHEE DICOM query/retrieve archive deployed in the virtual infrastructure of our secure hospital intranet, and 2. An on-premises single GPU workstation host for DICOM/NIfTI conversion steps, and image processing. DICOM images are visualized in OHIF along with their segmentation masks and associated volumetry measurements (in mL) using DICOM SEG and structured report (SR) elements. Since nnU-net has emerged as a widely-used out-of-the-box method for training segmentation models with state-of-the-art performance, feasibility of our pipleine is demonstrated by recording clock times for a traumatic pelvic hematoma nnU-net model.

Results

Mean total clock time from PACS send by user to completion of transfer to the DCM4CHEE query/retrieve archive was 5 min 32 s (± SD of 1 min 26 s). This compares favorably to the report turnaround times for whole-body CT exams, which often exceed 30 min, and illustrates feasibility in the clinical setting where quantitative results would be expected prior to report sign-off. Inference times accounted for most of the total clock time, ranging from 2 min 41 s to 8 min 27 s. All other virtual and on-premises host steps combined ranged from a minimum of 34 s to a maximum of 48 s.

Conclusion

The software worked seamlessly with an existing PACS and could be used for deployment of DL models within the radiology workflow for prospective testing on newly scanned patients. Once configured, the pipeline is executed through one command using a single shell script. The code is made publicly available through an open-source license at "https://github.com/vastc/," and includes a readme file providing pipeline config instructions for host names, series filter, other parameters, and citation instructions for this work.

Pulmonary contusion: automated deep learning-based quantitative visualization

PURPOSE

Rapid automated CT volumetry of pulmonary contusion may predict progression to Acute Respiratory Distress Syndrome (ARDS) and help guide early clinical management in at-risk trauma patients. This study aims to train and validate state-of-the-art deep learning models to quantify pulmonary contusion as a percentage of total lung volume (Lung Contusion Index, or auto-LCI) and assess the relationship between auto-LCI and relevant clinical outcomes.

METHODS

302 adult patients (age ≥ 18) with pulmonary contusion were retrospectively identified from reports between 2016 and 2021. nnU-Net was trained on manual contusion and whole-lung segmentations. Point-of-care candidate variables for multivariate regression included oxygen saturation, heart rate, and systolic blood pressure on admission. Logistic regression was used to assess ARDS risk, and Cox proportional hazards models were used to determine differences in ICU length of stay and mechanical ventilation time.

RESULTS

Mean Volume Similarity Index and mean Dice scores were 0.82 and 0.67. Interclass correlation coefficient and Pearson r between ground-truth and predicted volumes were 0.90 and 0.91. 38 (14%) patients developed ARDS. In bivariate analysis, auto-LCI was associated with ARDS (p < 0.001), ICU admission (p < 0.001), and need for mechanical ventilation (p < 0.001). In multivariate analyses, auto-LCI was associated with ARDS (p = 0.04), longer length of stay in the ICU (p = 0.02) and longer time on mechanical ventilation (p = 0.04). AUC of multivariate regression to predict ARDS using auto-LCI and clinical variables was 0.70 while AUC using auto-LCI alone was 0.68.

CONCLUSION

Increasing auto-LCI values corresponded with increased risk of ARDS, longer ICU admissions, and longer periods of mechanical ventilation.

Accelerating voxelwise annotation of cross-sectional imaging through AI collaborative labeling with quality assurance and bias mitigation

Background

precision-medicine quantitative tools for cross-sectional imaging require painstaking labeling of targets that vary considerably in volume, prohibiting scaling of data annotation efforts and supervised training to large datasets for robust and generalizable clinical performance. A straight-forward time-saving strategy involves manual editing of AI-generated labels, which we call AI-collaborative labeling (AICL). Factors affecting the efficacy and utility of such an approach are unknown. Reduction in time effort is not well documented. Further, edited AI labels may be prone to automation bias.

Purpose

In this pilot, using a cohort of CTs with intracavitary hemorrhage, we evaluate both time savings and AICL label quality and propose criteria that must be met for using AICL annotations as a high-throughput, high-quality ground truth.

Methods

57 CT scans of patients with traumatic intracavitary hemorrhage were included. No participant recruited for this study had previously interpreted the scans. nnU-net models trained on small existing datasets for each feature (hemothorax/hemoperitoneum/pelvic hematoma; n = 77-253) were used in inference. Two common scenarios served as baseline comparison- de novo expert manual labeling, and expert edits of trained staff labels. Parameters included time effort and image quality graded by a blinded independent expert using a 9-point scale. The observer also attempted to discriminate AICL and expert labels in a random subset (n = 18). Data were compared with ANOVA and post-hoc paired signed rank tests with Bonferroni correction.

Results

AICL reduced time effort 2.8-fold compared to staff label editing, and 8.7-fold compared to expert labeling (corrected p < 0.0006). Mean Likert grades for AICL (8.4, SD:0.6) were significantly higher than for expert labels (7.8, SD:0.9) and edited staff labels (7.7, SD:0.8) (corrected p < 0.0006). The independent observer failed to correctly discriminate AI and human labels.

Conclusion

For our use case and annotators, AICL facilitates rapid large-scale curation of high-quality ground truth. The proposed quality control regime can be employed by other investigators prior to embarking on AICL for segmentation tasks in large datasets.

Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel

BACKGROUND

AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty.

PURPOSE

To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness.

METHODS

Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends.

RESULTS

A total of 4052 records were screened, and 233 full-text articles were selected for content analysis. Twenty-one papers described FDA-approved commercial tools, and 212 reported algorithm prototypes. Works ranged from foundational research to multi-reader multi-case trials with heterogeneous external data. Scalable convolutional neural network-based implementations increased steeply after 2016 and were used in all commercial products; however, options for explainability were narrow. Of FDA-approved tools, 9/10 performed detection tasks. Dataset sizes ranged from < 100 to > 500,000 patients, and commercialization coincided with public dataset availability. Cross-sectional torso datasets were uniformly small. Data curation methods with ground truth labeling by independent readers were uncommon. No papers assessed user acceptance, and no method included human-computer interaction. The USA and China had the highest research output and frequency of research funding.

CONCLUSIONS

Trauma imaging CAD tools are likely to improve patient care but are currently in an early stage of maturity, with few FDA-approved products for a limited number of uses. The scarcity of high-quality annotated data remains a major barrier.

A vendor-agnostic, PACS integrated, and DICOMcompatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow

Background

Reproducible approaches are needed to bring AI/ML for medical image analysis closer to the bedside. Investigators wishing to shadow test cross-sectional medical imaging segmentation algorithms on new studies in real-time will benefit from simple tools that integrate PACS with on-premises image processing, allowing visualization of DICOM-compatible segmentation results and volumetric data at the radiology workstation.

Purpose

In this work, we develop and release a simple containerized and easily deployable pipeline for shadow testing of segmentation algorithms within the clinical workflow.

Methods

Our end-to-end automated pipeline has two major components-1. a router/listener and anonymizer and an OHIF web viewer backstopped by a DCM4CHEE DICOM query/retrieve archive deployed in the virtual infrastructure of our secure hospital intranet, and 2. An on-premises single GPU workstation host for DICOM/NIfTI conversion steps, and image processing. DICOM images are visualized in OHIF along with their segmentation masks and associated volumetry measurements (in mL) using DICOM SEG and structured report (SR) elements. Feasibility is demonstrated by recording clock times for a traumatic pelvic hematoma cascaded nnU-net model.

Results

Mean total clock time from PACS send by user to completion of transfer to the DCM4CHEE query/retrieve archive was 5 minutes 32 seconds (+/- SD of 1 min 26 sec). This compares favorably to the report turnaround times for whole-body CT exams, which often exceed 30 minutes. Inference times accounted for most of the total clock time, ranging from 2 minutes 41 seconds to 8 minutes 27 seconds. All other virtual and on-premises host steps combined ranged from a minimum of 34 seconds to a maximum of 48 seconds.

Conclusion

The software worked seamlessly with an existing PACS and could be used for deployment of DL models within the radiology workflow for prospective testing on newly scanned patients. Once configured, the pipeline is executed through one command using a single shell script. The code is made publicly available through an open-source license at "https://github.com/vastc/", and includes a readme file providing pipeline config instructions for host names, series filter, other parameters, and citation instructions for this work.

Toward automated interpretable AAST grading for blunt splenic injury

BACKGROUND

The American Association for the Surgery of Trauma (AAST) splenic organ injury scale (OIS) is the most frequently used CT-based grading system for blunt splenic trauma. However, reported inter-rater agreement is modest, and an algorithm that objectively automates grading based on transparent and verifiable criteria could serve as a high-trust diagnostic aid.

PURPOSE

To pilot the development of an automated interpretable multi-stage deep learning-based system to predict AAST grade from admission trauma CT.

METHODS

Our pipeline includes 4 parts: (1) automated splenic localization, (2) Faster R-CNN-based detection of pseudoaneurysms (PSA) and active bleeds (AB), (3) nnU-Net segmentation and quantification of splenic parenchymal disruption (SPD), and (4) a directed graph that infers AAST grades from detection and segmentation results. Training and validation is performed on a dataset of adult patients (age ≥ 18) with voxelwise labeling, consensus AAST grading, and hemorrhage-related outcome data (n = 174).

RESULTS

AAST classification agreement (weighted κ) between automated and consensus AAST grades was substantial (0.79). High-grade (IV and V) injuries were predicted with accuracy, positive predictive value, and negative predictive value of 92%, 95%, and 89%. The area under the curve for predicting hemorrhage control intervention was comparable between expert consensus and automated AAST grading (0.83 vs 0.88). The mean combined inference time for the pipeline was 96.9 s.

CONCLUSIONS

The results of our method were rapid and verifiable, with high agreement between automated and expert consensus grades. Diagnosis of high-grade lesions and prediction of hemorrhage control intervention produced accurate results in adult patients.

Blunt splenic injury in adults: Association between volumetric quantitative CT parameters and intervention

BACKGROUND.

Several ordinal grading systems are employed in deciding whether to perform angioembolization or splenectomy following blunt splenic injury. The 2018 AAST Organ Injury Scale (OIS) incorporates vascular lesions but not hemoperitoneum, which is considered in the Thompson classifier. Granular and verifiable quantitative measurements of these features may have a future role in facilitating objective decision-making.

PURPOSE.

To compare performance of CT volumetry-based quantitative modeling to the 1994 and 2018 AAST OIS and Thompson classifier for the following endpoints: decision to perform splenectomy (SPY), and the composite of SPY or angioembolization (AE)

MATERIALS AND METHODS.

Adult BSI patients (age ≥ 18 years) scanned with dual-phase CT prior to intervention at a single level I trauma center from 2017-2019 were included in this retrospective study (n=174). Scoring using 2018 AAST, 1994 AAST, and Thompson systems was performed retrospectively by two radiologists and arbitrated by a third. Endpoints included 1. SPY and 2. The composite of SPY or AE. Logistic regression models were developed from segmented active bleed, contained vascular lesion, splenic parenchymal disruption, and hemoperitoneum volumes. AUCs for ordinal systems and volumetric models were compared.

RESULTS.

Forty-seven BSI patients (27%) underwent SPY, and 87 patients (50%) underwent SPY or AE. Quantitative model AUCs (0.85- SPY, 0.82-composite) were not significantly different from 2018 AAST AUCs (0.81, 0.88, p=0.66, 0.14) for both endpoints, and were significantly improved over Thompson scoring (0.76, p=0.02; 0.77, p=0.04).

CONCLUSION:

Quantitative CT volumetry can be used to model intervention for BSI with accuracy comparable to 2018 AAST scoring and significantly higher than Thompson scoring.

Study Type:

Prognostic

Level of Evidence:

IV

CT volumetry of blunt splenic injury-related features predicts splenectomy and angioembolization in adults and identifies clinically important target features for computer vision and automation research.

A pilot study of deep learning-based CT volumetry for traumatic hemothorax

PURPOSE

We employ nnU-Net, a state-of-the-art self-configuring deep learning-based semantic segmentation method for quantitative visualization of hemothorax (HTX) in trauma patients, and assess performance using a combination of overlap and volume-based metrics. The accuracy of hemothorax volumes for predicting a composite of hemorrhage-related outcomes - massive transfusion (MT) and in-hospital mortality (IHM) not related to traumatic brain injury - is assessed and compared to subjective expert consensus grading by an experienced chest and emergency radiologist.

MATERIALS AND METHODS

The study included manually labeled admission chest CTs from 77 consecutive adult patients with non-negligible (≥ 50 mL) traumatic HTX between 2016 and 2018 from one trauma center. DL results of ensembled nnU-Net were determined from fivefold cross-validation and compared to individual 2D, 3D, and cascaded 3D nnU-Net results using the Dice similarity coefficient (DSC) and volume similarity index. Pearson's r, intraclass correlation coefficient (ICC), and mean bias were also determined for the best performing model. Manual and automated hemothorax volumes and subjective hemothorax volume grades were analyzed as predictors of MT and IHM using AUC comparison. Volume cut-offs yielding sensitivity or specificity ≥ 90% were determined from ROC analysis.

RESULTS

Ensembled nnU-Net achieved a mean DSC of 0.75 (SD: ± 0.12), and mean volume similarity of 0.91 (SD: ± 0.10), Pearson r of 0.93, and ICC of 0.92. Mean overmeasurement bias was only 1.7 mL despite a range of manual HTX volumes from 35 to 1503 mL (median: 178 mL). AUC of automated volumes for the composite outcome was 0.74 (95%CI: 0.58-0.91), compared to 0.76 (95%CI: 0.58-0.93) for manual volumes, and 0.76 (95%CI: 0.62-0.90) for consensus expert grading (p = 0.93). Automated volume cut-offs of 77 mL and 334 mL predicted the outcome with 93% sensitivity and 90% specificity respectively.

CONCLUSION

Automated HTX volumetry had high method validity, yielded interpretable visual results, and had similar performance for the hemorrhage-related outcomes assessed compared to manual volumes and expert consensus grading. The results suggest promising avenues for automated HTX volumetry in research and clinical care.

External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation With Limited Data

The spleen is one of the most commonly injured solid organs in blunt abdominal trauma. The development of automatic segmentation systems from multi-phase CT for splenic vascular injury can augment severity grading for improving clinical decision support and outcome prediction. However, accurate segmentation of splenic vascular injury is challenging for the following reasons: 1) Splenic vascular injury can be highly variant in shape, texture, size, and overall appearance; and 2) Data acquisition is a complex and expensive procedure that requires intensive efforts from both data scientists and radiologists, which makes large-scale well-annotated datasets hard to acquire in general. In light of these challenges, we hereby design a novel framework for multi-phase splenic vascular injury segmentation, especially with limited data. On the one hand, we propose to leverage external data to mine pseudo splenic masks as the spatial attention, dubbed external attention, for guiding the segmentation of splenic vascular injury. On the other hand, we develop a synthetic phase augmentation module, which builds upon generative adversarial networks, for populating the internal data by fully leveraging the relation between different phases. By jointly enforcing external attention and populating internal data representation during training, our proposed method outperforms other competing methods and substantially improves the popular DeepLab-v3+ baseline by more than 7% in terms of average DSC, which confirms its effectiveness.

Roles of Trauma CT and CTA in Salvaging the Threatened or Mangled Extremity

Extremity arterial injuries account for up to 50% of all arterial traumas. The speed, accuracy, reproducibility, and close proximity of modern CT scanners to the trauma bay have led to the liberal use of CT angiography (CTA) when a limb is in ischemic jeopardy or is a potential source of life-threatening hemorrhage. The radiologist plays a critical role in the rapid communication of findings related to vessel transection and occlusion. Another role of CT that is often overlooked involves adding value to surgical planning. The following are some of the key questions addressed in this review: How does CTA help determine whether a limb is salvageable? How do concurrent multisystem injuries affect decision making? Which arterial injuries can be safely managed with observation alone? What damage control techniques are used to address compartment syndrome and hemorrhage? What options are available for definitive revascularization? Ideally, the radiologist should be familiar with the widely used Gustilo-Anderson open-fracture classification system, which was developed to prognosticate the likelihood of a functional limb salvage on the basis of soft-tissue and bone loss. When functional salvage is feasible or urgent hemorrhage control is required, communication with trauma surgeon colleagues is augmented by an understanding of the unique surgical, endovascular, and hybrid approaches available for each anatomic region of the upper and lower extremities. The radiologist should also be familiar with the common postoperative appearances of staged vascular, orthopedic, and plastic reconstructions for efficient clinically relevant reporting of potential down-range complications. Online supplemental material is available for this article. ^©RSNA, 2022.

Radiological investigation of acute mandibular injury

This article focuses on the different imaging modalities used to evaluate acute mandibular fractures and explores important concepts relating to their diagnosis, investigation, and treatment. Significant focus will be given to exploring general management principles, considerations regarding first-line imaging, and recent technological advancement. Computed tomography (CT) is the preferred method when attempting to identify acute mandibular fractures, particularly in trauma patients, and has very high specificity and sensitivity. Multidetector CT now represents the standard of care, enabling fast scan times, reduced artifact, accurate reconstructed views, and three-dimensional (3D) reconstructions. Cone-beam CT is a newer advanced imaging modality that is increasingly being used worldwide, particularly in the ambulatory and intraoperative setting. It produces high-resolution images with submillimeter isotropic voxels, 3D and multiplanar reconstruction, and low radiation dose, however is less widely available and more expensive. Ultrasound is a valuable method in identifying a fracture in unstable patients, but is limited in its ability to detect nondisplaced fractures. Magnetic resonance imaging is useful in determining the presence of soft-tissue injury. CT angiography is invaluable in the assessment of potential vascular injury in condylar fracture dislocations.

Volumetric Markers of Body Composition May Improve Personalized Prediction of Major Arterial Bleeding After Pelvic Fracture: A Secondary Analysis of the Baltimore CT Prediction Model Cohort

METHODS

This work is a retrospective secondary analysis of a single institution cohort used in the development of the Baltimore CT prediction model. The cohort includes 115 consecutive patients that underwent admission contrast-enhanced CT of the abdomen and pelvis for blunt trauma with pelvic ring disruption followed by conventional angiography. Major arterial injury requiring angioembolization served as the outcome variable. Angioembolization was required in 73/115 patients (63% of the cohort). Average age was 46.9 years (±SD 20.4). Body composition measurements were determined as 2-dimensional (2D) or 3-dimensional (3D) parameters and included mid-L3 trabecular bone attenuation, abdominal visceral fat area or volume, and percent muscle fat fraction (as a marker of sarcopenia) measured using segmentation and histogram analysis.

RESULTS

Models incorporating 2D (Model B) or 3D markers (model C) of body composition showed improvement over the original Baltimore model (model A) in all parameters of performance, quality, and fit (area under the receiver-operating curve [AUC], Akaike information criterion, Brier score, Hosmer-Lemeshow test, and adjusted-R2). Area under the receiver-operating curve increased from 0.83 (A), to 0.86 (B), and 0.88 (C). The greatest improvement was seen with 3D parameters.

CONCLUSION

Once automated, quantitative visualization tools providing "free" 3D body composition information can be expected to improve personalized precision diagnostics, outcome prediction, and decision support in patients with bleeding pelvic fractures.

Should Rib Fracture Patients be Treated at High Acuity Trauma Hospitals?

BACKGROUND

It is well known that severely injured trauma patients have better outcomes when treated at centers that routinely treat high acuity trauma. The benefits of specialty treatment for chest trauma have not been shown. We hypothesized that patients with high risk rib fractures treated in centers that care for high acuity trauma would have better outcomes than patients treated in other centers.

METHODS

All rib fracture patients were identified via the 2016 National Inpatient Sample using ICD-10 codes; Abbreviated Injury Scales (AIS) and Elixhauser comorbidity scores were also extracted. Chest AIS was grouped as mild (≤ 1) or severe (≥ 2). All patients with AIS > 2 in another body region were excluded. High acuity trauma hospitals (TH) were defined as hospitals which transferred 0% of neurotrauma patients; all other hospitals were defined as non-trauma hospitals. Poor outcome was defined as any patient who died, had a tracheostomy, developed pneumonia, or had a length of stay in the longest decile. Logistic regression with an interaction term for hospital type and chest trauma severity was performed.

RESULTS

A total of 29,780 patients with rib fractures were identified (median age 64 (IQR 51-79), 60% male), of whom 22% had poor outcomes. Fifty-three percent of patients were treated at non-trauma hospitals. In unadjusted comparisons, poor outcomes occurred more often at TH (22.4% versus 21.4%, P = 0.03). However, after adjustment, severe chest trauma that was treated at non-trauma hospitals was associated with higher odds of poor outcomes (OR 1.6, < 0.001).

DISCUSSION

More than 20% of patients with severe chest trauma have a poor outcome. Severe chest trauma outcomes are improved at TH. Development of transfer criteria for chest injuries in high-risk patients may mitigate poor outcomes at hospitals without specialized trauma expertise.

An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma

Pelvic ring disruptions result from blunt injury mechanisms and are often found in patients with multi-system trauma. To grade pelvic fracture severity in trauma victims based on whole-body CT, the Tile AO/OTA classification is frequently used. Due to the high volume of whole-body trauma CTs generated in busy trauma centers, an automated approach to Tile classification would provide substantial value, e. g., to prioritize the reading queue of the attending trauma radiologist. In such scenario, an automated method should perform grading based on a transparent process and based on interpretable features to enable interaction with human readers and lower their workload by offering insights from a first automated read of the scan. This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grade classification. The method operates similarly to human interpretation of CT scans and first detects distinct pelvic fractures on CT with high specificity using a Faster-RCNN model that are then interpreted using a structural causal model based on clinical best practices to infer an initial Tile grade. The Bayesian causal model and finally, the object detector are then queried for likely co-occurring fractures that may have been rejected initially due to the highly specific operating point of the detector, resulting in an updated list of detected fractures and corresponding final Tile grade. Our method is transparent in that it provides finding location and type using the object detector, as well as information on important counterfactuals that would invalidate the system's recommendation and achieves an AUC of 83.3%/85.1% for translational/rotational instability. Despite being designed for human-machine teaming, our approach does not compromise on performance compared to previous black-box approaches.

Splenic trauma: endovascular treatment approach

The spleen is a commonly injured organ in blunt abdominal trauma. Splenic preservation, however, is important for immune function and prevention of overwhelming infection from encapsulated organisms. Splenic artery embolization (SAE) for high-grade splenic injury has, therefore, increasingly become an important component of non-operative management (NOM). SAE decreases the blood pressure to the spleen to allow healing, but preserves splenic perfusion via robust collateral pathways. SAE can be performed proximally in the main splenic artery, more distally in specific injured branches, or a combination of both proximal and distal embolization. No definitive evidence from available data supports benefits of one strategy over the other. Particles, coils and vascular plugs are the major embolic agents used. Incorporation of SAE in the management of blunt splenic trauma has significantly improved success rates of NOM and spleen salvage. Failure rates generally increase with higher injury severity grades; however, current management results in overall spleen salvage rates of over 85%. Complication rates are low, and primarily consist of rebleeding, parenchymal infarction or abscess. Splenic immune function is felt to be preserved after embolization with no guidelines for prophylactic vaccination against encapsulated bacteria; however, a complete understanding of post-embolization immune changes remains an area in need of further investigation. This review describes the history of SAE from its inception to its current role and indications in the management of splenic trauma. The endovascular approach, technical details, and outcomes are described with relevant examples. SAE is has become an important part of a multidisciplinary strategy for management of complex trauma patients.

Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis

PURPOSE

In patients presenting with blunt hepatic injury (BHI), the utility of CT for triage to hepatic angiography remains uncertain since simple binary assessment of contrast extravasation (CE) as being present or absent has only modest accuracy for major arterial injury on digital subtraction angiography (DSA). American Association for the Surgery of Trauma (AAST) liver injury grading is coarse and subjective, with limited diagnostic utility in this setting. Volumetric measurements of hepatic injury burden could improve prediction. We hypothesized that in a cohort of patients that underwent catheter-directed hepatic angiography following admission trauma CT, a deep learning quantitative visualization method that calculates % liver parenchymal disruption (the LPD index, or LPDI) would add value to CE assessment for prediction of major hepatic arterial injury (MHAI).

METHODS

This retrospective study included adult patients with BHI between 1/1/2008 and 5/1/2017 from two institutions that underwent admission trauma CT prior to hepatic angiography (n = 73). Presence (n = 41) or absence (n = 32) of MHAI (pseudoaneurysm, AVF, or active contrast extravasation on DSA) served as the outcome. Voxelwise measurements of liver laceration were derived using an existing multiscale deep learning algorithm trained on manually labeled data using cross-validation with a 75-25% split in four unseen folds. Liver volume was derived using a pre-trained whole liver segmentation algorithm. LPDI was automatically calculated for each patient by determining the percentage of liver involved by laceration. Classification and regression tree (CART) analyses were performed using a combination of automated LPDI measurements and either manually segmented CE volumes, or CE as a binary sign. Performance metrics for the decision rules were compared for significant differences with binary CE alone (the current standard of care for predicting MHAI), and the AAST grade.

RESULTS

36% of patients (n = 26) had contrast extravasation on CT. Median [Q1-Q3] automated LPDI was 4.0% [1.0-12.1%]. 41/73 (56%) of patients had MHAI. A decision tree based on auto-LPDI and volumetric CE measurements (CEvol) had the highest accuracy (0.84, 95% CI 0.73-0.91) with significant improvement over binary CE assessment (0.68, 95% CI 0.57-0.79; p = 0.01). AAST grades at different cut-offs performed poorly for predicting MHAI, with accuracies ranging from 0.44-0.63. Decision tree analysis suggests an auto-LPDI cut-off of ≥ 12% for minimizing false negative CT exams when CE is absent or diminutive.

CONCLUSION

Current CT imaging paradigms are coarse, subjective, and limited for predicting which BHIs are most likely to benefit from AE. LPDI, automated using deep learning methods, may improve objective personalized triage of BHI patients to angiography at the point of care.

An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT

Admission trauma whole-body CT is routinely employed as a first-line diagnostic tool for characterizing pelvic fracture severity. Tile AO/OTA grade based on the presence or absence of rotational and translational instability corresponds with need for interventions including massive transfusion and angioembolization. An automated method could be highly beneficial for point of care triage in this critical time-sensitive setting. A dataset of 373 trauma whole-body CTs collected from two busy level 1 trauma centers with consensus Tile AO/OTA grading by three trauma radiologists was used to train and test a triplanar parallel concatenated network incorporating orthogonal full-thickness multiplanar reformat (MPR) views as input with a ResNeXt-50 backbone. Input pelvic images were first derived using an automated registration and cropping technique. Performance of the network for classification of rotational and translational instability was compared with that of (1) an analogous triplanar architecture incorporating an LSTM RNN network, (2) a previously described 3D autoencoder-based method, and (3) grading by a fourth independent blinded radiologist with trauma expertise. Confusion matrix results were derived, anchored to peak Matthews correlation coefficient (MCC). Associations with clinical outcomes were determined using Fisher's exact test. The triplanar parallel concatenated method had the highest accuracies for discriminating translational and rotational instability (85% and 74%, respectively), with specificity, recall, and F1 score of 93.4%, 56.5%, and 0.63 for translational instability and 71.7%, 75.7%, and 0.77 for rotational instability. Accuracy of this method was equivalent to the single radiologist read for rotational instability (74.0% versus 76.7%, p = 0.40), but significantly higher for translational instability (85.0% versus 75.1, p = 0.0007). Mean inference time was < 0.1 s per test image. Translational instability determined with this method was associated with need for angioembolization and massive transfusion (p = 0.002-0.008). Saliency maps demonstrated that the network focused on the sacroiliac complex and pubic symphysis, in keeping with the AO/OTA grading paradigm. A multiview concatenated deep network leveraging 3D information from orthogonal thick-MPR images predicted rotationally and translationally unstable pelvic fractures with accuracy comparable to an independent reader with trauma radiology expertise. Model output demonstrated significant association with key clinical outcomes.

Concurrent Thoracic Endovascular Aortic Repair and Liver Transplant: Multidisciplinary Management of Multiple Posttraumatic Lesions

Association of thoracic and abdominal injuries in patients with major trauma is common. Under emergency conditions, it is often difficult to promptly perform a certain diagnosis and identify treatment priorities of life-threatening lesions. We present the case of a young man with combined thoracic and abdominal injuries after a motorcycle accident. Primary evaluation through echography and X-ray showed fluid within the hepatorenal recess and an enlarged mediastinum. Volume load, blood transfusions, and vasoactive agents were initiated to sustain circulation. Despite hemodynamic instability, we decided to perform computed tomographic angiography (CTA) scan that revealed a high-grade traumatic aortic pseudoaneurysm, multiple and severe areas of liver contusion, and a small amount of hemoperitoneum, without active bleeding spots. The patient was successfully submitted to thoracic endovascular aortic repair (TEVAR). Immediately after the end of the successful TEVAR, signs of massive abdominal bleeding revealed. Immediate explorative laparotomy was performed showing massive hepatic hemorrhage. After liver packing and Pringle's maneuver, control of bleeding was lastly obtained with hemostatic devices and selective cross-clamping of the right hepatic artery. The patient was then transferred to intensive care unit where, despite absence of further hemorrhage, hemodynamic instability, anuria, severe lactic acidosis together with liver necrosis indices appeared. A new CTA demonstrated massive parenchymal disruption within the right lobe of the liver and multiple hematomas in the left lobe. Considering the high-grade lesions of the hepatic vascular tree and liver failure, patient was listed for emergency liver transplantation (LT). LT occurred few hours later, and patient's clinical conditions rapidly improved even if the subsequent clinical course was characterized by a severe fungal infection because of immunosuppression. Evaluation of life-threatening lesions and treatment priorities, availability of different excellence skills, and multidisciplinary collaboration have a key role to achieve clinical success in such severe cases.

Diagnostic value of CT contrast extravasation for major arterial injury after pelvic fracture: A meta-analysis

PURPOSE

We conducted a meta-analysis to determine diagnostic performance of CT intravenous contrast extravasation (CE) as a sign of angiographic bleeding and need for angioembolization after pelvic fractures.

MATERIALS AND METHODS

A systematic literature search combining the concepts of contrast extravasation, pelvic trauma, and CT yielded 206 potentially eligible studies. 23 studies provided accuracy data or sufficient descriptive data to allow 2x2 contingency table construction and provided 3855 patients for meta-analysis. Methodologic quality was assessed using the QUADAS-2 tool. Sensitivity and specificity were synthesized using bivariate mixed-effects logistic regression. Heterogeneity was assessed using the I2-statistic. Sources of heterogeneity explored included generation of scanner (64 row CT versus lower detector row) and use of multiphasic versus single phase scanning protocols.

RESULTS

Overall sensitivity and specificity were 80% (95% CI: 66-90%, I2 = 92.65%) and 93% (CI: 90-96, I2 = 89.34%), respectively. Subgroup analysis showed pooled sensitivity and specificity of 94% and 89% for 64- row CT compared to 69% and 95% with older generation scanners. CE had pooled sensitivity and specificity of 95% and 92% with the use of multiphasic protocols, compared to 74% and 94% with single-phase protocols.

CONCLUSION

The pooled sensitivity and specificity of 64-row CT was 94 and 89%. 64 row CT improves sensitivity of CE, which was 69% using lower detector row scanners. High specificity (92%) can be maintained by incorporating multiphasic scan protocols.

A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation

PURPOSE

To evaluate the feasibility of a multiscale deep learning algorithm for quantitative visualization and measurement of traumatic hemoperitoneum and to compare diagnostic performance for relevant outcomes with categorical estimation.

MATERIALS AND METHODS

This retrospective, single-institution study included 130 patients (mean age, 38 years; interquartile range, 25-50 years; 79 men) with traumatic hemoperitoneum who underwent CT of the abdomen and pelvis at trauma admission between January 2016 and April 2019. Labeled cases were separated into five combinations of training (80%) and test (20%) sets, and fivefold cross-validation was performed. Dice similarity coefficients (DSCs) were compared with those from a three-dimensional (3D) U-Net and a coarse-to-fine deep learning method. Areas under the receiver operating characteristic curve (AUCs) for a composite outcome, including hemostatic intervention, transfusion, and in-hospital mortality, were compared with consensus categorical assessment by two radiologists. An optimal cutoff was derived by using a radial basis function-based support vector machine.

RESULTS

Mean DSC for the multiscale algorithm was 0.61 ± 0.15 (standard deviation) compared with 0.32 ± 0.16 for the 3D U-Net method and 0.52 ± 0.17 for the coarse-to-fine method (P < .0001). Correlation and agreement between automated and manual volumes were excellent (Pearson r = 0.97, intraclass correlation coefficient = 0.93). The algorithm produced intuitive and explainable visual results. AUCs for automated volume measurement and categorical estimation were 0.86 and 0.77, respectively (P = .004). An optimal cutoff of 278.9 mL yielded accuracy of 84%, sensitivity of 82%, specificity of 93%, positive predictive value of 86%, and negative predictive value of 83%.

CONCLUSION

A multiscale deep learning method for traumatic hemoperitoneum quantitative visualization had improved diagnostic performance for predicting hemorrhage-control interventions and mortality compared with subjective volume estimation. Supplemental material is available for this article. © RSNA, 2020.

Multidetector CT in Vascular Injuries Resulting from Pelvic Fractures: A Primer for Diagnostic Radiologists

Pelvic vascular injuries are typically caused by high-energy trauma. The majority of these injuries are caused by motor vehicle collisions, and the rest are caused by falls and industrial or crush injuries. Pelvic vascular injuries are frequently associated with pelvic ring disruption and have a high mortality rate due to shock as a result of pelvic bleeding. Morbidity and mortality resulting from pelvic vascular injury are due to pelvic hemorrhage and resultant exsanguination, which is potentially treatable and reversible if it is diagnosed early with multidetector CT and treated promptly. The pelvic bleeding source can be arterial, venous, or osseous, and differentiating an arterial (high-pressure) bleed from a venous-osseous (low-pressure) bleed is of paramount importance in stratification for treatment. Low-pressure venous and osseous bleeds are initially treated with a pelvic binder or external fixation, while high-pressure arterial bleeds require angioembolization or surgical pelvic packing. Definitive treatment of the pelvic ring disruption includes open or closed reduction and internal fixation. Multidetector CT is important in the trauma setting to assess and characterize pelvic vascular injuries with multiphasic acquisition in the arterial and venous phases, which allows differentiation of the common vascular injury patterns. This article reviews the anatomy of the pelvic vessels and the pelvic vascular territory; discusses the multidetector CT protocols used in diagnosis and characterization of pelvic vascular injury; and describes the spectrum of pelvic vascular injuries, the differentiation of common injury patterns, mimics, and imaging pitfalls. Online supplemental material is available for this article. ^©RSNA, 2019 See discussion on this article by Dreizin.

Prevelance of the costal cartilage fracture on the computerised tomography in chest trauma

INTRODUCTION

Radiography remains limited in costal cartilage injuries, and sonography, CT and MR imaging turns out to be more sensitive in the detection of cartilage injuries. This study aims to determine the frequency of costal cartilage fractures detected in the CT images of the patients with high energy chest trauma and to evaluate the association of costal cartilage fracture with the complications of trauma.

METHODS

The CT images of 93 patients aged 18-91 years with a trauma admitted to the Emergency Department of the State Hospital between February 2019 and June 2019 were studied retrospectively. Thorax CT images of 93 patients who presented to the emergency department with blunt chest trauma with AIS > 2 were retrospectively investigated by a radiologist with a board certificate who had 15 years of experience in the field.

RESULTS

Costal cartilage fracture was identified in 39 of 93 patients with severe chest trauma. Among the 93 chest trauma patients admitted to the emergency department between February and June 2019, the prevalence of costal cartilage was calculated as 41.93%. Note that the most common costal cartilage fractures in the study group were identified in the 6th, 7th, 8th and 1st costal cartilages. Another significant relationship (p = 0.007) was found between costal cartilage calcification and cartilage fracture.

CONCLUSION

Costal cartilage fractures frequently occur in blunt thoracic trauma with multiple rib fractures and are of clinical importance as they lead to the instability of chest wall. The incidence of cartilage fractures increases in elderly patients with costal cartilage calcification.

Facial fractures: classification and highlights for a useful report

In patients with facial trauma, multidetector computed tomography is the first-choice imaging test because it can detect and characterize even small fractures and their associated complications quickly and accurately. It has helped clinical management and surgical planning, so radiologists must communicate their findings to surgeons effectively. In Le Fort fractures, there is a breach between the pterygoid plates and the posterior maxilla. These fractures are classified in three basic patterns that can be combined and associated with various complications. Conceptualized when low-speed trauma was predominant, the Le Fort classification system has become less relevant giving more importance on maxillary occlusion-bearing segments. The classification of naso-orbito-ethmoid depends on the extent of injury to the attachment of the medial canthal tendon, with possible complications like nasofrontal duct disruption. Displaced fractures of the zygomaticomaxillary complex often widen the angle of the lateral orbital wall, resulting in increased orbital volume and sometimes in enophthalmos. Severe comminution or angulation can lead to wide surgical exposure. In orbital fractures, entrapment of the inferior rectus muscles can lead to diplopia, so it is important to assess its positioning and morphology. Orbital fractures can also result in injuries to the globe or infraorbital nerve. Frontal sinus fractures that extend through the posterior sinus wall can create a communication with the anterior cranial fossa resulting in leakage of cerebrospinal fluid, intracranial bleeding. It is essential to categorize fracture patterns and highlight features that may affect fracture management in radiology reports of facial trauma.

Dual-Energy CT: A Paradigm Shift in Acute Traumatic Abdomen

Abdominal trauma, one of the leading causes of death under the age of 45, can be broadly classified into blunt and penetrating trauma, based on the mechanism of injury. Blunt abdominal trauma usually results from motor vehicle collisions, fall from heights, assaults, and sports and is more common than penetrating abdominal trauma, which is usually seen in firearm injuries and stab wounds. In both blunt and penetrating abdominal trauma, an optimized imaging approach is mandatory to exclude life-threatening injuries. Easy availability of the portable ultrasound in the emergency department and trauma bay makes it one of the most commonly used screening imaging modalities in the abdominal trauma, especially to exclude hemoperitoneum. Evaluation of the visceral and vascular injuries in a hemodynamically stable patient, however, warrants intravenous contrast-enhanced multidetector computed tomography scan. Dual-energy computed tomography with its postprocessing applications such as iodine selective imaging and virtual monoenergetic imaging can reliably depict the conspicuity of traumatic solid and hollow visceral and vascular injuries.

Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support

INTRODUCTION

Admission computed tomography (CT) is a widely used diagnostic tool for patients with pelvic fractures. In this pilot study, we hypothesized that pelvic hematoma volumes derived using a rapid automated deep learning-based quantitative visualization and measurement algorithm predict interventions and outcomes including (a) need for angioembolization (AE), pelvic packing (PP), or massive transfusion (MT), and (b) in-hospital mortality.

METHODS

We performed a single-institution retrospective analysis of 253 patients with bleeding pelvic fractures who underwent admission abdominopelvic trauma CT between 2008 and 2017. Included patients had hematoma volumes of 30 mL or greater, were 18 years and older, and underwent contrast-enhanced CT before surgical or angiographic intervention. Automated pelvic hematoma volume measurements were previously derived using a deep-learning quantitative visualization and measurement algorithm through cross-validation. A composite dependent variable of need for MT, AE, or PP was used as the primary endpoint. The added utility of hematoma volume was assessed by comparing the performance of multivariable models with and without hematoma volume as a predictor. Areas under the receiver operating characteristic curve (AUCs) and sensitivities, specificities, and predictive values were determined at clinically relevant thresholds. Adjusted odds ratios of automated pelvic hematoma volumes at 200 mL increments were derived.

RESULTS

Median age was 47 years (interquartile range, 29-61), and 70% of patients were male. Median Injury Severity Score was 22 (14-36). Ninety-four percent of patients had injuries in other body regions, and 73% had polytrauma (Injury Severity Score, ≥16). Thirty-three percent had Tile/Orthopedic Trauma Association type B, and 24% had type C pelvic fractures. A total of 109 patients underwent AE, 22 underwent PP, and 53 received MT. A total of 123 patients received all 3 interventions. Sixteen patients died during hospitalization from causes other than untreatable (abbreviated injury scale, 6) head injury. Variables incorporated into multivariable models included age, sex, Tile/Orthopedic Trauma Association grade, admission lactate, heart rate (HR), and systolic blood pressure (SBP). Addition of hematoma volume resulted in a significant improvement in model performance, with AUC for the composite outcome (AE, PP, or MT) increasing from 0.74 to 0.83 (p < 0.001). Adjusted unit odds more than doubled for every additional 200 mL of hematoma volume. Increase in model AUC for mortality with incorporation of hematoma volume was not statistically significant (0.85 vs. 0.90, p = 0.12).

CONCLUSION

Hematoma volumes measured using a rapid automated deep learning algorithm improved prediction of need for AE, PP, or MT. Simultaneous automated measurement of multiple sources of bleeding at CT could augment outcome prediction in trauma patients.

LEVEL OF EVIDENCE

Diagnostic, level IV.

Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT

The volume of pelvic hematoma at CT has been shown to be the strongest independent predictor of major arterial injury requiring angioembolization in trauma victims with pelvic fractures, and also correlates with transfusion requirement and mortality. Measurement of pelvic hematomas (unopacified extraperitoneal blood accumulated from time of injury) using semi-automated seeded region growing is time-consuming and requires trained experts, precluding routine measurement at the point of care. Pelvic hematomas are markedly variable in shape and location, have irregular ill-defined margins, have low contrast with respect to viscera and muscle, and reside within anatomically distorted pelvises. Furthermore, pelvic hematomas occupy a small proportion of the entire volume of a chest, abdomen, and pelvis (C/A/P) trauma CT. The challenges are many, and no automated methods for segmentation and volumetric analysis have been described to date. Traditional approaches using fully convolutional networks result in coarse segmentations and class imbalance with suboptimal convergence. In this study, we implement a modified coarse-to-fine deep learning approach-the Recurrent Saliency Transformation Network (RSTN) for pelvic hematoma volume segmentation. RSTN previously yielded excellent results in pancreas segmentation, where low contrast with adjacent structures, small target volume, variable location, and fine contours are also problematic. We have curated a unique single-institution corpus of 253 C/A/P admission trauma CT studies in patients with bleeding pelvic fractures with manually labeled pelvic hematomas. We hypothesized that RSTN would result in sufficiently high Dice similarity coefficients to facilitate accurate and objective volumetric measurements for outcome prediction (arterial injury requiring angioembolization). Cases were separated into five combinations of training and test sets in an 80/20 split and fivefold cross-validation was performed. Dice scores in the test set were 0.71 (SD ± 0.10) using RSTN, compared to 0.49 (SD ± 0.16) using a baseline Deep Learning Tool Kit (DLTK) reference 3D U-Net architecture. Mean inference segmentation time for RSTN was 0.90 min (± 0.26). Pearson correlation between predicted and manual labels was 0.95 with p < 0.0001. Measurement bias was within 10 mL. AUC of hematoma volumes for predicting need for angioembolization was 0.81 (predicted) versus 0.80 (manual). Qualitatively, predicted labels closely followed hematoma contours and avoided muscle and displaced viscera. Further work will involve validation using a federated dataset and incorporation into a predictive model using multiple segmented features.

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

Splenic injury is commonly encountered in severe blunt abdominal trauma. Technological improvements and the increasing availability of both diagnostic computed tomography (CT) and therapeutic splenic artery embolisation (SAE) are key factors in defining the high success rate of modern-day non-operative management (NOM) for blunt splenic injuries (BSIs). The Association for Surgery for Trauma (AAST) Organ Injury Scale (OIS) is commonly used by both radiologists and clinicians to stratify injury severity, traditionally based on the degree of parenchymal disruption seen on CT, and guide management. Its recent 2018 update takes splenic vascular injuries (i.e., active bleed, pseudoaneurysm, and traumatic arteriovenous fistulae) into consideration, the presence of which will indicate at least a grade IV (i.e., high-grade) injury. This is a reflection of the paradigm shift towards spleen conservation with regular use of SAE as the current standard of treatment. Prompted by the latest AAST OIS revision, which represents a more complete and current grading system, we present the spectrum of pertinent CT findings that the diagnostic radiologist should accurately identify and convey to the multidisciplinary trauma team (including the interventional radiologist). This review divides imaging findings based on the AAST OIS definitions and categorises them into (1) parenchymal and (2) vascular injuries. Features that may help in the detection of subtle BSIs are also described. Lastly, it touches on the key changes made to the new AAST OIS, substantiated by case illustrations.

Operative Treatment and Clinical Outcomes in Peripheral Vascular Trauma: The Combined Experience of Two Centers in the Endovascular Era

BACKGROUND

Arterial traumas of the extremities are quite rare in civilian records; nevertheless, patients with trauma of limbs are admitted daily in emergency departments worldwide. The up-to-date information about epidemiology and treatment (open vs. endovascular surgery) comes from war records and it is not always easy getting data on mortality and morbidity in these patients. The aim of this study is to analyze the approach (open or endovascular) and the outcome of patients with vascular trauma of upper limbs (from the subclavian artery) and/or lower limbs (distal to the inguinal ligament), in the greater Milan area.

METHODS

A retrospective analysis was conducted on data recorded by the emergency departments of two hospitals of the greater Milan between 2009 and 2017. We collected all patients with arterial injuries of the limbs in terms of demography, injury patterns, clinical status at admission, therapy (open or endovascular approach), and outcomes in terms of limb salvage and survival.

RESULTS

We studied 52 patients with vascular trauma of extremities. The main mechanism of trauma was road accident (48.1%), followed by criminal acts (32.7%), self-endangering behavior (13.5%), work (3.8%), and sport accidents (1.9%). Associated lesions (orthopedic, neurological, and/or venous lesions of the limbs) were present in 39 patients (75%). All patients underwent emergency surgery, forty-six patients (88.5%) by open repair (polytetrafluoroethylene or greater saphenous vein bypass grafts, arterial suture or ligation), whereas endovascular approach was used only in 6 patients (11.5%), all treated with embolization. The overall postoperative mortality rate was 5.7% (3 patients). Among survivors, we report 5 major amputations of the lower limbs, 3 of them after bypass graft infection, and 2 after graft failure. The rate of limb salvage was 90.4%.

CONCLUSIONS

Isolated arterial trauma of the extremities are rare, usually they occur in the setting of multiple trauma patients. Despite progresses in surgical techniques, there are still controversies in diagnosis and treatment of these patients. We treated most cases with open surgery (n = 46), choosing endovascular approach (embolization performed mainly by interventional radiologists) in difficult anatomic districts. We believe that, during decision-making of the surgical strategy, it is important to consider the anatomical site of lesions and the general condition of the patients. Moreover, in case of multiple trauma, we suggest a multidisciplinary approach to provide the best medical care to the victims.

Imaging Acetabular Fractures

Acetabular fractures are encountered by radiologists in a wide spectrum of practice settings. The radiologist's value in the acute and long-term management of acetabular fractures is augmented by familiarity with systematic computed tomography-based algorithms that streamline and simplify Judet-Letournel fracture typing, together with an appreciation of the role of imaging in initial triage, operative decision making, postoperative assessment, prognostication, and evaluation of complications. The steep increase in incidence of acetabular fractures in the elderly over the past several decades places special emphasis on familiarity with geriatric fracture patterns.

Review of proximal splenic artery embolization in blunt abdominal trauma

The spleen is the most commonly injured organ in blunt abdominal trauma. Unstable patients undergo laparotomy and splenectomy. Stable patients with lower grade injuries are treated conservatively; those stable patients with moderate to severe splenic injuries (grade III-V) benefit from endovascular splenic artery embolization. Two widely used embolization approaches are proximal and distal splenic artery embolization. Proximal splenic artery embolization decreases the perfusion pressure in the spleen but allows for viability of the spleen to be maintained via collateral pathways. Distal embolization can be used in cases of focal injury. In this article we review relevant literature on splenic embolization indication, and technique, comparing and contrasting proximal and distal embolization. Additionally, we review relevant anatomy and discuss collateral perfusion pathways following proximal embolization. Finally, we review potential complications of splenic artery embolization.

CT Prediction Model for Major Arterial Injury after Blunt Pelvic Ring Disruption

Purpose To develop and test a computed tomography (CT)-based predictive model for major arterial injury after blunt pelvic ring disruptions that incorporates semiautomated pelvic hematoma volume quantification. Materials and Methods A multivariable logistic regression model was developed in patients with blunt pelvic ring disruptions who underwent arterial phase abdominopelvic CT before angiography from 2008 to 2013. Arterial injury at angiography requiring transarterial embolization (TAE) served as the outcome. Areas under the receiver operating characteristic (ROC) curve (AUCs) for the model and for two trauma radiologists were compared in a validation cohort of 36 patients from 2013 to 2015 by using the Hanley-McNeil method. Hematoma volume cutoffs for predicting the need for TAE and probability cutoffs for the secondary outcome of mortality not resulting from closed head injuries were determined by using ROC analysis. Correlation between hematoma volume and transfusion was assessed by using the Pearson coefficient. Results Independent predictor variables included hematoma volume, intravenous contrast material extravasation, atherosclerosis, rotational instability, and obturator ring fracture. In the validation cohort, the model (AUC, 0.78) had similar performance to reviewers (AUC, 0.69-0.72; P = .40-.80). A hematoma volume cutoff of 433 mL had a positive predictive value of 87%-100% for predicting major arterial injury requiring TAE. Hematoma volumes correlated with units of packed red blood cells transfused (r = 0.34-0.57; P = .0002-.0003). Predicted probabilities of 0.64 or less had a negative predictive value of 100% for excluding mortality not resulting from closed head injuries. Conclusion A logistic regression model incorporating semiautomated hematoma volume segmentation produced objective probability estimates of major arterial injury. Hematoma volumes correlated with 48-hour transfusion requirement, and low predicted probabilities excluded mortality from causes other than closed head injury. ^© RSNA, 2018 Online supplemental material is available for this article.

Quantitative MDCT assessment of binder effects after pelvic ring disruptions using segmented pelvic haematoma volumes and multiplanar caliper measurements

OBJECTIVE

To assess effects of pelvic binders for different instability grades using quantitative multidetector computed tomography (MDCT) parameters including segmented pelvic haematoma volumes and multiplanar caliper measurements.

METHODS

CT examinations of 49 patients with binders and 49 controls performed from January 2008-June 2016, and matched 1:1 for Tile instability grade and Pennal/Young-Burgess force vector, were compared for differences in pubic symphysis and sacroiliac displacement using caliper measurements in three orthogonal planes. Pelvic haematoma volumes (ml) were derived using semi-automated seeded region-growing segmentation. Median caliper measurements and volumes were compared using the Mann-Whitney U test, and correlations assessed with Pearson's correlation coefficient. Relevant caliper measurement cutoffs were established using ROC analysis.

RESULTS

Rotationally unstable (Tile B) patients with binders showed significant decreases in sacroiliac diastasis (2.7 mm vs. 4.5 mm; p=0.003) and haematoma volumes (135 ml vs. 295 ml; p=0.008). Globally unstable (Tile C) binder patients showed decreased sacroiliac diastasis (4.7 mm vs. 6.4 mm, p=0.04), without significant difference in haematoma volumes (284 ml vs. 234 ml, p=0.34). Four Tile C patients with binders demonstrated over-reduction resulting in pubic body over-ride.

CONCLUSION

Rotationally unstable patients with binders have significantly less sacroiliac diastasis versus controls, corresponding with significantly lower haematoma volumes.

KEY POINTS

• Haematoma segmentation and multiplanar caliper measurements provide new insights into binder effects. • Binder reduction corresponds with decreased pelvic haematoma volume in rotationally unstable injuries. • Discrimination between rotational and global instability is important for management. • Several caliper measurement cut-offs discriminate between rotationally and globally unstable injuries. • Pubic symphysis over-ride is suggestive of binder over-reduction in globally unstable injuries.

Incidence of traumatic carotid and vertebral artery dissections: results of cervical vessel computed tomography angiogram as a mandatory scan component in severely injured patients

Purpose

The aim of this study was to evaluate the true incidence of cervical artery dissections (CeADs) in trauma patients with an Injury Severity Score (ISS) of ≥16, since head-and-neck computed tomography angiogram (CTA) is not a compulsory component of whole-body trauma computed tomography (CT) protocols.

Patients and methods

A total of 230 consecutive trauma patients with an ISS of ≥16 admitted to our Level I trauma center during a 24-month period were prospectively included. Standardized whole-body CT in a 256-detector row scanner included a head-and-neck CTA. Incidence, mortality, patient and trauma characteristics, and concomitant injuries were recorded and analyzed retrospectively in patients with carotid artery dissection (CAD) and vertebral artery dissection (VAD).

Results

Of the 230 patients included, 6.5% had a CeAD, 5.2% had a CAD, and 1.7% had a VAD. One patient had both CAD and VAD. For both, CAD and VAD, mortality is 25%. One death was caused by fatal cerebral ischemia due to high-grade CAD. A total of 41.6% of the patients with traumatic CAD and 25% of the patients with VAD had neurological sequelae.

Conclusion

Mandatory head-and-neck CTA yields higher CeAD incidence than reported before. We highly recommend the compulsory inclusion of a head-and-neck CTA to whole-body CT routines for severely injured patients.

Multidetector CT of Mandibular Fractures, Reductions, and Complications: A Clinically Relevant Primer for the Radiologist

After the nasal bones, the mandible is the second most common site of facial fractures, and mandibular fractures frequently require open reduction. In the trauma injury setting, multidetector computed tomography (CT) has become the cornerstone imaging modality for determining the most appropriate treatment management, fixation method, and surgical approach. Multidetector CT is also used to assess the adequacy of the reduction and evaluate potential complications in the postoperative period. For successful restoration of the mandible's form and function, as well as management of posttraumatic and postoperative complications, reconstructive surgeons are required to have a detailed understanding of mandibular biomechanics, occlusion, and anatomy. To provide added value in the diagnosis, treatment planning, and follow-up of mandibular fractures, radiologists should be aware of these concepts. Knowledge of the techniques commonly used to achieve occlusal and anatomic reduction and of the rationale behind the range of available treatment options for different injury patterns-from isolated and nondisplaced fractures to multisite and comminuted fractures-also is essential. This article focuses on the use of multidetector CT for pre- and postoperative evaluation of mandibular fractures and outlines fundamental concepts of diagnosis and management-beginning with an explanation of common fracture patterns and their biomechanical underpinnings, and followed by a review of the common postoperative appearances of these fractures after semirigid and rigid fixation procedures. Specific considerations regarding fractures in different regions of the tooth-bearing and non-tooth-bearing mandible and the unique issues pertaining to the edentulous atrophic mandible are reviewed, and key features that distinguish major from minor complications are described. (©)RSNA, 2016.