CT of Periarticular Adult Knee Fractures: Classification and Management Implications.

Periarticular knee fractures, which include fractures of the distal femur, tibial plateau, and patella, account for 5%-10% of musculoskeletal injuries encountered in trauma centers and emergency rooms. These injuries are frequently complex, with articular surface involvement. Surgical principles center on reconstruction of the articular surface as well as restoration of limb length, alignment, and rotation to reestablish functional knee biomechanics. Fixation principles are guided by fracture morphology, and thus, CT with multiplanar reformats and volume rendering is routinely used to help plan surgical intervention. Fractures involving the distal femur, tibial plateau, and patella have distinct management considerations. This comprehensive CT primer of periarticular knee fractures promotes succinct and clinically relevant reporting as well as optimized communication with orthopedic trauma surgeon colleagues by tying fracture type and key CT findings with surgical decision making. Fracture patterns are presented within commonly employed fracture classification systems, rooted in specific biomechanical principles. Fracture typing of distal femur fractures and patellar fractures is performed using Arbeitsgemeinschaft für Osteosynthesefragen/Orthopedic Trauma Association (AO/OTA) classification schemes. Tibial plateau fractures are graded using the Schatzker system, informed by a newer explicitly CT-based three-column concept. For each anatomic region, the fracture pattern helps determine the surgical access required, whether bone grafting is warranted, and the choice of hardware that achieves suitable functional outcomes while minimizing the risk of articular collapse and accelerated osteoarthritis. Emphasis is also placed on recognizing bony avulsive patterns that suggest ligament injury to help guide stress testing in the early acute period. ©RSNA, 2024 Supplemental material is available for this article.

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.

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.

A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations.

PURPOSE: There is a growing body of diagnostic performance studies for emergency radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is known about user preferences, concerns, experiences, expectations, and the degree of penetration of AI tools in emergency radiology. Our aim is to conduct a survey of the current trends, perceptions, and expectations regarding AI among American Society of Emergency Radiology (ASER) members. METHODS: An anonymous and voluntary online survey questionnaire was e-mailed to all ASER members, followed by two reminder e-mails. A descriptive analysis of the data was conducted, and results summarized. RESULTS: A total of 113 members responded (response rate 12%). The majority were attending radiologists (90%) with greater than 10 years' experience (80%) and from an academic practice (65%). Most (55%) reported use of commercial AI CAD tools in their practice. Workflow prioritization based on pathology detection, injury or disease severity grading and classification, quantitative visualization, and auto-population of structured reports were identified as high-value tasks. Respondents overwhelmingly indicated a need for explainable and verifiable tools (87%) and the need for transparency in the development process (80%). Most respondents did not feel that AI would reduce the need for emergency radiologists in the next two decades (72%) or diminish interest in fellowship programs (58%). Negative perceptions pertained to potential for automation bias (23%), over-diagnosis (16%), poor generalizability (15%), negative impact on training (11%), and impediments to workflow (10%). CONCLUSION: ASER member respondents are in general optimistic about the impact of AI in the practice of emergency radiology and its impact on the popularity of emergency radiology as a subspecialty. The majority expect to see transparent and explainable AI models with the radiologist as the decision-maker.

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.

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.

Dual-Energy CT and Cinematic Rendering to Improve Assessment of Pelvic Fracture Instability.

Background Grading of pelvic fracture instability is challenging in patients with pelvic binders. Dual-energy CT (DECT) and cinematic rendering can provide ancillary information regarding osteoligamentous integrity, but the utility of these tools remains unknown. Purpose To assess the added diagnostic value of DECT and cinematic rendering, with respect to single-energy CT (SECT), for discriminating any instability and translational instability in patients with pelvic binders. Materials and Methods In this retrospective analysis, consecutive adult patients (age ≥18 years) were stabilized with pelvic binders and scanned in dual-energy mode using a 128-section CT scanner at one level I trauma center between August 2016 and January 2019. Young-Burgess grading by orthopedists served as the reference standard. Two radiologists performed blinded consensus grading with the Young-Burgess system in three reading sessions (session 1, SECT; session 2, SECT plus DECT; session 3, SECT plus DECT and cinematic rendering). Lateral compression (LC) type 1 (LC-1) and anteroposterior compression (APC) type 1 (APC-1) injuries were considered stable; LC type 2 and APC type 2, rotationally unstable; and LC type 3, APC type 3, and vertical shear, translationally unstable. Diagnostic performance for any instability and translational instability was compared between reading sessions using the McNemar and DeLong tests. Radiologist agreement with the orthopedic reference standard was calculated with the weighted κ statistic. Results Fifty-four patients (mean age, 41 years ± 16 [SD]; 41 men) were analyzed. Diagnostic performance was greater with SECT plus DECT and cinematic rendering compared with SECT alone for any instability, with an area under the receiver operating characteristic curve (AUC) of 0.67 for SECT alone and 0.82 for SECT plus DECT and cinematic rendering (P = .04); for translational instability, the AUCs were 0.80 for SECT alone and 0.95 for SECT plus DECT and cinematic rendering (P = .01). For any instability, corresponding sensitivities were 61% (22 of 36 patients) for SECT alone and 86% (31 of 36 patients) for SECT plus DECT and cinematic rendering (P < .001). The corresponding specificities were 72% (13 of 18 patients) and 78% (14 of 18 patients), respectively (P > .99). Agreement (κ value) between radiologists and orthopedist reference standard improved from 0.44 to 0.76 for SECT versus the combination of SECT, DECT, and cinematic rendering. Conclusion Combined use of single-energy CT, dual-energy CT, and cinematic rendering improved instability assessment over that with single-energy CT alone. © RSNA, 2022 Online supplemental material is available for this article.

Improving Sensitivity of Arterial Spin Labeling Perfusion MRI in Alzheimer's Disease Using Transfer Learning of Deep Learning-Based ASL Denoising.

BACKGROUND: Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) denoising through deep learning (DL) often faces insufficient training data from patients. One solution is to train DL models using healthy subjects' data which are more widely available and transfer them to patients' data. PURPOSE: To evaluate the transferability of a DL-based ASL MRI denoising method (DLASL). STUDY TYPE: Retrospective. SUBJECTS: Four hundred and twenty-eight subjects (189 females) from three cohorts. FIELD STRENGTH/SEQUENCE: 3 T two-dimensional (2D) echo-planar imaging (EPI)-based pseudo-continuous ASL (PCASL) and 2D EPI-based pulsed ASL (PASL) sequences. ASSESSMENT: DLASL was trained using young healthy adults' PCASL data (Dataset 1: 250/30 subjects as training/validation set) and was directly transferred (DTF) to PCASL data from Dataset 2 (45 subjects test set) of normal controls (NC) and Alzheimer's disease (AD) groups. DLASL was fine-tuned (DLASLFT) and tested on PASL data from Dataset 3 (103 subjects test set) of NC and AD. An existing non-DL method (NonDL) was used for comparison. Cerebral blood flow (CBF) images from ASL MRI were compared between NC and AD to assess characteristic hypoperfusion (lower CBF) patterns in AD. CBF image quality and CBF map sensitivity for detecting hypoperfusion using peak t-value and suprathreshold cluster size are outcome measures. STATISTICAL TESTS: Paired t-test, two-sample t-test, one-way analysis of variance, and Tukey honestly significant difference, and linear mixed-effects models were used. P < 0.05 was considered statistically significant. RESULTS: Mean contrast-to-noise ratio (CNR) of Dataset 2 showed that DTF outperformed NonDL (AD: 3.38 vs. 2.64, NC: 3.80 vs. 3.36). On Dataset 3, DLASLFT outperformed NonDL measured by mean CNR (AD: 2.45 vs. 1.87, NC: 2.54 vs. 2.17) and mean radiologic score (2.86 vs. 2.44). Image quality improvement was significant on both test sets. DTF and DLASLFT improved sensitivity for detecting AD-related hypoperfusion patterns compared with NonDL. DATA CONCLUSION: We demonstrated the DLASL's transferability across different ASL sequences and different populations. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

CT of Sacral Fractures: Classification Systems and Management.

CT is often performed as part of a whole-body protocol in the setting of polytrauma and is the standard of care for diagnosing and characterizing sacral fractures. These fractures are not uncommon, occurring in conjunction with pelvic ring disruption in approximately 40%-50% of patients. Knowledge of basic functional anatomy and fracture biomechanics is important in understanding sacral fracture patterns, which only rarely result from direct impact. More often, sacral fractures result from an indirect mechanism with fracture lines that propagate along relative lines of weakness, leading to predictable fracture patterns. Each fracture pattern has implications with respect to neurologic injury, spinopelvic stability, management, and potential complications. The authors explore the Denis, Roy-Camille, Isler, Robles, Sabiston-Wing, and shape-based classification systems for sacral fractures. These form the basis of the subsequently discussed unified AOSpine sacral fracture classification, a consensus system developed by spine and orthopedic surgeons as a means of improving and standardizing communication. The AOSpine sacral fracture classification also includes clinical designations for neurologic status and patient-specific modifiers. When a patient is unexaminable owing to obtundation or sedation, CT is an invaluable indirect marker of nerve compression or traction injury. It also plays an important role in visualizing and characterizing the type and extent of any associated soft-tissue injuries that may warrant a delay in surgery or an alternative operative approach. ©RSNA, 2022.

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.

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.

CT of Skull Base Fractures: Classification Systems, Complications, and Management.

As advances in prehospital and early hospital care improve survival of the head-injured patient, radiologists are increasingly charged with understanding the myriad skull base fracture management implications conferred by CT. Successfully parlaying knowledge of skull base anatomy and fracture patterns into precise actionable clinical recommendations is a challenging task. The authors aim to provide a pragmatic overview of CT for skull base fractures within the broader context of diagnostic and treatment planning algorithms. Laterobasal, frontobasal, and posterior basal fracture patterns are emphasized. CT often plays a complementary, supportive, or confirmatory role in management of skull base fractures in conjunction with results of physical examination, laboratory testing, and neurosensory evaluation. CT provides prognostic information about short- and long-term risk of cerebrospinal fluid (CSF) leak, encephalocele, meningitis, facial nerve paralysis, hearing and vision loss, cholesteatoma, vascular injuries, and various cranial nerve palsies and syndromes. The radiologist should leverage understanding of specific strengths and limitations of CT to anticipate next steps in the skull base fracture management plan. Additional imaging is warranted to clarify ambiguity (particularly for potential sources of CSF leak); in other cases, clinical and CT criteria alone are sufficient to determine the need for intervention and the choice of surgical approach. The radiologist should be able to envision stepping into a multidisciplinary planning discussion and engaging neurotologists, neuro-ophthalmologists, neurosurgeons, neurointerventionalists, and facial reconstructive surgeons to help synthesize an optimal management plan after reviewing the skull base CT findings at hand. Online supplemental material is available for this article. ©RSNA, 2021.

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.

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.

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.

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.

Iodine-based Dual-Energy CT of Traumatic Hemorrhagic Contusions: Relationship to In-Hospital Mortality and Short-term Outcome.

BackgroundTraumatic hemorrhagic contusions are associated with iodine leak; however, quantification of leakage and its importance to outcome is unclear.PurposeTo identify iodine-based dual-energy CT variables that correlate with in-hospital mortality and short-term outcomes for contusions at hospital discharge.Materials and MethodsIn this retrospective study, consecutive patients with contusions from May 2016 through January 2017 were analyzed. Two radiologists evaluated CT variables from unenhanced admission head CT and follow-up head dual-energy CT scans obtained after contrast material-enhanced whole-body CT. The outcomes evaluated were in-hospital mortality, Rancho Los Amigos scale (RLAS) score, and disability rating scale (DRS) score. Logistic regression and linear regression were used to develop prediction models for categorical and continuous outcomes, respectively.ResultsThe study included 65 patients (median age, 48 years; interquartile range, 25-65.5 years); 50 were men. Dual-energy CT variables that correlated with mortality, RLAS score, and DRS score were iodine concentration, pseudohematoma volume, iodine quantity in pseudohematoma, and iodine quantity in contusion. The single-energy CT variable that correlated with mortality, RLAS score, and DRS score was hematoma volume at follow-up CT. Multiple logistic regression analysis after inclusion of clinical variables identified two predictors that enabled determination of mortality: postresuscitation Glasgow coma scale (P-GCS) (adjusted odds ratio, 0.42; 95% confidence interval [CI]: 0.2, 0.86; P = 0.01) and iodine quantity in pseudohematoma (adjusted odds ratio, 1.4 per milligram; 95% CI: 1.02 per milligram, 1.9 per milligram; P = 0.03), with a mean area under the receiver operating characteristic curve of 0.96 ± 0.05 (standard error). For RLAS, the predictors were P-GCS (mean coefficient, 0.32 ± 0.06; P < .001) and iodine quantity in contusion (mean coefficient, -0.04 per milligram ± 0.02; P = 0.01). Predictors for DRS were P-GCS (mean coefficient, -1.15 ± 0.27; P < .001), age (mean coefficient, 0.13 per year ± 0.04; P = .002), and iodine quantity in contusion (mean coefficient, 0.19 per milligram ± 0.07; P = .02).ConclusionIodine-based dual-energy CT variables correlate with in-hospital mortality and short-term outcomes for contusions at hospital discharge.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Talbott and Hess in this issue.

Digital Breast Tomosynthesis: Physics, Artifacts, and Quality Control Considerations.

As digital breast tomosynthesis (DBT) becomes widely used, radiologists must understand the basic principles of (a) image acquisition, (b) artifacts, and (c) quality control (QC) that are specific to DBT. Standard acquisition parameters common to both full-field digital mammography (FFDM) and DBT are combinations of x-ray tube voltage, current exposure time, and anode target and filter combinations. Image acquisition parameters specific to DBT include tube motion, sweep angle, and number of projections. Continuous tube motion or x-ray emission decreases imaging time but leads to focal spot blurring when compared with step-and-shoot techniques. The sweep angle and number of projections determines resolution. Wider sweep angles allow greater out-of-plane (z-axis) resolution, improving visualization of masses and architecture distortion. A greater number of projections increases in-plane or x-y axis resolution, improving visualization of microcalcifications. Artifacts related to DBT include blurring-ripple, truncation, and loss of skin and superficial tissue resolution. Motion artifacts are difficult to recognize because of inherent out-of-plane blurring. To maintain optimal image quality and an "as low as reasonably achievable" (ALARA) radiation dose, regular QC must be performed. DBT is considered a new imaging modality; therefore, breast imaging facilities are required to obtain a separate certification in addition to that in FFDM, and all personnel (radiologists, technologists, and medical physicists) are mandated to complete initial DBT training and maintain appropriate continuing medical education credits. ©RSNA, 2019.

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.