MDCT of complications and common postoperative findings following penetrating torso trauma

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.

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.

Pancreatic Trauma: Imaging Review and Management Update

Traumatic injuries of the pancreas are uncommon and often difficult to diagnose owing to subtle imaging findings, confounding multiorgan injuries, and nonspecific clinical signs. Nonetheless, early diagnosis and treatment are critical, as delays increase morbidity and mortality. Imaging has a vital role in diagnosis and management. A high index of suspicion, as well as knowledge of the anatomy, mechanism of injury, injury grade, and role of available imaging modalities, is required for prompt accurate diagnosis. CT is the initial imaging modality of choice, although the severity of injury can be underestimated and assessment of the pancreatic duct is limited with this modality. The time from injury to definitive diagnosis and the treatment of potential pancreatic duct injury are the primary factors that determine outcome following pancreatic trauma. Disruption of the main pancreatic duct (MPD) is associated with higher rates of complications, such as abscess, fistula, and pseudoaneurysm, and is the primary cause of pancreatic injury-related mortality. Although CT findings can suggest pancreatic duct disruption according to the depth of parenchymal injury, MR cholangiopancreatography and endoscopic retrograde cholangiopancreatography facilitate direct assessment of the MPD. Management of traumatic pancreatic injury depends on multiple factors, including mechanism of injury, injury grade, presence (or absence) of vascular injury, hemodynamic status of the patient, and associated organ damage. ^©RSNA, 2020 See discussion on this article by Patlas.