Volumetric analysis of pelvic hematomas after blunt trauma using semi-automated seeded region growing segmentation: a method validation study

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.

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.

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.

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.

Blunt splenic injury: Assessment of follow-up CT utility using quantitative volumetry

Purpose:

Trials of non-operative management (NOM) have become the standard of care for blunt splenic injury (BSI) in hemodynamically stable patients. However, there is a lack of consensus regarding the utility of follow-up CT exams and relevant CT features. The purpose of this study is to determine imaging predictors of splenectomy on follow-up CT using quantitative volumetric measurements.

Methods:

Adult patients who underwent a trial of non-operative management (NOM) with follow-up CT performed for BSI between 2017 and 2019 were included (n = 51). Six patients (12% of cohort) underwent splenectomy; 45 underwent successful splenic salvage. Voxelwise measurements of splenic laceration, hemoperitoneum, and subcapsular hematoma were derived from portal venous phase images of admission and follow-up scans using 3D slicer. Presence/absence of pseudoaneurysm on admission and follow-up CT was assessed using arterial phase images. Multivariable logistic regression was used to determine independent predictors of decision to perform splenectomy.

Results:

Factors significantly associated with splenectomy in bivariate analysis incorporated in multivariate logistic regression included final hemoperitoneum volume (p = 0.003), final subcapsular hematoma volume (p = 0.001), change in subcapsular hematoma volume between scans (p = 0.09) and new/persistent pseudoaneurysm (p = 0.003). Independent predictors of splenectomy in the logistic regression were final hemoperitoneum volume (unit OR = 1.43 for each 100 mL change; 95% CI: 0.99–2.06) and new/persistent pseudoaneurysm (OR = 160.3; 95% CI: 0.91–28315.3). The AUC of the model incorporating both variables was significantly higher than AAST grading (0.91 vs. 0.59, p = 0.025). Mean combined effective dose for admission and follow up CT scans was 37.4 mSv.

Conclusion:

Follow-up CT provides clinically valuable information regarding the decision to perform splenectomy in BSI patients managed non-operatively. Hemoperitoneum volume and new or persistent pseudoaneurysm at follow-up are independent predictors of splenectomy.

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.

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.

3D morphological classification of the nasolacrimal duct: Anatomical study for planning treatment of tear drainage obstruction

BACKGROUND

The purpose of this study is to analyze and classify morphological features of the nasolacrimal duct (NLD) through 3D reconstruction to help understand the causes and treatment of NLD obstruction.

METHODS

In this study, we included 63 males and 55 females who underwent autopsy without NLD obstruction with ages ranging from 20 to 78 years. The NLD was defined from the lacrimal fossa to the opening of the BNLD to the inferior meatus, and all continuous CT images showing the NLD were selected. Segmentation was performed semi-automatically, and the reconstruction and measurement of NLD was performed using the Mimics program.

RESULTS

Overall NLD length, bony nasolacrimal duct (BNLD) length, anteroposterior and transverse diameters at the entrance to the BNLD, anteroposterior and transverse smallest diameters of the BNLD, BNLD volume, and lacrimal sac BNLD angle were significantly higher in males than females (p < .05). BNLD direction in the coronal plane was slightly more likely to be inward. The most common type in both sexes was cylinder type (42.0%), males were more likely to have lower-thicker types (34.1%), and females more likely to have upper-thicker types (22.7%).

CONCLUSION

There were sex differences in NLD measurements, and females had significantly smaller NLDs. These results may partially explain the increased prevalence of primary acquired NLD obstruction in females. The BNLD tends toward the midline, and inclines posteriorly.

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.

The size of pelvic hematoma can be a predictive factor for angioembolization in hemodynamically unstable pelvic trauma

Purpose

Unstable pelvic fracture with bleeding can be fatal, with a mortality rate of up to 40%. Therefore, early detection and treatment are important in unstable pelvic trauma. We investigated the early predictive factors for possible embolization in patients with hemodynamically unstable pelvic trauma.

Methods

From January 2011 to December 2013, 46 patients with shock arrived at a single hospital within 24 hours after injury. Of them, 44 patients underwent CT scan after initial resuscitation, except for 2 who were dead on arrival. Nine patients with other organ injuries were excluded. Seventeen patients underwent embolization. A single radiologist measured the width (longest length in axial view) and length (longest length in coronal view) of pelvic hematoma on CT scans. Demographic, clinical, and radiological data were reviewed retrospectively.

Results

Among 35 patients with hemodynamically unstable pelvic fracture, 22 (62.9%) were men. Width (P = 0.002) and length (P = 0.006) of hematoma on CT scans were significantly different between the embolization and nonembolization groups. The predictors of embolization were width of pelvic hematoma (odds ratio [OR], 1.07; P = 0.028) and female sex (OR, 10.83; P = 0.031). The cutoff value was 3.35 cm. More embolization was performed (OR, 12.00; P = 0.003) and higher mortality was observed in patients with hematoma width >3.35 cm (OR, 4.96; P = 0.048).

Conclusion

Patients with hemodynamically unstable pelvic trauma have a high mortality rate. CT is useful for the initial identification of the need for embolization among these patients. The width of pelvic hematoma can predict possible embolization in patients with unstable pelvic trauma.

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.

A comparison of segmented abdominopelvic fluid volumes with conventional CT signs of abdominal compartment syndrome in a trauma population

PURPOSE

To compare the utility of abdominopelvic fluid volume measurements with established computed tomography signs for refractory post-traumatic abdominal compartment syndrome.

METHODS

This retrospective observational cohort study included 64 consecutive adult trauma patients with preoperative CT and diagnosis of refractory abdominal compartment syndrome requiring decompressive laparotomy at a level I trauma referral center between 2004 and 2014. We hypothesized that abdominal fluid volume measurements would be more predictive of the need for early laparotomy than previously described conventional CT signs of refractory ACS. Abdominopelvic fluid volumes were determined quantitatively using semi-automated segmentation software. The following conventional imaging parameters were recorded: abdominal anteroposterior:transverse ratio (round belly sign); infrahepatic vena cava diameter; distal abdominal aortic diameter; largest single small bowel wall diameter; hydronephrosis, inguinal herniation; and mesenteric and body wall edema. For outcome analysis, patients were stratified into two groups: those who underwent early (< 24 h) and late (≥ 24 h) decompressive laparotomy following CT. Correlation analysis, comparison of means, and multivariate logistic regression were performed.

RESULTS

Abdominal fluid volumes (p = 0.001) and anteroposterior:transverse ratio (p = 0.009) were increased and inferior vena cava diameter (p = 0.009) was decreased in the early decompressive laparotomy group. Multivariate analysis including conventional CT variables, fluid volumes, and laboratory values revealed abdominal fluid volumes (p = 0.012; Δ in log odds of 1.002/mL) as the only independent predictor of early decompressive laparotomy.

CONCLUSIONS

Segmented abdominopelvic free fluid volumes had greater predictive utility for decision to perform early decompressive laparotomy than previously described ACS-related CT signs in trauma patients who developed refractory abdominal compartment syndrome.

Impact of three-dimensional visualization technology on surgical strategies in complex hepatic cancer

Surgical resection is still the mainstay of treatment for primary liver cancer (PLC). It is unclear whether three-dimensional visualization (3DV) preoperative evaluation and simulated liver resection would affect the surgical strategies and improve the R0 resection rates of patients with complex PLC when compared with the 2D evaluation using computed tomography or magnetic resonance imaging. In the study, patients with complex PLC who were subjected to laparotomy underwent both 2D and 3DV evaluation before operation. A comparison between the 2D and 3DV evaluation was compared with the gold standard of laparotomy findings. In this study, of 335 patients with complex PLC, 71 were assessed to have resectable tumors. 2D and 3DV assessments determined 63 and 71 patients to have resectable PLC, respectively. At laparotomy 69 of the 71 patients were found to have resectable PLC, but 2 patients were found to be unresectable because of detection of metastatic lesions on laparotomy, which were not detected either by 2D or 3DV preoperative evaluation. The accuracy, false positive and false negative rates of the 2D and the 3DV preoperative assessments in determining tumor resectability were 85.9%, 2.8%, 11.3%, and 97.2% (p < 0.05), 2.8%, 0%, respectively. The 3DV and 2D preoperative evaluation revealed 17 and 13 patients with vascular anomalies, respectively. There were 4 patients with major vascular anomalies not detected by 2D evaluation, whose surgical strategies were modified by 3DV evaluation. These results suggested 3DV preoperative assessment could lead to better in evaluating tumor resectability, with potential benefit in the modification of surgical strategy for patients with complex PLC.

Abdominopelvic bleed rate on admission CT correlates with mortality and transfusion needs in the setting of blunt pelvic fractures: a single institution pilot study

PURPOSE

The objectives of this study were to calculate the total volumetric rate of abdominopelvic bleeding in patients with acute pelvic fractures and examine the relationships between the bleeding rate, patient outcomes, and required patient interventions.

METHODS

This was a retrospective cohort study which included 29 patients from a 4-year period (May 2013 to May 2017). Patients with acute pelvic fractures and active bleeding detected on CT with two phases of imaging were included. Software was used to measure the volume of active bleeding on arterial and parenchymal phases. The active bleeding rate was calculated by dividing the change in active bleeding volume by the time between the two phases. The total volumetric bleed rate from all sites was then computed. Clinical variables were compared between survivors and non-survivors.

RESULTS

Overall mortality in this cohort was 21% (n = 6). The mean abdominopelvic volumetric bleed rate in non-survivors was much greater than survivors (40.7 cc/min vs. 5.7 cc/min; p < 0.01). Ninety-six percent of survivors had an abdominopelvic bleed rate < 20 cc/min compared to 33% of non-survivors. An abdominopelvic bleed rate > 20 cc/min was associated with a mortality rate of 80% while a rate of < 20 cc/min was associated with a 92% survival rate. The mean pelvic hematoma volume was greater in non-survivors compared to survivors (1854 cc vs. 746 cc; p < 0.01). There was a positive association between hematoma volume and units of blood transfused (r_s = 0.4, n = 29, p = 0.04).

CONCLUSION

An abdominopelvic bleeding rate > 20 cc/min was associated with a high risk of mortality.

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.