Search

Purpose To create and validate a computer system with which to detect, localize, and classify compression fractures and measure bone density of thoracic and lumbar vertebral bodies on computed tomographic (CT) images. Materials and Methods Institutional review board approval was obtained, and informed consent was waived in this HIPAA-compliant retrospective study. A CT study set of 150 patients (mean age, 73 years; age range, 55-96 years; 92 women, 58 men) with (n = 75) and without (n = 75) compression fractures was assembled. All case patients were age and sex matched with control subjects. A total of 210 thoracic and lumbar vertebrae showed compression fractures and were electronically marked and classified by a radiologist. Prototype fully automated spinal segmentation and fracture detection software were then used to analyze the study set. System performance was evaluated with free-response receiver operating characteristic analysis. Results Sensitivity for detection or localization of compression fractures was 95.7% (201 of 210; 95% confidence interval [CI]: 87.0%, 98.9%), with a false-positive rate of 0.29 per patient. Additionally, sensitivity was 98.7% and specificity was 77.3% at case-based receiver operating characteristic curve analysis. Accuracy for classification by Genant type (anterior, middle, or posterior height loss) was 0.95 (107 of 113; 95% CI: 0.89, 0.98), with weighted κ of 0.90 (95% CI: 0.81, 0.99). Accuracy for categorization by Genant height loss grade was 0.68 (77 of 113; 95% CI: 0.59, 0.76), with a weighted κ of 0.59 (95% CI: 0.47, 0.71). The average bone attenuation for T12-L4 vertebrae was 146 HU ± 29 (standard deviation) in case patients and 173 HU ± 42 in control patients; this difference was statistically significant (P < .001). Conclusion An automated machine learning computer system was created to detect, anatomically localize, and categorize vertebral compression fractures at high sensitivity and with a low false-positive rate, as well as to calculate vertebral bone density, on CT images. © RSNA, 2017 Online supplemental material is available for this article.

PurposeTo design and validate a fully automated computer system for the detection and anatomic localization of traumatic thoracic and lumbar vertebral body fractures at computed tomography (CT).Materials and methodsThis retrospective study was HIPAA compliant. Institutional review board approval was obtained, and informed consent was waived. CT examinations in 104 patients (mean age, 34.4 years; range, 14-88 years; 32 women, 72 men), consisting of 94 examinations with positive findings for fractures (59 with vertebral body fractures) and 10 control examinations (without vertebral fractures), were performed. There were 141 thoracic and lumbar vertebral body fractures in the case set. The locations of fractures were marked and classified by a radiologist according to Denis column involvement. The CT data set was divided into training and testing subsets (37 and 67 subsets, respectively) for analysis by means of prototype software for fully automated spinal segmentation and fracture detection. Free-response receiver operating characteristic analysis was performed.ResultsTraining set sensitivity for detection and localization of fractures within each vertebra was 0.82 (28 of 34 findings; 95% confidence interval [CI]: 0.68, 0.90), with a false-positive rate of 2.5 findings per patient. The sensitivity for fracture localization to the correct vertebra was 0.88 (23 of 26 findings; 95% CI: 0.72, 0.96), with a false-positive rate of 1.3. Testing set sensitivity for the detection and localization of fractures within each vertebra was 0.81 (87 of 107 findings; 95% CI: 0.75, 0.87), with a false-positive rate of 2.7. The sensitivity for fracture localization to the correct vertebra was 0.92 (55 of 60 findings; 95% CI: 0.79, 0.94), with a false-positive rate of 1.6. The most common cause of false-positive findings was nutrient foramina (106 of 272 findings [39%]).ConclusionThe fully automated computer system detects and anatomically localizes vertebral body fractures in the thoracic and lumbar spine on CT images with a high sensitivity and a low false-positive rate.

Background Pre-liver transplant (LT) sarcopenia is associated with poor survival. Methods exist for measuring body composition with use of CT scans; however, it is unclear which components best predict post-LT outcomes. Purpose To quantify the association between abdominal CT-based body composition measurements and post-LT mortality in a large North American cohort. Materials and Methods This was a retrospective cohort of adult first-time deceased-donor LT recipients from 2009 to 2018 who underwent pre-LT abdominal CT scans, including at the L3 vertebral level, at Johns Hopkins Hospital. Measurements included sarcopenia (skeletal muscle index [SMI] <50 in men and <39 in women), sarcopenic obesity, myosteatosis (skeletal muscle CT attenuation <41 mean HU for body mass index [BMI] <25 and <33 mean HU for BMI ≥25), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and VAT/SAT ratio. Covariates in the adjusted models were selected with use of least absolute shrinkage and selection operator regression with lambda chosen by means of 10-fold cross-validation. Cox proportional hazards models were used to quantify associations with post-LT mortality. Model discrimination was quantified using the Harrell C-statistic. Results A total of 454 recipients (median age, 57 years [IQR, 50-62 years]; 294 men) were evaluated. In the adjusted model, pre-LT sarcopenia was associated with a higher hazard ratio (HR) of post-LT mortality (HR, 1.6 [95% CI: 1.1, 2.4]; C-statistic, 0.64; P = .02). SMI was significantly negatively associated with survival after adjustment for covariates. There was no evidence that myosteatosis was associated with mortality (HR, 1.3 [95% CI: 0.86, 2.1]; C-statistic, 0.64; P = .21). There was no evidence that BMI (HR, 1.2 [95% CI: 0.95, 1.4]), VAT (HR, 1.0 [95% CI: 0.98, 1.1]), SAT (HR, 1.0 [95% CI: 0.97, 1.0]), and VAT/SAT ratio (HR, 1.1 [95% CI: 0.90, 1.4]) were associated with mortality ( P = .15-.77). Conclusions Sarcopenia, as assessed on routine pre-liver transplant (LT) abdominal CT scans, was the only factor significantly associated with post-LT mortality. © RSNA, 2022 See also the editorial by Ruehm in this issue.

Purpose: To retrospectively identify volume and average attenuation thresholds for differentiating between ileocecal valve (ICV) and polyp at computed tomographic (CT) colonography with computer-aided detection (CAD)., Materials and Methods: Informed consent (with consent for future retrospective research) and institutional review board (IRB) approval were obtained for the original prospective study. This retrospective study had IRB approval, as well, and was HIPAA-compliant. A total of 496 patients were selected from a larger screening population. CT colonographic images from 394 patients (227 men, 167 women; mean age, 58.0 years; range, 40-79 years) were used as a training set, and images from 102 patients (76 men, 26 women; mean age, 59.8 years; range, 46-79 years) were used as a test set. A series of 2742 volume and attenuation thresholds, for which segmented findings both larger in volume and lower in average attenuation were labeled as ICVs and remaining findings were labeled polyps, were applied to the training set to determine settings with 100% sensitivity for polyp detection and the highest specificity for ICV detection. The optimal settings were then applied to the test set. Significance was assessed with the Fisher exact test, and 95% confidence intervals (CIs) were computed for sensitivity and specificity., Results: A total of 386 ICVs and 67 adenomatous polyps from the training set and 102 ICVs and 138 adenomatous polyps from the test set could be segmented with a three-dimensional segmentation algorithm. When supine and prone images were counted individually, 746 nonunique ICVs from the training set and 191 from the test set were segmentable. In the training set, a volume of 600 mm(3) and an attenuation of 36 HU provided 100% sensitivity (67 polyps; 95% CI: 93%, 100%) and the optimal 83% specificity (618 of 746 ICVs; 95% CI: 80%, 85%). When applied to the test set, this combination provided 97% sensitivity (134 of 138 polyps; 95% CI: 92%, 99%) and 84% specificity (160 of 191 ICVs; 95% CI: 78%, 89%). Differences in sensitivity and specificity in the detection of polyps between the sets were not significant., Conclusion: Volume and average CT attenuation thresholds can help differentiate most ICVs from true polyps.