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Reliability and accuracy of scout CT in the detection of vertebral fractures.

ABSTRACT: OBJECTIVE: The aim of the present study was to evaluate the reliability of scout CT (sCT) lateral radiograph, in terms of diagnostic accuracy and intra- and interobserver agreement in the detection of vertebral fractures (VFs). METHODS: 300 CT examinations of the thoracic and/or lumbar spine were collected and independently analysed by 3 musculoskeletal radiologists in 2 different sessions. A semi-quantitative approach was used for VF assessment on sCT, and morphometric analysis was performed when a VF was suspected. Results of multiplanar sagittal CT reconstructions interpreted by the most expert radiologist were considered as gold standard. Arthrosis was also scored. Only vertebral bodies assessable by both sCT and gold standard were considered for the analysis. Area under the receiver operating characteristic curve (AUROC), Cohen's kappa statistic and linear-by-linear association were used for statistical analysis. RESULTS: 1522 vertebrae were considered (130 males and 170 females; ages, 73.0±2.8 years). 73 of 1522 (4.8%) VFs were identified in 34/300 patients (11.3%). In the detection of VFs, the sensitivity and specificity of sCT were 98.7% and 99.7%, respectively. Accuracy (AUROC=0.992±0.008), as well as interobserver agreement (k=0.968±0.008), was excellent. Intra-observer agreement was perfect (k=1.000). Performance of this method was independent of arthrosis, vertebral level and type and grade of VFs. CONCLUSION: sCT is a simple but very accurate method for the detection of VFs. It should be introduced as a spine evaluation tool for the detection of VFs in examinations that are performed for other diagnostic purposes. ADVANCES IN KNOWLEDGE: sCT lateral radiograph is an accurate tool for the detection of VFs. This technique may be used with several advantages in clinical practice.

SUBMITTER: Bazzocchi A

PROVIDER: S-EPMC3854570 | biostudies-other | 2013 Dec

REPOSITORIES: biostudies-other

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Project description: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.

Project description:PurposeTo systematically evaluate the effects of virtual tube current reduction and sparse sampling on image quality and vertebral fracture diagnostics in multi-detector computed tomography (MDCT).Materials and methodsIn routine MDCT scans of 35 patients (80.0% females, 70.6?±?14.2 years, 65.7% showing vertebral fractures), reduced radiation doses were retrospectively simulated by virtually lowering tube currents and applying sparse sampling, considering 50%, 25%, and 10% of the original tube current and projections, respectively. Two readers evaluated items of image quality and presence of vertebral fractures. Readout between the evaluations in the original images and those with virtually lowered tube currents or sparse sampling were compared.ResultsA significant difference was revealed between the evaluations of image quality between MDCT with virtually lowered tube current and sparse-sampled MDCT (p?<?0.001). Sparse-sampled data with only 25% of original projections still showed good to very good overall image quality and contrast of vertebrae as well as minimal artifacts. There were no missed fractures in sparse-sampled MDCT with 50% reduction of projections, and clinically acceptable determination of fracture age was possible in MDCT with 75% reduction of projections, in contrast to MDCT with 50% or 75% virtual tube current reduction, respectively.ConclusionSparse-sampled MDCT provides adequate image quality and diagnostic accuracy for vertebral fracture detection with 50% of original projections in contrast to corresponding MDCT with lowered tube current. Thus, sparse sampling is a promising technique for dose reductions in MDCT that could be introduced in future generations of scanners.Key points• MDCT with a reduction of projection numbers of 50% still showed high diagnostic accuracy without any missed vertebral fractures. • Clinically acceptable determination of vertebral fracture age was possible in MDCT with a reduction of projection numbers of 75%. • With sparse sampling, higher reductions in radiation exposure can be achieved without compromised image or diagnostic quality in routine MDCT of the spine as compared to MDCT with reduced tube currents.

Project description: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.

Project description:ObjectiveTo evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images.Materials and methodsThis study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists.ResultsA total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 × 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds.ConclusionOur CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

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Reliability and accuracy of scout CT in the detection of vertebral fractures.

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