Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images - PubMed (original) (raw)

Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images

Joseph E Burns et al. Radiology. 2017 Sep.

Abstract

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.

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Figures

Figure 1:

Figure 1:

Axial CT image of a vertebra shows zone division of the vertebral body. The axial section of the vertebral bodies was divided into zones for determination of subsection of height loss. This zone division was designed to conform to the original Genant classification schema, with allowance for lateralization and development of an enhanced Genant classification schema. The two lateral zones created by the sagittal plane partitions are defined as R (right) and L (left). Three zones were created by partitioning the central sagittal region into three central coronal zones A (anterior), C (central), and P (posterior).

Figure 2:

Figure 2:

Height compass and compression fracture detection and characterization. The geometric arrangement of the height compass is a compasslike structure, with a central circular sector surrounded by two ring-shaped finite thickness concentric bands. Each band is divided by eight radii of common central angles into eight sectors. All images were obtained in an 86-year-old woman with compression fractures. A, Sagittal CT section shows vertebral column segmentation and partitioning. B, Stacked height compass of the entire vertebral column. C, Height compass of a grade 2 concave fracture at T3. D, Height compass of a grade 3 wedge fracture at T7. E, Height compass of a normal vertebral body at L2.

Figure 3:

Figure 3:

FROC curve of support vector machine performance in the detection of vertebral bodies with compression fractures shows 95.7% sensitivity (95% CI: 87%, 98.9%) with a false-positive rate of 0.29 per patient. Error bars represent 95% CIs.

Figure 4:

Figure 4:

Sagittal CT section shows false-negative findings in compression fracture detection with the computer system in a 77-year-old male patient. Missed concave fractures (arrows) are visible at T8 and T11.

Figure 5a:

Figure 5a:

Sagittal CT sections show examples of compression fracture grading with the computer system. These are true-positive findings. (a) Image shows the T6 vertebra in an 86-year-old female patient. The radiologist grade was a grade 3 wedge fracture; the computer grade also was a grade 3 wedge fracture. (b) Image shows the T10 vertebra in a 79-year-old male patient. The radiologist grade was a grade 1 concave fracture; the computer grade was a grade 2 concave fracture.

Figure 5b:

Figure 5b:

Sagittal CT sections show examples of compression fracture grading with the computer system. These are true-positive findings. (a) Image shows the T6 vertebra in an 86-year-old female patient. The radiologist grade was a grade 3 wedge fracture; the computer grade also was a grade 3 wedge fracture. (b) Image shows the T10 vertebra in a 79-year-old male patient. The radiologist grade was a grade 1 concave fracture; the computer grade was a grade 2 concave fracture.

Figure 6:

Figure 6:

The distribution of vertebrae in the thoracic and lumbar spine with compression fracture deformities. This graph is annotated in standard anatomic fashion, with T1 indicating the first thoracic vertebra, L1 indicating the first lumbar vertebra, and so on. At the top of each bar is the number of compression fractures at that anatomic level in the case set. The expected bimodal distribution of the frequency of fractures is seen, with a peak in the midthoracic spine (at T7 here) and a peak in the upper lumbar spine (at L1).

Figure 7:

Figure 7:

FROC curves arranged by grade. FROC curves of system performance for Genant fracture severity grade 1 (<25%), grade 2 (26%–40%), and grade 3 (>40%). The performance difference between grade 1 and grade 3 classification is significant (P = .05), while performance differences between grades 2 and 3 (P = .12) and between grades 1 and 2 (P = .64)

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