Dual Energy Computed Tomography Collagen Material Decomposition for Detection of Lumbar Spine Disc Extrusion and Sequestration: A Comparative Study With Greyscale Computed Tomography
Bibliographic record
Abstract
Purpose: To assess value of dual energy computed tomography (DECT) collagen material decomposition algorithm when combined with standard computed tomography (CT) in detection of lumbar disc extrusion and sequestration. Materials and Methods: Retrospective analysis of all patients with acute low back pain who had a diagnosis of lumbar spine disc extrusion and/or sequestration on Magnetic Resonance Imaging (MRI) (reference standard), and had undergone non-contrast DECT of the lumbar spine within 60 days of the MRI. Age and sex-matched control patients (n = 42) were included. Patients were grouped into standard, grey-scale CT only group and standard CT + DECT tendon images group. Two double-blinded radiologists reviewed both groups for presence of extrusion or sequestration. They also rated their diagnostic confidence on Likert 5-point scale. McNemar Chi-square test was used to compare diagnostic accuracy, unpaired t-test to compare reviewers diagnostic confidence, and Cohen’s k (kappa) test for interobserver agreement. Results: The combined group showed higher overall sensitivity (96.6% vs 87.2%), specificity (99% vs 95.4%), and diagnostic accuracy (98.7% vs 94.5%) with a lower false positive rate (1.1% vs 4.6%). McNemar Chi-square test confirmed statistical significance ( P = .03 and P = .02 for Reviewers R1 and R2, respectively). The mean diagnostic confidence was also significantly higher on combined group (R1: 3.74 ± 1.1 vs 3.47 ± 1.15 ( P < .01) and R2: 3.91 ± 1.15 vs 3.72 ± 1.16 [mean ± SD] ( P = .02)). Conclusion: Utilizing MRI as a reference standard, DECT tendon application combined with standard CT increases the sensitivity, specificity, and accuracy of detection of lumbar spine disc extrusion and sequestration, when compared to standard CT alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".