Diffusion-Weighted Magnetic Resonance Imaging of Spinal Infection and Malignancy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Pyogenic and tuberculous spondylitis can mimic malignancy. The purpose of this study was to deter mine the efficacy of diffusion-weighted magnetic resonance imaging in differentiating spinal infection and malignancy. METHODS: Fifty-one consecutive patients with suspected spinal infection or malignancy were enrolled in the study. Apparent diffusion coefficients (ADCs) of paraspinal soft tissue mass and normal and abnormal vertebral bone marrow were determined on the diffusion-weighted magnetic resonance images of the spine. The mean ADCs of normal and abnormal vertebral bodies in patients with confirmed infection or malignancy were compared using nonparametric tests. RESULTS: ADCs of 69 tuberculous, 9 pyogenic, and 50 malignant vertebral marrow lesions were significantly higher than ADCs of normal marrow. ADCs of malignant bone marrow and 5 paraspinal soft tissue lesions were significantly lower than tuberculosis and pyogenic infection. There was no significant difference between the ADCs of 44 adult and 25 pediatric tuberculous bone lesions or between tuberculosis and pyogenic infection. Using the cutoff ADC of 1.02x10(-3)mm2/s for bone marrow, the sensitivity, specificity, and accuracy were 60.26%, 66.00%, and 62.50%, respectively, for distinguishing infection from malignancy. The sensitivity, specificity, and accuracy increased to 94.12%, 82.35%, and 90.20%, respectively, when the ADCs of associated soft tissue lesions were higher than 1.17x10(-3)mm2/s. CONCLUSIONS: Diffusion-weighted magnetic resonance imaging has limited usefulness for differentiating spinal infection and malignancy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".