Combined intravoxel incoherent motion diffusion-weighted MR imaging and magnetic resonance spectroscopy in differentiation between osteoporotic and metastatic vertebral compression fractures
Bibliographic record
Abstract
PURPOSE: Our purpose was to combine intravoxel incoherent motion diffusion-weighted MR imaging (IVIM-DWI) and magnetic resonance spectroscopy (MRS) to differentiate osteoporotic fractures from osteolytic metastatic vertebral compression fractures (VCFs). METHODS: A total of 70 patients with VCFs were included and divided into two groups, according to their causes of fractures based on pathological findings or clinical follow-up. All patients underwent conventional sagittal T1WI, T2WI, STIR, IVIM-DWI, and single-voxel MRS. The diffusion coefficient (D), pseudo diffusion (D*), and perfusion fraction (f) parameters from IVIM-DWI and the lipid water ratio (LWR) and fat fraction (FF) parameters from MRS were obtained and compared among groups. Furthermore, the diagnostic performance of MRS, IVIM-DWI, and IVIM-DWI combined with MRS for differentiation between osteoporotic and osteolytic metastatic VCFs was assessed by using receiver operating characteristic (ROC) curve analysis. RESULTS: Compared with the osteoporotic group, the metastatic group had significantly lower values for f, D, and FF, but higher D* (all P < 0.05). The area under the receiver operating characteristic (ROC) curve of MRS, IVIM-DWI, and IVIM-DWI combined with MRS were 0.73, 0.88, and 0.94, respectively. Among these, the IVIM-DWI combined with MRS showed the highest sensitivity, specificity, and accuracy, which are 90.63% (29/32), 97.37 % (37/38), and 94.29% (66/70), respectively. CONCLUSIONS: IVIM-DWI combined with MRS can be more accurate and efficient for differentiation between osteoporotic and osteolytic metastatic VCFs than single MRS or IVIM-DWI.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".