Osseous Invasion by Soft-Tissue Sarcoma: Assessment with MR Imaging
Bibliographic record
Abstract
PURPOSE: To assess magnetic resonance (MR) imaging signs and overall accuracy of MR imaging for detection of osseous invasion by soft-tissue sarcoma, with histopathologic correlation as the reference standard. MATERIALS AND METHODS: Preoperative MR images (1.5 T, transverse and longitudinal planes, T1 and T2 weighted) of 56 osseous sites in 51 patients who underwent bone resection at surgery for soft-tissue sarcoma were assessed retrospectively for signs of osseous invasion, by consensus of two readers who were blinded to clinical and histopathologic findings. Delay between MR imaging and surgery averaged 34 days (range, 2-112 days). MR signs assessed included osseous abutment by tumor, maximal diameter of osseous abutment, extent of circumferential abutment of long bones (<25%, 25%-50%, >50%), cortical destruction, and cortical and medullary signal intensity change on T1- and T2-weighted images. Imaging findings were correlated with histopathologic findings. Sensitivities, specificities, positive and negative predictive values (PPV, NPV), and P values were calculated. RESULTS: Eleven sites (20%) showed osseous invasion histologically (two, cortical; nine, both cortical and medullary). Tumor abutted bone at 44 lesion sites (sensitivity, 100%; specificity, 27%). Maximal diameter of osseous abutment and extent of circumferential abutment did not significantly affect osseous invasion (P =.09 and.11, respectively). On T1- and T2-weighted images, 13 lesion sites showed cortical signal intensity change (sensitivity, 100%; specificity, 96%) and 10 showed cortical destruction (sensitivity, 82%; specificity, 98%). Eleven sites showed decreased medullary T1 signal intensity (sensitivity, 100%; specificity, 96%), and 12 showed increased medullary T2 signal intensity (sensitivity, 100%; specificity, 94%). MR imaging overall had a sensitivity of 100%, specificity of 93%, PPV of 79%, and NPV of 100% for detection of osseous invasion on the basis of any finding of cortical destruction or cortical or medullary signal intensity change on T1- or T2-weighted images (P <.001). CONCLUSION: On T1- and T2-weighted MR images, findings of cortical and medullary signal intensity change and cortical destruction were sensitive and specific for detection of osseous invasion by soft-tissue sarcoma.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".