Development of a risk assessment model to differentiate malignant and benign musculoskeletal soft‐tissue masses on magnetic resonance imaging
Bibliographic record
Abstract
INTRODUCTION: This study aimed to develop a risk stratification model to differentiate benign and malignant MRI-imaged musculoskeletal soft-tissue tumours, informing decisions surrounding biopsy and follow-up imaging. METHODS: Imaging of patients who underwent MRI and subsequent biopsy to evaluate a soft-tissue mass was retrospectively reviewed. Features analysed included patient age; tumour size; shape; margins; enhancement pattern; signal intensity pattern; deep fascia, neurovascular bundle, bone and joint involvement; and the presence of necrosis, haemorrhage, oedema and intralesional fat. Univariate comparisons, by final histopathological status, employed t-tests and chi-square tests, followed by simple and multiple logistic regressions. Variables included in the final multiple regression model were used to define a three-level risk stratification strategy. RESULTS: One-hundred and ten patients were included in the analysis. Univariate relationships were identified between malignancy and age, tumour size, deep fascia involvement, neurovascular involvement, necrosis, haemorrhage, oedema and heterogeneous enhancement (all P < 0.01). Final multiple regression modelling included size, enhancement and oedema. Thirty of 40 (75%) tumours >5 cm with surrounding oedema ('high risk') were malignant, 13 of 47 (28%) tumours with one or more of tumour size >5 cm, surrounding oedema or heterogeneous enhancement ('moderate risk') were malignant, and none of the 16 tumours ≤5 cm with the absence of surrounding oedema and heterogeneous enhancement ('low risk') were malignant. CONCLUSIONS: A model including tumour size, enhancement and oedema has potential to stratify soft-tissue tumours into high-, intermediate- and low-risk categories; this may inform decisions surrounding biopsy and follow-up imaging.
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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) | 0.000 | 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".