Investigating the role of functional imaging in the management of soft-tissue sarcomas of the extremities
Bibliographic record
Abstract
Background and purpose In this work, we validate a texture-based model computed from positron emission tomography (PET) and magnetic resonance imaging (MRI) for the prediction of lung metastases in soft-tissue sarcomas (STS). We explore functional imaging at different treatment time points and evaluate the feasibility of radiotherapy dose painting as a potential treatment strategy for patients with higher metastatic risk. Materials and methods We acquired fluorodeoxyglucose (FDG)-PET, fluoromisonidazole (FMISO)-PET, diffusion weighting (DW)-MRI and dynamic contrast enhanced (DCE)-MRI data for 18 patients with extremity STS before, during, and after pre-operative radiotherapy. We tested the lung metastases prediction model using pre-treatment images. We evaluated the feasibility of dose painting using volumetric arc therapy (VMAT) via treatment re-planning with a prescription of 50 Gy to the planning target volume (PTV 50Gy ) and boost doses of 60 Gy to the FDG hypermetabolic gross tumour volume (GTV 60Gy ) and 65 Gy to the low-perfusion DCE-MRI hypoxic GTV contained within the GTV 60Gy (GTV 65Gy ). Results The texture-based model for lung metastases prediction reached an area under the curve (AUC), sensitivity, specificity and accuracy of 0.71, 0.75, 0.85 and 0.82, respectively. Dose painting resulted in adequate coverage and homogeneity in the re-planned treatments: D 95% to the PTV 50Gy , GTV 60Gy and GTV 65Gy were 50.0 Gy, 60.3 Gy and 65.4 Gy, respectively. Conclusions Textural biomarkers extracted from FDG-PET and MRI could be useful to identify STS patients that might benefit from dose escalation. The feasibility of treatment planning with double boost levels to intratumoural GTV functional sub-volumes was established.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".