The Role of [18F]FDG PET/CT Prior to and During Neoadjuvant Chemotherapy for Soft Tissue Sarcomas
Bibliographic record
Abstract
This retrospective, single-center study investigates the association between PET parameters and pathological response or disease recurrence in patients with soft tissue sarcoma (STS) treated with neoadjuvant chemotherapy (NACT). The maximum standardized uptake value (SUVmaxBL), metabolic tumor volume (MTVBL), and total lesion glycolysis (TLGBL) were measured at baseline [18F]FDG PET/CT and the change in percentage (ΔSUVmax, ΔMTV, ΔTLG) from baseline to early evaluation [18F]FDG PET/CT was calculated. The optimal cutoff values of the different PET parameters for pathological response, defined as <10% residual viable tumor (RVT) or >15% fibrosis/hyalinization, and recurrence-free survival were obtained for analysis. Forty-two patients who underwent baseline [18F]FDG PET/CT and NACT followed by surgery were included between January 2015 and January 2023. The primary diagnoses were angiosarcoma (n = 15), leiomyosarcoma (n = 15), sarcoma not otherwise specified (n = 9) and synovial sarcoma (n = 3). Twenty-eight (66.6%) patients underwent an early evaluation PET/CT. MTVBL, TLGBL, and ΔSUVmax (p = 0.024; p = 0.042, p = 0.009, respectively) values above the cutoff were associated with a pathological response based on RVT. ΔSUVmax, ΔMTV, and ΔTLG (p = 0.002; p = 0.019; p = 0.039, respectively) values above the cutoff were positively related to >15% fibrosis/hyalinization. MTVBL, TLGBL, and ΔMTV (p = 0.014; p = 0.022; p = 0.034, respectively) values above the cutoff were prognostic for the recurrence of disease. [18F]FDG PET/CT has a promising role in STS patients treated with NACT.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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 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".